JP6540238B2 - Data output regulation device for three-dimensional object modeling - Google Patents

Description

  In the present invention, based on data representing a three-dimensional object, when using a three-dimensional object modeling apparatus such as a 3D printer that processes a resin or the like to model a three-dimensional object, it determines what is inappropriate for output from the three-dimensional object modeling apparatus Related to technology.

  BACKGROUND In recent years, 3D printers that process and model resin, gypsum, and the like based on data representing three-dimensional objects have become widespread as three-dimensional object formation devices. The 3D printer outputs a solid object using a polygon model in which the three-dimensional shape of the solid object is represented by a set of polygons. As medical application of 3D printer, modeling data (polygon model) for 3D printer output is created based on CT / MRI image (DICOM format 3D voxel data) taken by medical diagnosis, surgery simulation, medical education, in Attempts have been made to output an organ model for a form-out and create an artificial organ (blood vessel, bone, joint, etc.) for implantation in the body.

  The 3D printer has the innovativeness of indirectly propagating things to remote locations as well as information via the Internet. Therefore, there is a problem that dangerous goods such as guns and swords, which have been regulated by customs, are distributed in the form of data across borders, and can be easily obtained as things by 3D printers.

  Specifically, STL (Standard Triangulated Language), which can produce handguns of a predetermined model using a 3D printer, is available for free at a web site. Although the range is somewhat inferior to that of metal, even bullets created with a 3D printer have a reasonable killing ability. Furthermore, the materials that can be formed by the current 3D printer are limited to resin and gypsum, and the drawback that metal can not be used is pointed out, and it has been pointed out that the problem is that metal detectors in airports are missed. In the future, as consumer 3D printers become widespread, it is required to have security functions such as shape authentication of output data input to the 3D printer side and output regulation of dangerous materials.

  On the other hand, a method of constructing a system which performs output permission determination by a method similar to an anti-virus tool is considered, and in realizing it, a technique of collating polygons is used. However, it is necessary to include not only reproductions of 3D polygon models but also 3D conversions of illustrations and photographs that are expressed in 2 dimensions as targets for which the output in a 3D printer should be restricted. is there. In order to cope with this, techniques for collating with a three-dimensional polygon model based on a two-dimensional image have also been developed (see Patent Documents 1 to 3).

Patent No. 4709668 Patent No. 5278881 gazette Patent No. 5560925 gazette

  However, in the above-described conventional techniques, a three-dimensional polygon model is projected in two dimensions to generate an image, and a feature vector obtained from this image is used for matching, so the feature vector is unique according to the projection condition. Not determined. Therefore, there is a problem that it is difficult to determine the output appropriateness of the three-dimensional polygon model based on the two-dimensional image.

  Therefore, in the present invention, when outputting a three-dimensional object based on a polygon model, it is possible to accurately determine whether or not the output should be restricted based on a two-dimensional image. An object of the present invention is to provide an output regulating device.

In order to solve the above problems, in the first aspect of the present invention,
It is an apparatus that determines whether or not to restrict when outputting a polygon model expressed as a set of polygons to a three-dimensional object formation apparatus as data for three-dimensional object formation,
A database in which a distance distribution and an angle distribution representing the features of a regulated image, which is an image whose output should be regulated, is registered,
A model that calculates a distance distribution that is a frequency distribution of distances between polygons in the target model and an angle distribution that is a frequency distribution of angles unique to the polygons for the target model that is a polygon model to be output Frequency distribution calculation means;
The distance distribution and the angle distribution of the target model calculated by the model frequency distribution calculating means are respectively compared with the distance distribution and the angle distribution of the regulation image registered in the database, and it is determined whether or not the output should be regulated. And frequency distribution matching means,
With regard to the distance distribution and the angle distribution of the restriction image registered in the database, the pixels constituting the edge of the object are extracted as edge points from the restriction image, and the distance between each edge point in the restriction image is It is obtained by an image frequency distribution calculating apparatus having image frequency distribution calculating means for calculating distance distribution which is frequency distribution and angle distribution which is frequency distribution of angles unique to each edge point,
The image frequency distribution calculating means corrects the direction to specify the edge of the edge point with reference to the pixels around the edge point, and then specifies the edge after correction and each edge in the restricted image There is provided a three-dimensional object formation data output restricting device characterized in that an angle formed by directions connecting points is calculated as an inherent angle between the edge points.

  Here, an object (object) is an object area modeled on a polygon model in a restriction image, and the other area is a background. Therefore, the object is a portion corresponding to a subject if the image is a picture, and a portion corresponding to an illustration, a character or the like if it is an artificially created image. Whether it is an object or a background in an image is subjective, but in an image registered as a restriction image, a portion to be reproduced is often clearly identified as an object, so Background classification is relatively clear. The “direction for specifying an edge” corresponds to a direction in which the boundary between the object and the background is provided, the angle being orthogonal to or nearly perpendicular to the boundary.

  According to the first aspect of the present invention, there is provided a database in which a distance distribution and an angle distribution representing the features of a restriction image are registered, and a frequency distribution of the distance between polygons in the target model with respect to the target model. The distance distribution and the angle distribution which is the frequency distribution of the angles unique to each polygon are calculated, and the calculated distance distribution of the target model and the angle distribution are respectively the distance distribution of the regulation image registered in the database, the angle distribution The distance distribution and the angle distribution of the restriction image registered in the database are compared with the restriction image using pixels constituting the edge of the object as an edge point. A device obtained by a device for extracting and calculating a distance distribution which is a frequency distribution of distances between edge points in the restriction image and an angle distribution which is a frequency distribution of angles unique to each edge point Yes, after correcting the direction to specify the edge of the edge point with reference to the pixels around the edge point, the angle between the direction to specify the edge after correction and the direction connecting the edge points in the restricted image Is calculated as an inherent angle between edge points. Therefore, when outputting a three-dimensional object based on a polygon model, whether or not the output should be restricted by performing collation with a two-dimensional restriction image having different dimensions It becomes possible to determine the In particular, even when the direction of specifying the edge of the important edge point for determining the unique angle between edge points is basically limited to a predetermined number, the angle distribution is obtained at the angle corrected in the direction of the intermediate position. This makes it possible to create a continuous distribution, which makes it easy to match the angular distribution of the three-dimensional polygon model.

In the second aspect of the present invention,
It is an apparatus that determines whether or not to restrict when outputting a polygon model expressed as a set of polygons to a three-dimensional object formation apparatus as data for three-dimensional object formation,
For a regulated image which is an image whose output is to be regulated, pixels constituting the edge of the object are extracted as edge points, and a distance distribution which is a frequency distribution of distances between the respective edge points in the regulated image, and Image frequency distribution calculating means for calculating an angle distribution which is a frequency distribution of angles unique to edge points;
A model that calculates a distance distribution that is a frequency distribution of distances between polygons in the target model and an angle distribution that is a frequency distribution of angles unique to the polygons for the target model that is a polygon model to be output Frequency distribution calculation means;
Whether to restrict the output by comparing the distance distribution and the angle distribution of the restriction image calculated by the image frequency distribution calculating means with the distance distribution and the angle distribution of the target model calculated by the model frequency distribution calculating means Frequency distribution matching means for determining
Equipped with
The image frequency distribution calculating means corrects the direction to specify the edge of the edge point with reference to the pixels around the edge point, and then specifies the edge after correction and each edge in the restricted image There is provided a three-dimensional object formation data output restricting device characterized in that an angle formed by directions connecting points is calculated as an inherent angle between the edge points.

  According to the second aspect of the present invention, in the restriction image, pixels constituting the edge of the object are extracted as edge points, and a distance distribution which is a frequency distribution of distances between the edge points and between the edge points An angle distribution which is a frequency distribution of specific angles is calculated, and a distance distribution which is a frequency distribution of distances between polygons of the target model and an angle which is a frequency distribution of angles unique to each polygon with respect to the target model The distribution is calculated, the distance distribution of the target model, the angle distribution, and the distance distribution of the control image, the angle distribution are collated, it is determined whether the output should be restricted or not, and the direction for specifying the edge of the edge point is the edge After correcting with reference to the pixels around the point, the angle between the direction for specifying the corrected edge and the direction connecting the edge points in the restricted image is calculated as an inherent angle between the edge points. Because we made polygon model To when performing output of the three-dimensional object, by performing matching with the two-dimensional regulations images of different dimensions, it is possible to perform accurately determines whether to regulate the output. In particular, even when the direction of specifying the edge of the important edge point for determining the unique angle between edge points is basically limited to a predetermined number, the angle distribution is obtained at the angle corrected in the direction of the intermediate position. This makes it possible to create a continuous distribution, which makes it easy to match the angular distribution of the three-dimensional polygon model.

  In the third aspect of the present invention, the image frequency distribution calculating means applies a plurality of filter matrices defined in advance to the value of each pixel of the restricted image, and the filter matrix for each pixel is applied. Let the maximum value of multiple intensities calculated in step be the edge intensity, calculate the direction unique to the filter matrix that gives the maximum value as the direction to specify the edge, and extract the pixels whose edge intensity exceeds a predetermined value as the edge point A vector from an edge point of the restricted image to another edge point is determined as an inter-edge point vector indicating a direction connecting each edge point in the restricted image, and one edge corresponding to the edge point vector When calculating an angle between the edge point and the direction for identifying the edge of the point as the unique angle between the edge points, the correction of the direction for identifying the edge of the edge point is Characterized in that it is carried out by averaging with weighting the edge strength in the direction and the peripheral edge point to identify the edges in the edge point around the edge points to.

  According to the third aspect of the present invention, the plurality of filter matrices defined in advance are applied to the value of each pixel of the regulated image, and the maximum value of the plurality of intensities calculated by each filter matrix for each pixel A direction unique to the filter matrix giving the maximum value is calculated as the direction for specifying the edge, a pixel whose edge strength exceeds a predetermined value is extracted as an edge point, and the other edge points from the restricted image The vector to the edge point of is determined as an inter-edge point vector indicating the direction connecting each edge point in the restricted image, and the angle between the edge point vector and the direction specifying the edge of the corresponding one edge point is When calculating as an inherent angle between edge points, the correction in the direction to specify the edge of the edge point specifies the edge at the edge point around the target edge point. It is performed by averaging while weighting the edge strength of the surrounding direction and the surrounding edge point, so restricting the angular distribution that is consistent with the angular distribution of the target model obtained by the model frequency distribution calculating means The similarity can also be calculated from an image, and it becomes possible to determine the similarity between mutually different data of two-dimensional regulation image and three-dimensional object model by comparing the respective angular distributions.

  In the fourth aspect of the present invention, a filter matrix of 3 × 3 pixels in 16 directions defined in advance is applied as the filter matrix (edge extraction filter), and values previously set in each direction are used. It is characterized in that calculation is performed to obtain the intensity in each direction.

  According to the fourth aspect of the present invention, the filter matrix of 16 directions 3 × 3 pixels defined in advance is applied as the filter matrix, and the intensity in each direction is set using values preset in each direction. Since the calculation is performed to obtain the above, it becomes possible to accurately calculate the direction of the edge as well as extracting the edge point which is a pixel constituting the edge of the object in the restriction image.

  Further, in the fifth aspect of the present invention, the image frequency distribution calculating means corrects the intensity in each of the determined directions using the intensity correction amount set in advance for each of the 16 directions. It features.

  According to the fifth aspect of the present invention, the intensities in the respective directions determined are corrected using the intensity correction amount set in advance for each of the 16 directions corresponding to the filter matrix. It is possible to correct the bias of the edge strength in the direction according to the characteristics of the above.

  Further, in the sixth aspect of the present invention, the image frequency distribution calculating means subtracts the predetermined setting minimum value (SL) from the edge strength calculated for each pixel and sets the value of the edge strength to a value obtained by subtraction. The number of gradations is multiplied, and the edge strength is corrected by dividing the difference between the number of gradations and the set minimum value, and a pixel having a positive value of the corrected edge strength is extracted as the edge point It is characterized by

  According to the sixth aspect of the present invention, the predetermined setting minimum value is subtracted from the edge strength calculated for each pixel, and the value obtained by subtraction is multiplied by the number of gradations of the edge strength, and the number of gradations is calculated. Since the edge strength is corrected by dividing the difference between the value and the set minimum value, and the pixel whose corrected edge strength is a positive value is extracted as the edge point, the corrected edge strength is It is only necessary to determine whether it is a positive value, and efficient extraction of edge points becomes possible.

  In the seventh aspect of the present invention, the image frequency distribution calculating means prepares a one-dimensional array composed of a predetermined number (MD) of elements in calculating the distance distribution, and calculates the calculated distance. The distance distribution is calculated by equally dividing the range of the maximum value into the predetermined number, assigning each of the distances to any one of the elements based on the distances, and counting the number corresponding to the elements. It is characterized in that.

  According to the seventh aspect of the present invention, in calculating the distance distribution of the regulated image, a one-dimensional array composed of a predetermined number of elements is prepared, and the calculated maximum range of distances is equally divided into the predetermined number. Then, each distance is assigned to one of the elements based on the distance, and the number corresponding to the element is counted, so that the distance distribution can be easily calculated using the distance between each edge point. It becomes possible.

  Further, in the eighth aspect of the present invention, the image frequency distribution calculating means prepares a one-dimensional array composed of a predetermined number (MA) of elements when calculating the angle distribution, and makes an angle from 0 degree to 180 degrees. Is equally divided into the predetermined number, and is assigned to any one of the elements based on the unique angle between the edge points, and the value of the element is counted to calculate the angular distribution. It is characterized by

  According to the eighth aspect of the present invention, in calculating the angular distribution of the restriction image, a one-dimensional array composed of a predetermined number of elements is prepared, and the range from 0 degrees to 180 degrees is equally divided into a predetermined number Above, by assigning to any of the elements based on the unique angle between each edge point and counting the value of the element, the angle distribution is calculated, so it is easy to use the unique angle between each edge point It is possible to calculate the angular distribution on

  Further, in the ninth aspect of the present invention, the image frequency distribution calculating means weights the edge intensity of the edge point corresponding to the edge point interval vector when counting the value of the allocated element. It is characterized by

  According to the ninth aspect of the present invention, in calculating the distance distribution or angle distribution of the restriction image, when counting the value of the assigned element, weighting is performed using the edge strength of the edge point corresponding to the vector between edge points. Therefore, the difference in definition of the regulation image does not reflect much in the distance distribution or the angle distribution, and whether or not the output should be regulated appropriately even without registering a plurality of regulation images having different numbers of pixels in the database Can be determined.

Further, in the tenth aspect of the present invention, the image frequency distribution calculating means sets, as the edge strength for the weighting, the magnitude of the vector between edge points to De (i, j) and the maximum vector between edge points. Assuming that the value is Demax, a value obtained by correcting the original edge strength by using a value De (i, j) × {Demax ² −De (i, j) ² } ^1/2 is used. Do.

According to the tenth aspect of the present invention, as the edge strength for weighting, when the magnitude of the vector between edge points is De (i, j) and the maximum value of the vector between edge points is Demax, De (i , J) × {Demax ² − De (i, j) ² } ^{1/2 The} original edge strength is multiplied by the corrected value and used, so from the two-dimensional planar restriction image A distance distribution having three-dimensional spherical features can be obtained, and more accurate determination can be made.

  In the eleventh aspect of the present invention, the model frequency distribution calculating means determines a vector from the average coordinates of a polygon in the target model to the average coordinates of another polygon as an inter-polygon vector, and The distance between the polygons and the polygons can be obtained by using the distance between the polygons and the angle between the polygon polygon vector and the normal vector of the corresponding polygon as the unique angle between the polygons. It is characterized in that an inherent angle between the two is calculated.

  According to the eleventh aspect of the present invention, a vector from an average coordinate of a polygon to an average coordinate of another polygon is determined as an inter-polygon vector, and the size of the inter-polygon corresponds to the distance between the polygons and the inter-polygon vector Since the distance between each polygon and the unique angle between polygons are calculated by making the angle between the polygons and the normal vector of one of the two polygons unique, the three-dimensional space of each polygon It is possible to easily calculate a frequency distribution that accurately reflects the shape features of the polygon model by reflecting the above positional relationship.

  In the twelfth aspect of the present invention, the model frequency distribution calculating means prepares a one-dimensional array composed of a predetermined number (MD) of elements in calculating the distance distribution, and calculates the calculated distance. The distance distribution is calculated by equally dividing the range of the maximum value into the predetermined number, assigning each of the distances to any one of the elements based on the distances, and counting the number corresponding to the elements. It is characterized in that.

  According to the twelfth aspect of the present invention, in calculating the distance distribution, a one-dimensional array composed of a predetermined number of elements is prepared, and the range of the maximum value of the calculated distances is equally divided into the predetermined number. Since the distance distribution is calculated by assigning each distance to a predetermined number of elements based on the distance and counting the number corresponding to the elements, it is easy to use the distance between each polygon It is possible to calculate the distance distribution.

  Further, in the thirteenth aspect of the present invention, the model frequency distribution calculating means prepares a one-dimensional array composed of a predetermined number (MA) of elements in calculating the angle distribution, and makes an angle of 0 degrees to 180 degrees. Is equally divided into the predetermined number, and is assigned to any one of the elements based on the unique angle between the polygons, and the value of the element is counted to calculate the angular distribution. It is characterized by

  According to the thirteenth aspect of the present invention, in calculating the angular distribution, a one-dimensional array composed of a predetermined number of elements is prepared, and the range from an angle of 0 degrees to 180 degrees is equally divided into a predetermined number. The angle distribution is calculated by assigning to a predetermined number of elements based on the angle unique to each polygon and counting the values of the elements, so using vectors from one polygon to another It becomes possible to easily calculate the angular distribution.

  Further, in a fourteenth aspect of the present invention, the model frequency distribution calculating means is characterized in that, when counting the value of the allocated element, weighting is performed by the area of the polygon corresponding to the inter-polygon vector. I assume.

  According to the fourteenth aspect of the present invention, when calculating the distance distribution or angle distribution, when counting the value of the assigned element, weighting is performed with the area of the polygon corresponding to the inter-polygon vector, so polygon division is performed. It is possible to make it possible to determine that differences in definition of polygon division are the same (the output should be restricted) with respect to a plurality of polygon models having different definition of polygon division without reflecting much in the distance distribution or angle distribution. .

In the fifteenth aspect of the present invention,
The frequency distribution matching unit matches the distance distribution and the angle distribution of the target model with the distance distribution and the angle distribution of the restriction image, respectively.
If the correlation coefficient between the distance distributions and the correlation coefficient between the angle distributions are calculated, and the calculated correlation coefficient between the two is larger than a predetermined positive threshold, the output should be regulated. It is characterized in that it is determined.

  According to the fifteenth aspect of the present invention, when the distance distribution and the angle distribution of the object model are compared with the distance distribution and the angle distribution of the regulation image, respectively, the correlation coefficient between the distance distributions and the correlation coefficient between the angle distributions Since it was determined that the output should be restricted if both calculated and calculated correlation coefficients are greater than a predetermined positive threshold, matching of the features of the target model and the restriction image is It will be possible to do it quickly and accurately.

  In the sixteenth aspect of the present invention, the object model further includes polygon reduction means for reducing polygons such that the number of polygons included in the target model is equal to or less than a predetermined value. The model frequency distribution calculating means performs processing.

  According to the sixteenth aspect of the present invention, first, the number of polygons included in the target model is reduced so that the number of polygons is equal to or less than a predetermined value, and the distance distribution and the angle distribution are obtained for the target model in which the polygons are reduced. Since it is calculated, it becomes possible to allow the difference of the minute shape of the object model and to judge at a high speed whether the output is appropriate or not.

  The seventeenth aspect of the present invention further comprises object model division means for dividing an object model which is a polygon model to be output into a plurality of partial object models, and the model frequency distribution calculation means for the partial object models. Perform processing.

  According to the seventeenth aspect of the present invention, first, the target model is divided into a plurality of partial target models, and the distance distribution and the angular distribution are calculated for the partial target model. It is possible to determine whether the output is appropriate or not, even when the component configurations of the target model and the target model that are created based on the regulation image differ from each other when the target model representing the product is compared. Become.

  Further, in an eighteenth aspect of the present invention, the polygon is a triangle, and the target model division means is configured such that a certain polygon and three adjacent polygons sharing an edge with the polygon belong to the same partial target model. , And divided.

  According to an eighteenth aspect of the present invention, the polygon is a triangle, and the target model dividing means divides a certain polygon and three adjacent polygons sharing an edge with the polygon so that they belong to the same partial target model. As a result, it is possible to quickly and accurately divide an object model representing an article composed of a plurality of parts into a plurality of partial object models.

  Further, in the nineteenth aspect of the present invention, a pixel forming an edge of the object is extracted as an edge point from the regulation image, and a distance between each edge point of the regulation image and an angle unique to each edge point Calculated by the image frequency distribution calculation device which calculates the distance distribution which is the frequency distribution of the distance between each edge point based on the calculation result and the angle distribution which is the frequency distribution of the angle unique to each edge point The apparatus is characterized by further comprising registration means for receiving a frequency distribution and registering the received frequency distribution in the database.

  According to the nineteenth aspect of the present invention, with respect to the restriction image, pixels constituting the edge of the object are extracted as edge points, and the distance between each edge point of the restriction image and the unique angle between each edge point are calculated Then, based on the calculation result, separately prepare an image frequency distribution calculation device for calculating an angle distribution which is a frequency distribution of distances between each edge point and a frequency distribution of angles unique to each edge point, Since the frequency distribution of the restriction image is received from the image frequency distribution calculation device and registered in the database, it is possible to quickly update the database from a remote place.

In the twentieth aspect of the present invention,
A unit for outputting the target model to a connected three-dimensional object forming apparatus;
When it is determined that the output should be restricted (output unsuitable) by the frequency distribution collating means executed in parallel with the three-dimensional object formation processing by the three-dimensional object formation device, the three-dimensional object formation device, A unit for outputting an output cancellation instruction of the target model;
And the like.

  According to the twentieth aspect of the present invention, the object model is output to the connected three-dimensional object formation apparatus, and it is determined that the output should be restricted as a result of the collation by the frequency distribution collating means executed in parallel. In this case, since the output stop instruction of the target model is output to the three-dimensional object forming apparatus, the output may be canceled only when the output is unsuitable, without delaying the formation of the time-consuming three-dimensional object. It becomes possible.

In the twenty-first aspect of the present invention,
A configuration in which an output control terminal and a processing server are connected via a network,
The output control terminal includes the model frequency distribution calculating unit.
The processing server is
Said database,
Receiving means for receiving distance distribution and angle distribution of the target model from the output control terminal via a network;
The frequency distribution comparing means;
An output appropriateness data transmitting unit that transmits data based on whether or not the output should be restricted, which is determined by the frequency distribution comparing unit, to the output control terminal;
And the like.

  According to a twenty-first aspect of the present invention, data is received based on whether or not the output obtained by receiving the distance distribution and angle distribution of the target model via the network and determining whether or not the output should be restricted. Since transmission is performed to the distance distribution of the object model and the transmission source of the angular distribution, it is possible to provide a cloud-type determination as to whether or not the output should be restricted, and the processing on the output side can be reduced. Furthermore, since the database can be centrally managed on the cloud side and there is no need to manage the database in the output control terminal connected to each three-dimensional object forming apparatus such as a 3D printer, the output is always regulated based on the latest database. It is possible to determine whether or not to do so.

In the twenty-second aspect of the present invention,
The three-dimensional object modeling data output regulation device;
A three-dimensional object formation apparatus for forming a three-dimensional object using the target model permitted to be output by the three-dimensional object formation data output regulation device;
Providing a three-dimensional object forming system characterized by comprising:

  According to a twenty-second aspect of the present invention, a three-dimensional object forming apparatus for forming a three-dimensional object using the three-dimensional object formation data output control device and the target model permitted to output by the three-dimensional object formation data output restriction device Since the modeling system is realized, it is possible to provide a three-dimensional object modeling system in the form of a 3D printer or the like incorporating a board computer.

  According to the present invention, when outputting a three-dimensional object based on a polygon model, it is possible to accurately determine whether the output should be restricted or not based on a two-dimensional image.

It is a figure for demonstrating the basic concept of this invention. FIG. 6 is a hardware configuration diagram of an image frequency distribution calculation device 300. FIG. 6 is a functional block diagram showing a configuration of an image frequency distribution calculation device 300. 7 is a flowchart showing the processing operation of the image frequency distribution calculation device 300. It is a figure which shows the 16 direction edge extraction filter which is a filter matrix. It is a figure which shows the edge strength correction table which recorded the edge strength correction amount according to direction. It is a figure which shows the relationship of the distance and angle in distance distribution and angle distribution. It is a hardware block diagram of a solid thing shaping system containing data output control device 100 for solid thing shaping concerning one embodiment of the present invention. It is a functional block diagram showing composition of a data output regulation device for solid thing modeling concerning a 1st embodiment of the present invention. It is a flowchart which shows the processing outline of the data output regulation device for solid thing modeling concerning a 1st embodiment of the present invention. It is a flowchart which shows the detail of calculation processing of model frequency distribution of step S100. It is a flowchart which shows the detail of collation process of frequency distribution of step S200. It is a functional block diagram showing composition of a data output regulation device for solid thing modeling concerning a 2nd embodiment of the present invention. It is a flowchart which shows the processing outline of the data output control device for solid thing modeling concerning a 2nd embodiment of the present invention. It is a figure which shows the distance distribution and angle distribution which were obtained with respect to two spherical model A, B, respectively. It is a figure which shows the distance distribution and angle distribution which are obtained with respect to each of two spherical body models B and C. It is a figure which shows the basic principle of polygon deletion. It is a flowchart which shows the detail of a polygon reduction process. It is a flowchart which shows the detail of object model division processing. It is a hardware block diagram of a solid thing shaping system including a data output control device for solid thing shaping in a modification. It is a hardware block diagram of a three-dimensional object formation system of a cloud type. It is a functional block diagram of a cloud type three-dimensional object formation system. It is a figure which shows the example of frequency distribution calculation with respect to the sphere a and the circle b. It is a figure which shows the example of frequency distribution calculation with respect to the spherical body c. It is a figure which shows the frequency distribution calculation example with respect to Daruma d and Daruma e. It is a figure which shows the frequency distribution calculation example with respect to Daruma f.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the drawings.
<1. Basic concept of the present invention>
First, the basic concept of the present invention will be described. In order to model a figure based on a motif image of a character with a 3D printer, it is necessary to create a three-dimensional polygon model, but in recent years, it is only necessary to trace over illustrations and photos displayed on the screen Therefore, development of tools that can automatically create a three-dimensional polygon model semi-automatically by adding depth by cylindrical shape etc. has become active. Along with this, illegal figures are easily created by 3D printers based on two-dimensional images with high copyrights such as characters and illustrations, and the market for figure regulars molded with molds is about to be compressed. .

  In the present invention, when a three-dimensional polygon model is created based on a two-dimensional image with high copyright property such as a character and an illustration, and this polygon model is to be output without permission of the character and illustration owner, the output is One purpose is to regulate. However, it is difficult to create a database of 3D polygon models of illegal figures in advance, and even if they can be registered in the database, there is no limit to the shape of 3D polygon models created based on the character motif image In the method of collating the polygon model to be output with the polygon model registered in the database, timely output regulation can not be performed. Therefore, when trying to output a two-dimensional motif image of a typical character with less variety to a database and outputting a three-dimensional polygon model with a 3D printer, matching is performed using the registered two-dimensional image It is necessary to realize the function that allows you to

  FIG. 1 is a diagram for explaining the basic concept of the present invention. FIG. 1A shows a three-dimensional polygon model to be output, and FIG. 1B shows a two-dimensional image to be restricted registered in a database. In the present invention, two types of frequency distributions for the distance and angle shown in FIG. 1C are created from the polygon model shown in FIG. 1A, and from the image shown in FIG. Two frequency distributions of distance and angle as shown in c) are created as shown in FIG. Then, by matching the two frequency distributions of the distance and angle shown in FIG. 1C and FIG. 1D with each other, they are collated and correlated with each other. The similarity between the polygon model shown in FIG. 1A and the image shown in FIG. 1B can be determined. When the polygon model has a feature similar to the registered image, the output of the polygon model is restricted.

<2. First embodiment>
<2.1. Device Configuration of Image Frequency Distribution Calculator>
In the first embodiment, when the output suitability of the three-dimensional polygon model is determined, the frequency distribution created from the polygon model and the frequency distribution created from the two-dimensional image to be restricted are compared. Therefore, it is necessary to create a frequency distribution from the image in advance. First, an image frequency distribution calculation device that creates a frequency distribution from such an image will be described.

  FIG. 2 is a hardware configuration diagram of the image frequency distribution calculation device. The image frequency distribution calculation device 300 can be realized by a general-purpose computer, and as shown in FIG. 2, a central processing unit (CPU) 81, a random access memory (RAM) 82 as a main memory of the computer, and the CPU 81. A large-capacity storage device 83 such as a hard disk or flash memory for storing programs and data executed by a computer, a key input I / F (interface) 84 such as a keyboard and a mouse, and an external such as a 3D printer or data storage medium A data input / output I / F (interface) 85 for performing data communication with the apparatus, and a display unit 86 which is a display device such as a liquid crystal display are provided and mutually connected via a bus.

  FIG. 3 is a functional block diagram showing the configuration of the image frequency distribution calculation device. In FIG. 3, 91 is a regulation image storage means, 92 is an image frequency distribution calculation means, and 93 is an image frequency distribution storage means.

  The image frequency distribution calculation means 92 calculates the distance between each edge point in the restriction image and the inherent angle between each edge point in the restriction image which is the image to be restricted, and each edge point based on the calculation result A process of calculating a distance distribution, which is a frequency distribution of distances between them, and an angle distribution, which is a frequency distribution of angles unique to each edge point, is performed.

  The image frequency distribution calculating unit 92 is realized by the CPU 81 executing a program stored in the storage device 83. The regulation image storage unit 91 is a storage unit that stores a regulation image which is an image whose output is to be regulated, and is realized by the storage device 83.

  The image frequency distribution storage unit 93 stores the distance distribution and the angle distribution calculated as two types of frequency distribution for a regulated image which is an image whose output is to be regulated, and stores it in a database. It is realized by 83. The distance distribution and the angle distribution, which are two types of frequency distribution, serve as feature vectors representing the features of the regulation image. That is, although the difference of the original restriction image can be identified by the two types of frequency distribution, the original restriction image can not be restored. This is similar to the fact that individual differences can be identified by fingerprints, but not the appearance of the person itself. Thus, the two frequency distributions do not play a role as works, but serve as so-called fingerprints, which prove the identity of the two works. Not only distance distribution and angle distribution, but also regulation image itself based on calculation of distance distribution and angle distribution is registered in the image frequency distribution storage means 93 for the regulation image for which the similarity is judged. Since human beings can visually confirm, normally, the regulation image itself is also registered at a resolution not worth commercial quality, under permission from the copyright holder.

  Each configuration means shown in FIG. 3 is actually realized by mounting a dedicated program in hardware such as a computer and its peripheral equipment as shown in FIG. That is, the computer executes the contents of each means in accordance with the dedicated program.

  The storage device 83 illustrated in FIG. 2 is implemented with a dedicated program for operating the CPU 81 to cause the computer to function as an image frequency distribution calculation device. By executing this dedicated program, the CPU 81 realizes a function as the image frequency distribution calculating means 92. The storage device 83 not only functions as the restriction image storage means 91 and the image frequency distribution storage means 93, but also stores various data necessary for processing as an image frequency distribution calculation device.

<2.2. Processing Operation of Image Frequency Distribution Calculator>
Next, the processing operation of the image frequency distribution calculation apparatus shown in FIGS. 2 and 3 will be described. The image frequency distribution calculating means 92 of the image frequency distribution calculating device calculates a distance distribution and an angle distribution which are two types of frequency distributions for a regulated image which is an image whose output is to be regulated. FIG. 4 is a flowchart showing details of frequency distribution calculation processing. Here, the restricted image is treated as a 256-tone monochrome image in order to simplify the filter calculation described later. Therefore, in the case of a color image, the process of converting it into a 256-tone monochrome image is performed in advance, and in the case of a binary image, it is processed as a 256-tone monochrome image having a value of either 0 or 255. Do. Therefore, the restriction image of the pixel number Xs in the x direction and the pixel number Ys in the y direction is Img (x, y) = 0 to 255 (x = 0, ..., Xs-1; y = 0, ... , Ys-1). In the restriction image, an object part and a background part are present, and the object part is displayed in gray or white Img (x, y)> 0, and the background part is displayed in black Img (x, y) Set to = 0. Since the four corners of the restriction image serve as the background, Img (0,0) = Img (0, Ys-1) = Img (Xs-1,0) = Img (Xs-1, Ys-1) = 0 ing.

  First, the image frequency distribution calculating unit 92 performs a filter operation on each pixel of the restriction image to calculate an edge strength and an edge direction vector of each pixel (step S510). First, an edge extraction filter which is a filter matrix used for filter calculation will be described. The filter matrix is also called a matrix filter, a spatial filter, etc., and is generally used to convert the pixel value of the central pixel (the target pixel) and the pixel values of the peripheral pixels using the pixel values of the central pixel. is there. Basically, it is performed on all pixels in the restriction image.

  FIG. 5 is a diagram showing a 16-direction edge extraction filter used in the present embodiment. The 16-direction edge extraction filter shown in FIG. 5 is such that the values of nine pixels in the vicinity of eight including itself are changed in weight according to the respective directions of the sixteen directions. In the example of FIG. 5, 16 directions from the direction 0 to the direction 15 are shown. Taking the case of the direction 0 as an example, (0.0, -1.0) on the right side indicates a direction vector which is a unit vector specifying the direction 0. Further, nine numbers of vertical 3 × horizontal 3 indicate weights defined by M (d, u, v) = [− 2, −1, 1, 2]. The weight takes four values of -2, -1, 1 and 2. And nine numbers take the center value as (u, v) = (0, 0), and u, v take three values of -1, 0, 1 respectively. In the case of the center, M (d, 0, 0) = 1 regardless of the value of d, and in the case of other than the center, -2 of the value of M (d, u, v) according to the direction d The positions -1, 1 and 2 are different. In the case of direction 0 (d = 0), as shown in the example on the right side of Fig. 5, the weights M (d, u, v) are M (0, -1, -1) = -2 to M (0, 0) Nine pieces of 1, 1) = 1 are defined. At this time, the direction vectors Ex (d) and Ey (d) of the filter in the direction 0 are Ex (0) = 0.0 and Ey (0) = − 1.0. The 16-direction edge extraction filter shown in FIG. 5 changes the direction in 22.5 degrees.

  The image frequency distribution calculating unit 92 executes the processing according to the following [Expression 1] using the edge extraction filter M (d, u, v) shown in FIG. 5, and the edge strength and edge direction vector of each pixel Decide.

Formula 1
F (d, x, y) = [Σ _{v = -1, 1} 1,1 _{u = -1, 1} M (d, u, v) [I mg (x, y)-128]] / 6
EK (x, y) = F (dmax, x, y) = MAX _{d = 0, D-1} F (d, x, y)
Vx (x, y) = Ex (dmax)
Vy (x, y) = Ey (dmax)

In the first expression of the above [Equation 1], all subscripts of Σ, “v = −1, 1” and “u = −1, 1”, v is −1 to 1 and u is −1 to 1 In the case of taking an integer of, it is shown that the sum is obtained. The filter operation value F (d, x, y) representing the intensity is obtained for each pixel according to the first expression of the above [Expression 1]. In the second expression of the above [Expression 1], "MAX _{d = 0, D} " indicates the maximum value in all directions from the direction d = 0 to the direction d = D-1. When using a 16-direction edge extraction filter, D = 16. The maximum filter operation value is obtained as the edge strength EK (x, y) of each pixel according to the second expression of the above-mentioned [Expression 1]. Also, the maximum filter calculation value is taken, and the direction d for giving the edge strength is dmax. The edge direction vectors Vx (x, y) and Vy (x, y) of the pixel (x, y) are obtained as the direction of the direction dmax according to the third equation and the fourth equation of the above-mentioned [Equation 1]. An edge direction vector is a vector indicating a direction for specifying an edge of an edge point (a pixel forming an edge of an object), and is orthogonal to the boundary at the boundary between the object and the background in the restricted image and is an object From the direction of the background Inside the object of the control image, at the boundary where the difference in density is significant, the direction perpendicular to the boundary line and the direction from the lower density (near white) to the higher density (near black) are indicated.

  Next, the image frequency distribution calculating unit 92 performs edge intensity correction and tone correction of each pixel (step S520). First, edge strength correction is performed using an edge strength correction amount for each direction. An edge strength correction table in which the edge strength correction amount Tc (d) for each direction is recorded is shown in FIG. As shown in FIG. 6, in the edge strength correction table, the edge strength correction amount is recorded for each of the directions d = 0-15. The edge strength correction table shown in FIG. 6 indicates the edge strength correction amount in%. As described above, the 16-direction edge extraction filter is in 22.5 degrees increments, and the edge strength correction table is also in 22.5 degrees increments. In the example of FIG. 6, the edge strength correction amount is repeated so as to have the same value every four directions d, that is, every 90 degrees. This means that since the edge strength calculated by the 16-direction edge extraction filter has a predetermined characteristic in units of 90 degrees, it is necessary to make a correction accordingly.

  The image frequency distribution calculating means 92 executes the processing according to the following [Expression 2] using the edge strength correction amount Tc (d) shown in FIG. 6 to correct the edge strength of each pixel.

Formula 2
EK '(x, y) = EK (x, y) · Tc (d) / 100

  In the above [Formula 2], “·” indicates multiplication. Since Tc (d) is a numerical value in% as shown in FIG. 6, the edge strength before correction is multiplied by 100 and then divided by 100. As a result, corrected edge strength EK '(x, y) is obtained.

  Next, the image frequency distribution calculating unit 92 performs tone correction (contrast correction) of the edge intensity. Specifically, processing according to the following [Equation 3] is executed to perform tone correction of the edge intensity of each pixel.

[Equation 3]
EK ′ ′ (x, y) = {EK ′ (x, y) −SL} · 256 / {256−SL}

  In the above [Formula 3], 256 is the number of gradations, and indicates the number of 0 to 255 that can be taken by the pixel of the regulation image. SL indicates a set minimum value which is a minimum value after tone correction. The set minimum value SL can be set as appropriate, but is preferably about 72 when the original image can take values of 0 to 255. The set minimum value substantially functions as a threshold for determining whether or not to set an edge point by comparing with the edge strength.

  As a result of performing edge strength correction according to [Expression 2] and tone correction according to [Expression 3], a corrected edge strength EK ′ ′ (x, y) is obtained, but in order to avoid the explanation being complicated, Hereinafter, the corrected edge strength is described as EK (x, y).

  Next, edge points are extracted (step S530). Specifically, the edge strength corrected in step S520 is compared with a threshold set in advance, and a pixel whose edge strength is equal to or higher than the threshold is extracted as an edge point. When gradation correction is performed in step S520, it is preferable to set "0" as the threshold value so that a pixel whose edge intensity after gradation correction is 0 or more is extracted.

  Next, the image frequency distribution calculating unit 92 corrects the edge direction vector (step S540). As described above, the edge direction vector is calculated as a vector indicating any direction of the number D of directions d using an edge extraction filter. Therefore, for example, in the case of D = 16, there are only 16 directions, and the angle between a certain edge point and another edge point vector (an edge point vector) calculated using this is discontinuous (Because the vector between the edge points is a continuous quantity, the angle with this is not limited to 16 stages, but it is rather rough). On the other hand, the angle specified in the three-dimensional polygon model to be compared is calculated using the normal vector of the polygon corresponding to the edge direction vector, and the normal vector is a continuous quantity in the three-dimensional space, The angle between one polygon and another polygon vector (inter-polygon vector) calculated using this is a continuous amount in the range of 0 to 180 degrees. Therefore, in the angle distribution created from the restriction image, the resolution of the angle becomes coarser than the angle distribution created from the target model, and the correlation between the two can not be calculated with high accuracy. Therefore, in this case, correction of the edge direction vector is performed using pixels around the pixel specified as the edge point. Specifically, the processing according to the following [Equation 4] is executed to correct the edge direction vector of each edge point.

[Equation 4]
Vx x (x, y) = [Σ _{v = -1, 1} Σ _{u = -1, 1; EK (x + u, y + v) EK EK (x, y)} V x (x + u, y + v) · EK ( x + u, y + v)] / [Σ _{v = -1, 1 u u = -1, 1; EK (x + u, y + v) EK EK (x, y)} EK (x + u, y + v)]
Vy ((x, y) = [ _{v v = -1, 1 u u = -1, 1; EK (x + u, y + v) EK EK (x, y)} Vy (x + u, y + v) · EK ( x + u, y + v)] / [Σ _{v = -1, 1 u u = -1, 1; EK (x + u, y + v) EK EK (x, y)} EK (x + u, y + v)]

  In the above [Equation 4], the calculation is performed only on the pixels for which EK (x + u, y + v) ≧ EK (x, y) (the center pixel (x, y) is also an addition target). EK (x + u, y + v) EK EK (x, y) means a pixel (x + u, y + v) of a peripheral edge point having an edge strength equal to or higher than the edge strength EK (x, y) of the central pixel (edge point) Only indicates that the summation operation is performed. In the numerator of the formula 4, the edge direction vector before correction is multiplied by the edge strength before the correction. In the denominator of the formula 4, the central pixel (edge point) and its eight neighboring pixels are The average edge strength of the combined maximum 9 pixels is calculated. After all, in the above [Equation 4], the edge strength in the direction Vx (x + u, y + v) specifying the edge at the edge point (x + u, y + v) around the target edge point (x, y) and the edge strength of the edge point around This means that EK (x + u, y + v) is weighted and averaged.

  Next, the image frequency distribution calculating unit 92 calculates a vector between each edge point (step S550). Assuming that the extraction order of edge points extracted in step S530 is i, if Ne edge points are extracted, the array of edge points is [Xe (i), Ye (i)] (i = 0,. , Ne-1). Then, the edge strength EK (i) = EK (Xe (i), Ye (i)) of the ith edge point is set. Furthermore, the image frequency distribution calculating unit 92 executes a process according to the following [Equation 5] to obtain an edge point i (Xe (i), Ye (i)) in the regulated image within the same regulated image. Between the edge points (Dxe (i, j), Dye (i) which are vectors to other edge points j (j = 0, ..., Ne-1) (Xe (j), Ye (j)) of , J)). Since the inter-edge point vector is a vector to an edge point other than itself, the case of i = j is excluded.

[Equation 5]
D xe (i, j) = Xe (j)-Xe (i)
Dye (i, j) = Ye (j)-Ye (i)

  Next, the image frequency distribution calculating unit 92 calculates the maximum distance between the edge points (step S560). Specifically, by executing the processing according to the following [Equation 6], an edge point i (Xe (i), Ye (i)) in the restricted image to another edge point j (Xe (j) The distance De (i, j) to Y.sub.1 and Y.sub.e (j)) is obtained, and the largest one of Ne.times. (Ne-1) / 2 distances De (i, j) is taken as the maximum distance Demax.

[Formula 6]
De (i, j) = [Dxe (i, j) ² + Dye (i, j) ² ] ^1/2
Demax = MAX _{j = 0, Ne-1; i = 0, Ne-1} De (i, j)

  In the above [Equation 6], MAX is a possible range of subscript “i, j” i = 0,..., Ne−1, j = 0,. It shows that the maximum value is selected from De (i, j) in the case of j).

  Next, the image frequency distribution calculating unit 92 calculates an angle formed by the inter-edge point vector and the edge direction vector of the corresponding edge point (step S570). Specifically, an inter-edge point vector (Dxe (i, j), Dye (i, j)) between the edge point i and the edge point j is executed by executing the processing according to the following [Equation 7]. And the angle Ae (i, j) formed by the edge direction vector (Vx (j), Vy (j)) (= (Vx (x, y), Vy (x, y)) of the edge point j .

[Equation 7]
Ae (i, j) = [cos- ¹ [Dxe (i, j) Vx (j) + Dye (i, j) Vy (j)] / De (i, j)] 180 /?

  In the above [Equation 7], in order to change the unit from radian to degree, the portion enclosed by [] is multiplied by 180 / π. Therefore, Ae (i, j) takes a value of 0 to 180. The angle between each edge point vector and each edge direction vector is a unique angle between each edge point.

  Next, the image frequency distribution calculating means 92 calculates the distance distribution between each edge point (step S580). The distance distribution calculated in the present embodiment is a distribution of distances obtained by weighting edge strengths, where the number of elements is MD, and the frequency of each element md (md = 0,..., MD-1) is Hdo (md) It is expressed as).

  The image frequency distribution calculating unit 92 sets initial values as Hdo (md) = 0 and EKsum = 0, i = 0,..., Ne-1, j = 0,. The frequency Hdo (md) of the element md is calculated by executing the processing according to the following [Equation 8] in the case of = j). Here, EKsum is the sum of edge strengths weighted in the distance distribution Hdo (md) (md = 0,..., MD-1).

[Equation 8]
If Demax · md / MD ≦ De (i, j) <Demax · (md + 1) / MD, then
Hdo (md) H Hdo (md) + EK (j)
EKsum EK EKsum + EK (j)

  In the above [Equation 8], the distance De (i, j) from the edge point i to the edge point j is not less than a value obtained by multiplying md / MD by the maximum distance Demax and smaller than a value obtained by multiplying (md + 1) / MD In this case, it means that the edge strength EK (j) of the edge point j is added to Hdo (md).

  The distance distribution Hdo (md) (md = 0,..., MD-1) is obtained by executing the processing according to the above-mentioned [Equation 8] between Ne (Ne-1) / 2 edge points. It is calculated. Since the edge strength EK (j) is added to each element, not just the frequency, the distance distribution Hdo (md) indicates the edge strength weighted distance distribution. It is also possible to simply add 1 instead of the edge strength EK (j), but in that case, the distance distribution does not include the edge strength, and the case where the pixel is coarse and fine for the same regulated image At this point, a noticeable difference occurs in the distance distribution. Therefore, in the present embodiment, edge strengths of edge points are added at the time of calculation of the distance distribution. After execution of the processing according to the above [Equation 8], the unit is normalized to the edge strength rate [%] by further executing processing of multiplying Hdo (md) by 100 / EKsum and replacing Hdo (md). It is also good.

  Since the distance distribution calculated by the processing according to the above-mentioned [Equation 8] is obtained from the two-dimensional restriction image, it is optimal for matching with the distance distribution obtained from the three-dimensional polygon model later as it is It is not in the state. Therefore, when calculating the distance distribution from the two-dimensional restriction image, it is preferable to perform spherical correction. Specifically, the spherical correction edge strength EKa (j) is calculated by executing the processing according to the following [Expression 9].

[Equation 9]
EKa (j) = EK (j) · De (i, j) · {Demax ² −De (i, j) ² } ^1/2

  The spherical correction edge strength EKa (j) calculated by executing the processing according to the above equation 9 is replaced with EK (j) in the above equation 8 and the processing according to the above equation 8 is carried out. Run. By using the spherical correction edge strength EKa (j), spherical correction is performed, and a distance distribution having spherical features can be obtained from a planar restriction image.

  Next, the image frequency distribution calculating unit 92 calculates an angle distribution of the edge point vector and the edge direction vector of the corresponding edge point (step S590). The angle distribution calculated in the present embodiment is a distribution of angles obtained by weighting the edge strength, and the number of elements is MA, and the frequency of each element ma (ma = 0,..., MA-1) is Hao (ma It is expressed as). The number of elements MA may be the same as the number of elements MD.

  The image frequency distribution calculating unit 92 sets the initial value to Hao (ma) = 0 and executes the processing according to the following [Equation 10] to calculate the frequency Hao (ma) of the element ma.

[Equation 10]
If 180 · ma / MA ≦ Ae (i, j) <180 · (ma + 1) / MA, then
Hao (ma) H Hao (ma) + EK (j)

  [Equation 10] is a value obtained by multiplying ma by the angle Ae (i, j) of the angle between the edge point vector of the edge point i and the edge point j and the edge direction vector of the edge point j by ma / MA. As described above, when smaller than the value obtained by multiplying (ma + 1) / MA, it means that the edge strength EK (j) of the edge point j is added to Hao (ma).

  The angular distribution Hao (ma) (ma = 0, ..., MA-1) can be obtained by executing the processing according to the above-mentioned [Equation 10] between Ne (Ne-1) / 2 edge points. It is calculated. Since the edge strength EK (j) is added to each element, not just the frequency, the angular distribution Hao (ma) indicates an angular distribution of edge strength weighting. It is also possible to simply add 1 instead of the edge strength EK (j), but in that case, the angular distribution does not include the edge strength, and the case where the pixel is coarse and fine for the same regulated image At this point, a noticeable difference occurs in the distance distribution. Therefore, in the present embodiment, edge strengths of edge points are added at the time of calculation of the angular distribution. After execution of the processing according to the above [Equation 10], the unit is normalized to the edge strength rate [%] by further executing processing of multiplying Hao (ma) by 100 / EKsum and replacing it with Hao (ma). It is also good. Here, EKsum can be diverted from EKsum obtained when calculating the distance distribution based on [Equation 8].

  The angle distribution calculated by the processing according to the above-mentioned [Equation 10] is obtained from the two-dimensional restriction image, so as it is, it is most suitable for matching with the angle distribution obtained later from the three-dimensional polygon model. It is not in the state. Therefore, when calculating the distance distribution from the two-dimensional restriction image, it is preferable to perform spherical correction. Specifically, using De (i, j) used to calculate Ae (i, j) based on Equation 7, the same as Equation 9, according to Equation 9, is used, as in the case of distance distribution. By executing the process, the spherical correction edge strength EKa (j) is calculated.

  The spherical correction edge strength EKa (j) calculated by performing the processing according to the above [Equation 9] is replaced with EK (j) in the above [Equation 10] to perform the processing according to the above [Equation 10] Run. By using the spherical correction edge strength EKa (j), spherical correction is performed, and an angular distribution having spherical characteristics can be obtained from a planar restriction image.

  The two frequency distributions of the distance distribution and the angle distribution obtained by the processing shown in FIG. 4 are stored in the image frequency distribution storage unit 93 in association with identification information for specifying a restriction image.

  The relationship between the distance and angle in the distance distribution and angle distribution obtained from the restriction image (2D image) is shown in FIG. 7 (a). In FIG. 7A, the hatched portion is the target of the regulation image. The target object of the regulation image means the pattern of the character excluding the background portion. This part is largely different in pixel value from the background. The edge point is located at the boundary between the object and the background or at the boundary where the difference in light and shade within the object is not present in the example of FIG. 7A. Then, the distance from one edge point in the restriction image to another edge point in the same restriction image is calculated between the edge points. The unique angle between each edge point is the angle between the edge point vector, which is a vector from one edge point to another edge point, and the edge direction vector of the edge point.

<2.3. Configuration of data output regulation device for three-dimensional object formation>
Next, a three-dimensional object shaping data output regulating device according to the first embodiment of the present invention will be described. FIG. 8 is a hardware configuration diagram of a three-dimensional object formation system including the three-dimensional object formation data output control device 100 according to the first embodiment of the present invention. The three-dimensional object formation data output restricting device 100 according to the present embodiment can be realized by a general-purpose computer, and as shown in FIG. 8, a CPU (Central Processing Unit) 1 and a RAM (main memory of the computer). Random Access Memory 2) Hard disk for storing programs and data executed by CPU 1, large-capacity storage device 3 such as flash memory, Key input I / F (interface) 4 such as keyboard and mouse, 3D A data input / output I / F (interface) 5 for data communication with an external device such as a printer or a data storage medium, and a display unit 6 which is a display device such as a liquid crystal display are connected to each other via a bus. ing.

  The 3D printer 7 is a general-purpose 3D printer, which processes materials such as resin and gypsum based on data for three-dimensional object formation which is a polygon model representing the three-dimensional shape of a three-dimensional object by a set of polygons. It is a three-dimensional object formation apparatus which forms. The 3D printer 7 includes a data processing unit 7a and an output unit 7b. The data processing unit 7a of the 3D printer 7 is connected to the data input / output I / F 5 so that the output unit 7b forms a three-dimensional object based on the three-dimensional object formation data received from the data input / output I / F 5. It has become.

  In FIG. 8, the three-dimensional object modeling data output control device 100 and the 3D printer 7 are shown in a separated form, but most three-dimensional printer products currently on the market are Constituent elements: CPU 1, RAM 2, storage device 3, key input I / F 4 (not keyboard / mouse for general-purpose computer but several buttons of ten keys level), data input / output I / F 5, display unit 6 (several lines A small liquid crystal panel capable of displaying the characters of () and a touch panel are overlapped and the key input I / F 4 is often used. Therefore, the 3D printer 7 itself directly receives data for modeling a three-dimensional object via an external storage medium, and it is possible to operate a three-dimensional object by itself (in particular, in a consumer 3D printer, this form is more common) ). That is, there are many cases where the three-dimensional object formation system shown in FIG. 8 is accommodated in one case and distributed as a product of “3D printer”.

  FIG. 9 is a functional block diagram showing the configuration of a three-dimensional object formation data output restricting device according to the present embodiment. In FIG. 9, 10 is a model frequency distribution calculating means, 20 is a frequency distribution comparing means, 30 is a target model storage means, and 40 is a frequency distribution database.

  The model frequency distribution calculating means 10 calculates the distance between the polygons in the target model and the unique angle between the polygons for the target model which is the polygon model to be output, and is the frequency distribution of the calculated distance. A distance distribution is calculated, and a process of calculating a distance distribution which is a frequency distribution of distances between polygons and an angle distribution which is a frequency distribution of angles unique to each polygon is performed based on the calculation result. The frequency distribution collating means 20 collates the distance distribution and angle distribution calculated for the object model with the distance distribution and angle distribution of the regulation image registered in the frequency distribution database 40, respectively, and determines whether or not the output should be regulated, That is, it is determined whether the output is inappropriate or appropriate.

  The model frequency distribution calculating means 10 and the frequency distribution comparing means 20 are realized by the CPU 1 executing a program stored in the storage device 3. The target model storage unit 30 is a storage unit that stores a target model that is a polygon model that is a target of determination of whether to restrict the output, and is realized by the storage device 3. A polygon model is data representing a predetermined three-dimensional shape in a three-dimensional space by a set of polygons, and is output as three-dimensional object formation data to a three-dimensional object formation apparatus such as a 3D printer. In the present embodiment, STL (Standard Triangulated Language) is adopted as a data format of the polygon model.

  The frequency distribution database 40 stores a restriction model which is a polygon model whose output is to be restricted, and a distance distribution and an angle distribution calculated as two types of frequency distributions for the control image which is an image whose output is to be restricted. , And is realized by the storage device 3. The distance distribution and the angle distribution, which are two types of frequency distribution, serve as feature vectors representing features of the regulation image and the regulation model. That is, although the difference between the original regulation model and the regulation image can be identified by the two types of frequency distribution, the original regulation model and the regulation image can not be restored. This is similar to the fact that individual differences can be identified by fingerprints, but not the appearance of the person itself. Thus, the two frequency distributions do not play a role as works, but serve as so-called fingerprints, which prove the identity of the two works. Not only the distance distribution and the angle distribution, but also the restriction model and the restriction image itself based on the calculation of the distance distribution and the angle distribution can be registered in the frequency distribution database 40. With regard to regulatory models, normally, regulatory models themselves are not registered due to copyright issues, but for regulatory images, operations are generally registered with permission from the copyright holder and database registration is performed. In order to make it possible to visually check the image, the regulation image itself is often registered together.

  Each component means shown in FIG. 9 is actually realized by mounting a dedicated program in hardware such as a computer and its peripheral equipment as shown in FIG. That is, the computer executes the contents of each means in accordance with the dedicated program. In the present specification, a computer means an apparatus having an arithmetic processing unit such as a CPU and capable of data processing, and is incorporated not only into a general-purpose computer such as a personal computer but also into a "3D printer" as a product. Also included on board computers.

  The storage device 3 shown in FIG. 8 is implemented with a dedicated program for operating the CPU 1 to cause the computer to function as a three-dimensional object modeling data output regulating device. By executing this dedicated program, the CPU 1 realizes functions as the model frequency distribution calculating means 10 and the frequency distribution comparing means 20. The storage device 3 not only functions as the target model storage means 30 and the frequency distribution database 40, but also stores various data necessary for processing as a three-dimensional object modeling data output regulating device.

<2.4. Processing operation of data output regulation device for three-dimensional object formation>
2.4.1. Pre-processing>
Next, the processing operation of the three-dimensional object formation data output regulating device shown in FIGS. 8 and 9 will be described. First, two frequency distributions are created from the regulation image which is an image whose output is to be regulated by the image frequency distribution calculating apparatus as described above, and the created frequency distributions are registered in the frequency distribution database 40. The image frequency distribution calculation device does not have to be realized by a computer separate from the three-dimensional object formation data output control device, and is for realizing the image frequency distribution calculation device in a computer that realizes the three-dimensional object formation data output restriction device. It is also possible to incorporate a program.

  Furthermore, not only the frequency distribution created from the control image, but also a frequency distribution that expresses a control model that is a polygon model to be controlled may be created and registered in the frequency distribution database 40. In this case, a distance distribution and an angle distribution, which are two types of frequency distribution, are created for the regulation model. The creation of the distance distribution and the angle distribution from the polygon model can be performed in the same manner as the processing in the data output regulation device for three-dimensional object formation described later. The creation of the distance distribution and the angular distribution from the regulation model may be performed by the three-dimensional object formation data output restriction device, or may be performed by executing the same program by another computer. The created distance distribution and angle distribution are registered in the frequency distribution database 40. The frequency distribution generated from the restriction image, which is two-dimensional data, and the frequency distribution generated from the restriction model, which is three-dimensional data, are generated so as to have the same format. Thereby, at the time of collation mentioned later, it becomes possible to collate similarly about both a regulation picture and a regulation model.

  The target of output control is not only dangerous materials such as guns and swords, but also works such as characters. In any case, the produced image or polygon model is a work whose copyright holder exists. Therefore, in order to register an image or polygon model that is a work in a database, the act itself requires permission of the author. The image is often a legal entity in which the copyright holder is clearly indicated, and it is possible to obtain permission, but for polygon models, many individuals whose copyright holder is unknown, such as an individual, are difficult to obtain permission It is. Although the format of the distance distribution and the angle distribution can distinguish the difference between the original image and the polygon model, the original image and the polygon model can not be restored. This is similar to the fact that individual differences can be identified by fingerprints, but not the shape of the person itself can be restored, and the form of distance distribution and angular distribution corresponds to fingerprints, and the work Not applicable to

  Therefore, as in the present invention, by registering in the form of distance distribution and angle distribution, it is possible to construct a database in which the features of the regulation image and the regulation model are recorded without requiring the author's permission. However, with regard to the regulation image, the regulation image itself is also registered in a state where the resolution is lowered to a level which can not be used commercially with the permission of the author, and the regulation image matched with the form of distance distribution and angle distribution It is desirable to use an operation that allows visual confirmation.

<2.4.2. Processing outline>
Next, the processing operation of the three-dimensional object formation data output regulating device shown in FIGS. 8 and 9 will be described. FIG. 10 is a flowchart showing a processing outline of the three-dimensional object formation data output regulating device according to the first embodiment of the present invention. First, the model frequency distribution calculating means 10 calculates distance distribution and angle distribution which are two types of frequency distribution for a target model which is a polygon model (step S100). Then, the calculated frequency distribution of the target model is compared with the frequency distribution of the restriction image and the restriction model registered in the frequency distribution database 40 (step S200).

<2.4.3. Calculation processing of frequency distribution>
First, the process of calculating the model frequency distribution in step S100 will be described. FIG. 11 is a flowchart showing details of the calculation process of the model frequency distribution. Here, the polygon number N of the target model, and variables i (i = 0,..., N−1) for specifying the polygons, and the vertex of each polygon i is (Xu (i), Yu (i), Zu ( i) shall be defined as u indicates a vertex number, and in the present embodiment, since the polygon is a triangle, it takes three values of u = 0, 1 and 2. Thus, for example, Xu (i) is also written as X0 (i), X1 (i), X2 (i). Also, the normal vector of each polygon i is defined as (Xn (i), Yn (i), Zn (i)). The polygon specifying the vertex and the normal vector are required for output by the 3D printer which is a three-dimensional object forming apparatus, and the direction of the normal vector indicates the outside of the forming surface (the direction from the material to the air).

  The model frequency distribution calculating unit 10 first calculates the total area Ssum, which is the sum of the area S (i) of each polygon i constituting the target model and the area S (i) of N polygons, The calculation is performed by executing the processing that follows (step S110).

[Equation 11]
Vx = [Y1 (i) -Y0 (i)] [Z2 (i) -Z0 (i)]-[Z1 (i) -Z0 (i)] [Y2 (i) -Y0 (i)]
Vy = [Z1 (i) -Z0 (i)] [X2 (i) -X0 (i)]-[X1 (i) -X0 (i)] [Z2 (i) -Z0 (i)]
Vz = [X1 (i) -X0 (i)] [Y2 (i) -Y0 (i)]-[Y1 (i) -Y0 (i)] [X2 (i) -X0 (i)]
S (i) = [Vx (i) ² + Vy (i) ² + Vz (i) ² ] ^1/2
Ssum = _{i i = 0, N-1} S (i)

  In the above [Equation 11], the subscript “i = 0, N−1” of Σ indicates that the sum is obtained for the case where i takes all integers from 0 to N−1. Next, for each polygon constituting the target model, an average point to be an average of the vertices is calculated (step S120). Specifically, the average point (Xc (i), Yc (i), Zc (i)) of each polygon is calculated by executing the processing according to the following [Expression 12].

[Equation 12]
Xc (i) = {X0 (i) + X1 (i) + X2 (i)} / 3
Yc (i) = {Y0 (i) + Y1 (i) + Y2 (i)} / 3
Zc (i) = {Z0 (i) + Z1 (i) + Z2 (i)} / 3

  In the above [Equation 12], X0 (i), X1 (i) and X2 (i) indicate the case of vertex numbers u = 0, 1 and 2 respectively in Xu (i), and Y0 (i) and Y1 (Y) i) and Y2 (i) indicate cases of vertex numbers u = 0, 1 and 2 respectively in Yu (i), and Z0 (i), Z1 (i) and Z2 (i) respectively in Zu (i) , Vertex numbers u = 0, 1, 2 are shown. As shown in the above equation 12, the average point of each polygon is calculated as a point having the average coordinates of each vertex of the polygon.

  Next, the model frequency distribution calculating means 10 calculates a vector between each polygon average point (step S130). Specifically, by executing processing according to the following [Equation 13], the same target is obtained from the average point (Xc (i), Yc (i), Zc (i)) of a polygon i in the target model. An inter-polygon vector (Dx (d)) which is a vector to the average points (Xc (j), Yc (j), Zc (j)) of other polygons j (j = 0,..., N-1) in the model. i, j), Dy (i, j), Dz (i, j)) are calculated. Since the inter-polygon vector is a vector to another polygon other than itself, the case of i = j is excluded.

[Equation 13]
Dx (i, j) = Xc (j)-Xc (i)
Dy (i, j) = Yc (j)-Yc (i)
Dz (i, j) = Zc (j)-Zc (i)

  Next, the model frequency distribution calculating means 10 calculates the maximum distance between each polygon average point (step S140). Specifically, by executing processing according to the following [Equation 14], the average point (Xc (i), Yc (i), Zc (i)) of a polygon i in the target model is The distance D (i, j) to the average point (Xc (j), Yc (j), Zc (j)) of the polygon j is determined, and N (N-1) / 2 distances D (i, j) The largest among them is the maximum distance Dmax.

[Equation 14]
D (i, j) = [Dx (i, j) ² + Dy (i, j) ² + Dz (i, j) ² ] ^1/2
Dmax = MAX _{i, j} D (i, j)

  In the above [Equation 14], MAX is a possible range of subscript “i, j” i = 0,..., N−1, j = 0,. It shows that the maximum value is selected from D (i, j) in the case of j).

  Next, the model frequency distribution calculating means 10 calculates an angle between an inter-polygon vector which is a vector from a certain polygon to another polygon and a normal vector of the corresponding polygon (step S150). The inter-polygon vector and the corresponding polygon mean the polygon i and the polygon j on which the inter-polygon vector is calculated, but here, the polygon j which is one of the polygons is used. Specifically, an inter-polygon vector (Dx (i, j), Dy (i, j), Dz (i) between polygon i and polygon j is executed by executing processing according to the following [Equation 15]. , J) and the angle A (i, j) between normal vector (Xn (j), Yn (j), Zn (j)) of polygon j.

[Equation 15]
A (i, j) = [cos ^-1 [Dx (i, j) Xn (j) + Dy (i, j) Yn (j) + Dz (i, j) Zn (j)] / D (i, j) ] 180 / π

  In the above [Equation 15], in order to change the unit from radian to degree, the portion enclosed by [] is multiplied by 180 / π. Therefore, A (i, j) takes a value of 0 to 180. The angle between each polygon vector and the normal vector is an angle unique to each polygon.

  Next, the model frequency distribution calculating means 10 calculates the distance distribution between each polygon average point (step S160). The distance distribution calculated in the present embodiment is a distribution of area-weighted distances, where the number of elements is MD, and the frequency of each element md (md = 0,..., MD-1) is Hd (md) Represent.

  The model frequency distribution calculating unit 10 sets the initial value to Hd (md) = 0, and executes the processing according to the following [Equation 16] to obtain the frequency Hd (md) of the element md, i = 0. ,..., N−1, j = 0,..., N−1 (except in the case of i = j).

[Equation 16]
If Dmax · md / MD ≦ D (i, j) <Dmax · (md + 1) / MD, then
Hd (md) H Hd (md) + S (j)
Ssum S Ssum + S (j)

  [Equation 16] is obtained by multiplying (md + 1) / MD by a distance D (i, j) from the average point of polygon i to the average point of polygon j by not less than the maximum distance Dmax multiplied by md / MD. When smaller than the above value, it means adding the area S (j) of the polygon j to Hd (md).

  The distance distribution Hd (md) (md = 0,..., MD-1) can be obtained by executing the processing according to the above-mentioned [Equation 16] for the distance between N (N-1) / 2 polygons. It is calculated. Since the area S (j) is added to each element, not just the frequency, the distance distribution Hd (md) indicates an area weighted distance distribution. It is also possible to simply add 1 instead of the area S (j), in which case the distance distribution does not take into account the area of the polygon, and the case where the polygon configuration is rough or fine for the same polygon model And a noticeable difference occurs in the distance distribution. Therefore, in the present embodiment, the area of the polygon is added when calculating the distance distribution. Further, the unit is normalized to the area ratio [%] by further multiplying 100 / Ssum by Hd (md) calculated in the above [Equation 16] and replacing it with Hd (md). As a result, even polygon models having different sums of area of polygons, ie, different scales, are converted into the same distance distribution, and the shape can be determined regardless of the scale.

  Next, the model frequency distribution calculating means 10 calculates an angle distribution of an inter-polygon vector which is a vector between each polygon average point and a normal vector of one corresponding polygon (step S170). The angle distribution calculated in this embodiment is an area-weighted distribution of angles, where the number of elements is MA, and the frequency of each element ma (ma = 0,..., MA-1) is Ha (ma). Represent. The number of elements MA may be the same as the number of elements MD.

  The model frequency distribution calculating unit 10 sets the initial value to Ha (ma) = 0, and executes the process according to the following [Equation 17] to obtain the frequency Ha (ma) of the element ma as i = 0. ,..., N−1, j = 0,..., N−1 (except in the case of i = j).

[Equation 17]
If 180 · ma / MA ≦ A (i, j) <180 · (ma + 1) / MA, then
Ha (ma) Ha Ha (ma) + S (j)

  [Equation 17] is not less than a value obtained by multiplying ma by the ma / MA by 180 as the angle A (i, j) between the vectors between polygon i and polygon j and the normal vector of polygon j When smaller than the value obtained by multiplying ma + 1) / MA, it means adding the area S (j) of the polygon j to Ha (ma).

  The angle distribution Ha (ma) (ma = 0,..., MA-1) is calculated by executing the processing according to the above-mentioned [Equation 17] between N (N-1) / 2 polygons. Be done. The angle distribution Ha (ma) represents an area-weighted angle distribution, since the area S (j) is added to each element, not just the frequency. It is also possible to simply add 1 instead of the area S (j), but in that case, the angular distribution does not take the area of the polygon into consideration, and the case where the polygon configuration is rough or fine for the same polygon model And a noticeable difference occurs in the distance distribution. Therefore, in the present embodiment, the area of the polygon is added at the time of calculation of the angular distribution, and further, Ha (ma) is multiplied by 100 / Ssum to replace it with Ha (ma). It is As a result, even polygon models having different sums of area of polygons, ie, different scales, are converted into the same distance distribution, and the shape can be determined regardless of the scale.

  In this embodiment, when calculating the distance between each polygon and the angle unique to each polygon, the vector between the average points of each polygon is obtained as an inter-polygon vector. It is preferable that a vector connecting polygons be an average point of each vertex of the polygon, but a vector connecting predetermined points on the polygon other than the average point (for example, any vertex constituting the polygon) is an interpolygon It is also possible to make it a vector.

  The relationship between the distance and angle in the distance distribution and angle distribution obtained from the object model is shown in FIG. 7 (b). FIG. 7B shows one cross section of the polygon model (3D model). In FIG. 7 (b), the surrounding line segments indicate polygons. The distance between the average point having the average coordinates of the vertices of one polygon and the average point of the other polygons is calculated for each polygon. An angle unique to each polygon is an angle formed by an inter-polygon vector which is a vector from an average point of one polygon to an average point of another polygon and a normal vector of the polygon.

<2.4.4. Frequency distribution matching process>
Next, the frequency distribution matching process of step S200 will be described. FIG. 12 is a flowchart showing frequency distribution matching processing. First, the frequency distribution comparing unit 20 extracts the frequency distribution registered in the record re (re = 0,..., RE-1) from the RE records stored in the frequency distribution database 40. (Step S210). Extract the distance distribution Hdo (re, md) (md = 0, ..., MD-1) and the angular distribution Hao (re, ma) (ma = 0, ..., MA-1) as the frequency distribution .

  Next, the frequency distribution comparing unit 20 calculates a correlation coefficient between each element between the distance distribution calculated from the target model and the distance distribution of the restriction model extracted from the frequency distribution database 40 (step S220). The correlation coefficient is an index showing the strength of the association between two array elements. Here, it is used to determine the degree of similarity between the target model and the regulation image (or regulation model). First, the average value Ad of the distance distribution Hd (md) calculated from the object model and the distance distribution Hdo of the restriction image extracted from the frequency distribution database 40 are calculated by executing the processing according to the following [Expression 18]. Calculate the average value Ado (re) of re, md).

[Equation 18]
Ad = Σ _{md = 0, MD-1} Hd (md) / MD
Ado (re) = _{md = 0, MD-1} Hdo (re, md) / MD

  Subsequently, the standard deviation Sd of the distance distribution Hd (md) calculated from the object model and the distance distribution Hdo of the restriction image extracted from the frequency distribution database 40 are executed by executing the processing according to the following [Equation 19]. Calculate the standard deviation Sdo (re) of (re, md).

[Equation 19]
Sd = [Σ _{md = 0, MD-1} (Hd (md) −Ad) ² ] ^1/2
Sdo (re) = [Σ _{md = 0, MD-1} (Hdo (re, md)-Ado (re)) ² ] ^1/2

  Strictly speaking, the standard deviation needs to be further divided by a constant MD in {} of the above-mentioned [Equation 19]. However, here, since the purpose is to calculate the correlation coefficient, it is necessary to divide by the constant MD also in the term for calculating the covariance of the numerator in [Equation 20] described later, and the numerator and denominator overlap. The operation to divide by the constant MD is canceled (the denominator is divided twice by the square root of the constant MD).

  Next, by using the calculated average value and standard deviation, the processing according to the following [Equation 20] is executed to obtain the phase relationship between the distance distribution of the target model and the distance distribution of the restriction model corresponding to the record re. Calculate the number Dd (re).

[Equation 20]
Dd (re) = [Σ _{md = 0, MD-1} (Hd (md) −Ad) · (Hdo (re, md) −Ado (re))] / (Sdo (re) · Sd)

  In the above-mentioned [Equation 20], the subscript “md = 0, MD−1” of こ と indicates that the sum is obtained for the case where md takes all integers from 0 to MD−1. The correlation coefficient Dd (re) is obtained by dividing the covariance of the frequency distributions Hd (md) and Hdo (re, md) by their standard deviations. Performing the division of the square root of the denominator twice as in the above equation 20 generally results in high processing load. For this reason, in the field of image processing, a product requiring only numerator product-sum operation is called "correlation coefficient", and Dd (re) shown in the above equation 20 is called "normalized correlation coefficient". There is also a custom.

  Next, the frequency distribution comparing unit 20 compares the correlation coefficient Dd (re) of the distance distribution calculated in step S220 with the determination threshold (step S230). If the correlation coefficient Dd (re) is equal to or higher than the determination threshold value, it is determined as conforming, and if the correlation coefficient Dd (re) is smaller than the determination threshold value, it is determined as nonconforming. The determination threshold can be set arbitrarily as long as it is a positive value, but here, when the correlation coefficient value in the range of +5.0 (-1 to +1) is multiplied by 100 and expressed in% dimension As).

  When it is determined in step S230 that the frequency distribution is matched, the frequency distribution comparing unit 20 determines the phase between each element between the angle distribution calculated from the target model and the angle distribution of the regulation image extracted from the frequency distribution database 40. The relation number is calculated (step S240). First, the average value Aa of the angular distribution Ha (ma) calculated from the target model, and the angular distribution Hao of the restriction image extracted from the frequency distribution database 40 by executing the processing according to the following [Equation 21] Calculate the average value Aao (re) of re, ma).

[Equation 21]
Aa = Σ _{ma = 0, MA-1} Ha (ma) / MA
Aao (re) = Σ _{ma = 0, MA-1} Hao (re, ma) / MA

  Subsequently, the standard deviation Sa of the angular distribution Ha (ma) calculated from the object model and the angular distribution Hao of the restriction image extracted from the frequency distribution database 40 are calculated by executing the processing according to the following [Expression 22]. Calculate the standard deviation Sao (re) of (re, ma).

[Equation 22]
Sa = [Σ _{ma = 0, MA-1} (Ha (ma) -Aa) ² ] ^1/2
Sao (re) = [Σ _{ma = 0, MA-1} (Hao (re, ma) -Aao (re)) ² ] ^1/2

  Strictly speaking, the standard deviation needs to be further divided by the constant MA within {} of the above [Equation 22]. However, here, since the purpose is to calculate the correlation coefficient, it is necessary to divide by the constant MA also in the term for calculating the covariance of the numerator in [Equation 23] described later, and the numerator and denominator overlap. The operation to divide by the constant MA is canceled (the denominator is divided twice by the square root of the constant MA).

  Next, by using the calculated average value and standard deviation, the processing according to the following [Equation 23] is executed to obtain the phase relationship between the angular distribution of the target model and the angular distribution of the regulated image corresponding to the record re. Calculate the number Da (re).

[Equation 23]
Da (re) = [. SIGMA. _{Ma = 0, MA-1} (Ha (ma) -Aa). (Hao (re, ma) -Aao (re))] / (Sao (re). Sa)

  In the above [Equation 23], the suffix “ma = 0, MA-1” of Σ indicates that the sum is obtained for the case where ma takes all integers from 0 to MA-1. The correlation coefficient Da (re) is obtained by dividing the covariance of the frequency distributions Ha (ma) and Hao (re, ma) by the respective standard deviations. Performing the division of the square root of the denominator twice as in the operation of the above-mentioned [Equation 23] generally has a high processing load. For this reason, in the field of image processing, a product that requires only numerator product-sum operation is called "correlation coefficient", and Da (re) shown in the above equation 23 is called "normalized correlation coefficient". There is also a custom.

  Next, the frequency distribution comparing unit 20 compares the correlation coefficient Da (re) of the angle distribution calculated in step S240 with the determination threshold (step S250). If the correlation coefficient Da (re) is equal to or higher than the determination threshold, it is determined as conforming, and if the correlation coefficient Da (re) is smaller than the determination threshold, it is determined as nonconforming. The determination threshold can be set arbitrarily as long as it is a positive value, but here, when the correlation coefficient value in the range of +5.0 (-1 to +1) is multiplied by 100 and expressed in% dimension As). If it is determined in step S250 that the target model is suitable, the frequency distribution comparison unit 20 determines that the output should be restricted (the output is not suitable) because the target model and the restriction image of the record re will be compatible. Do.

  If it is determined in any of steps S230 and S250 that the matching is determined to be nonconforming, the frequency distribution matching unit 20 finishes the matching of the target model and the restriction image of the record re, and shifts to the matching of the next record re + 1 with the restriction image. . The process according to the flowchart of FIG. 12 is executed for all the records in the frequency distribution database 40, and if there is at least one matching regulatory image, the target model should be “regulated the output (output inappropriate The verification process of step S200 is terminated.

<2.4.4.1. Euclidean distance matching>
As mentioned above, although it is preferable to use a correlation coefficient for collation, Euclidean distance can also be used. In this case, the processes of steps S220 to S250 are as follows. The frequency distribution comparison unit 20 calculates the Euclidean distance between each element between the distance distribution calculated from the target model and the distance distributions of the regulation image extracted from the frequency distribution database 40 (step S220). Specifically, the Euclidean distance Dd2 (re) of the distance distribution of the target model and the distance distribution of the restricted image corresponding to the record re is calculated by executing the processing according to the following [Equation 24]. The Euclidean distance is a distance in Euclidean space given by the square of the absolute value of the difference between the frequencies of each element. The Euclidean distance Dd2 (re) is obtained as the distance between two points in the MD / K-dimensional Euclidean space.

[Equation 24]
_{Dd2 (re) = Σ i =} 0, MD / K-1 [{Σ k = 0, K-1 Hdo (re, i · K + k) / K-Σ k = 0, K-1 Hd (i · K + k) / K} ² · K / MD] ^1/2

  In the above [Equation 24], the suffix “i = 0, MD / K−1” of Σ indicates that the sum is to be obtained for the case where i takes all integers from 0 to MD / K−1, Σ The subscripts “k = 0, K−1” of indicate that the sum is to be calculated for the case where md takes all integers from 0 to MD-1. In the above [Equation 24], K is a positional deviation allowable range of distance. Although K can be set arbitrarily, for example, when MD = 360, K = 10. k is an integer having a value of k = 0, ..., K-1. The tolerance range K is used to eliminate the influence of a change in the distance between polygons due to a slight change in the shape of the target model (a change in definition of polygon configuration or a slight change in shape decoration) (If you have) By setting the tolerance range K, even if there is a slight change in the distance distribution of the target model, it is possible to accurately check the restriction image.

  Next, the frequency distribution comparing unit 20 compares the Euclidean distance Dd2 (re) of the distance distribution calculated in step S220 with the determination threshold (step S230). The determination by comparison with the determination threshold is the reverse of the case of the correlation coefficient. That is, when the Euclidean distance Dd2 (re) is smaller than the determination threshold value, it is determined as conforming, and when the Euclidean distance Dd2 (re) is greater than the determination threshold value, it is determined as nonconforming. The determination threshold may be arbitrarily set, but may be 0.5, for example.

  If it is determined in step S230 that the frequency distribution is matched, the frequency distribution comparing unit 20 determines the Euclidean distance between the angle distribution calculated from the target model and the angle distribution of the restriction image extracted from the frequency distribution database 40. Is calculated (step S240). Specifically, the Euclidean distance Da2 (re) of the angular distribution of the target model and the angular distribution of the restricted image corresponding to the record re is calculated by executing the processing according to the following [Equation 25]. The Euclidean distance Da2 (re) is determined as the distance between two points in MA-dimensional Euclidean space.

[Equation 25]
Da2 (re) =. SIGMA. _{Ma = 0, MA-1} [{Hao (re, ma) -Ha (ma)} ² / MA] ^1/2

In the above [Equation 25], the suffix “ma = 0, MA-1” of Σ corresponds to the case of [ ^1/2 ] of the following [...] ^{1/2 in} the case where ma takes all integers from 0 to MA-1. It shows that the sum is to be determined.

  Next, the frequency distribution comparing unit 20 compares the Euclidean distance Da2 (re) of the angle distribution calculated in step S240 with the determination threshold (step S250). The determination by comparison with the determination threshold is the reverse of the case of the correlation coefficient. That is, when the Euclidean distance Da2 (re) is smaller than the determination threshold value, it is determined as conforming, and when the Euclidean distance Da2 (re) is greater than the determination threshold value, it is determined as nonconforming. The determination threshold may be arbitrarily set, but may be 0.5, for example.

<3. Second embodiment>
Next, a second embodiment will be described. In the first embodiment, the frequency distribution of the restriction image is created in advance by the image frequency distribution calculating device, and registered in the frequency distribution database of the data output restricting device for three-dimensional object formation. In the second embodiment, the regulation image itself is registered in the regulation image database, and the above two types of frequency distributions are created on the spot from the regulation image in real time, and the verification is performed.

<3.1. Device configuration>
FIG. 13 is a functional block diagram showing the configuration of a three-dimensional object formation data output regulating device according to a second embodiment of the present invention. In FIG. 13, 10 is model frequency distribution calculating means, 20 is frequency distribution comparing means, 30 is target model storage means, 41 is a restriction image database, and 92 is image frequency distribution calculating means. 13, about what has a function equivalent to FIG. 3, FIG. 9, the same code | symbol is attached | subjected and detailed description is abbreviate | omitted.

  The model frequency distribution calculating means 10 performs processing of calculating a distance distribution which is a frequency distribution of distances and an angle distribution which is a frequency distribution of angles, with respect to the object model, as in the case shown in FIG. The image frequency distribution calculating means 92 performs processing for calculating a distance distribution which is a frequency distribution of distances and an angle distribution which is a frequency distribution of angles, as in the case shown in FIG. Does the frequency distribution collating means 20 collate each of the distance distribution and angle distribution calculated for the target model with the distance distribution and angle distribution calculated for the regulation image, similarly to those shown in FIG. It is determined whether or not the output is inappropriate or appropriate.

  The model frequency distribution calculating means 10, the image frequency distribution calculating means 92, and the frequency distribution comparing means 20 are realized by the CPU 1 shown in FIG. 8 executing a program stored in the storage device 3. The target model storage unit 30 is a storage unit that stores the target model, as in the case shown in FIG. The restriction image database 41 is a storage unit that stores restriction images, and is realized by the storage device 3.

  Each configuration means shown in FIG. 13 is actually realized by mounting a dedicated program in hardware such as a computer and its peripheral equipment as shown in FIG. That is, the computer executes the contents of each means in accordance with the dedicated program.

  The storage device 3 shown in FIG. 8 is implemented with a dedicated program for operating the CPU 1 to cause the computer to function as a three-dimensional object modeling data output regulating device. By executing this dedicated program, the CPU 1 realizes functions as the model frequency distribution calculating means 10, the image frequency distribution calculating means 92, and the frequency distribution comparing means 20. The storage device 3 not only functions as the target model storage means 30 and the restriction image database 41, but also stores various data necessary for processing as the three-dimensional object formation data output restriction device.

<3.2. Processing operation>
Next, the processing operation of the three-dimensional object formation data output regulating device shown in FIG. 13 will be described. FIG. 14 is a flowchart showing an outline of processing of the three-dimensional object formation data output regulating device shown in FIG. First, the model frequency distribution calculating means 10 calculates distance distribution and angle distribution which are two types of frequency distributions for the target model (step S100). Next, the image frequency distribution calculating means 92 calculates distance distribution and angle distribution which are two types of frequency distribution for the regulation image (step S300). Then, the calculated frequency distribution of the target model is compared with the frequency distribution of the restriction image (step S200). The calculation processing of the image frequency distribution in step S300 and the collation processing of the frequency distribution in step S200 are not appropriate in the collation processing if it is determined that the output should not be output (output unsuitable), or the collation with all restricted images End when it is determined that the output is appropriate. The processes in step S100 and step S200 are the same as those shown in FIG. Moreover, since the process in step S300 is the same as the process of calculating the frequency distribution shown in FIG. 4, the detailed description will be omitted.

<4. Usefulness of distance distribution and angle distribution>
Here, in the first and second embodiments, the usefulness of performing matching using two types of frequency distribution of distance distribution and angle distribution will be described using an example of three types of three-dimensional polygon models. Do. Here, for convenience of explanation, although it explains with the example of each polygon model, as it mentions later, in the two-dimensional picture (the sphere model of Figure 15 and Figure 16 is regarded as the circle model) each expressed by two-dimensional illustration. The same is true for the same. FIG. 15 is a view showing distance distribution and angle distribution obtained for each of two spherical model A and B. In FIG. FIG. 15 (a) shows a spherical model A, and FIGS. 15 (b) and 15 (c) show the distance distribution and angular distribution of the spherical model A in the form of a histogram. Fig. 15 (d) shows a sphere model B, and Figs. 15 (e) and 15 (f) show the distance distribution and the angle distribution of the sphere model B in the form of a histogram, respectively.

  A spherical model A shown in FIG. 15 (a) is a spherical model in which there is no internal cavity and the inside is full. If there is no cavity inside, as shown by the small arrow pointing to the right in FIG. 15A, all the normal vectors of all the polygons face outward. A sphere model B shown in FIG. 15 (d) is a sphere model having a cavity inside. When there is a cavity inside, as shown in FIG. 15 (d), the normal vector of the polygon is both facing outward (rightward in the figure) and facing inside (leftward in the figure). Large arrows extending from left to right in FIGS. 15A and 15D indicate an example of the distance between polygons.

  In the spherical model A, since the polygons are uniformly distributed at positions equidistant from the center of the spherical body, as shown in FIG. 15 (b), the distance distribution between the polygons of the spherical model A increases as the distance increases. Get higher. In the sphere model B, although there is a thickness at the outer peripheral portion, polygons are uniformly distributed at positions equidistant from the center of the sphere. Therefore, as shown in FIG. 15 (e), the distance distribution between polygons of the sphere model B becomes higher as the distance is larger. Therefore, the distance distribution of the sphere model B becomes a distribution similar to the distance distribution of the sphere model A as a whole, and if the thickness of the outer periphery of the sphere model B is smaller than the minimum resolution MD of the horizontal axis in FIG. The distribution completely matches the distance distribution of model A.

  Further, in the spherical model A, the normal vector is directed from the center of the sphere to the outside in order to make the inside of the sphere empty without being hollow. Therefore, the angle distribution of the angle between the vector between the polygons and the normal vector of the polygon is distributed only between 0 ° and 90 ° as shown in FIG. In the sphere model B, the normal vector of the outer polygon points outward from the center of the sphere, but the normal vector of the inner polygon points toward the center of the sphere to form a cavity. Therefore, the angular distribution is distributed not only between 0 ° and 90 ° but also between 90 ° and 180 ° as shown in FIG. 15 (f). In the example of FIG. 15, a polygon at the end point of vectors between polygons is used as one polygon using normal vectors. When polygons on the start point side of vectors between polygons are used as one polygon using normal vectors, an angular distribution different from that in FIGS. 15C and 15F is obtained. It is the same that the angular distribution of the sphere model B is clearly different.

  As shown in FIGS. 15 (b) and 15 (e), both distance distributions are higher as the distance is larger, and there is no big difference. Therefore, when only the distance distribution is used to check the spherical model A and the spherical model B, the possibility of being determined as matching (similar) increases. However, as shown in FIGS. 15 (c) and 15 (f), in the angular distribution, the distribution between 90 ° and 180 ° differs greatly. For this reason, it can be determined that the two are different by collating the sphere model A and the sphere model B using the angle distribution.

  The usefulness of performing matching using two types of frequency distribution, distance distribution and angular distribution, will be described using a second example. FIG. 16 is a diagram showing the distance distribution and the angle distribution obtained for each of the two sphere models B and C. 16 (a) shows a spherical model B, and FIGS. 16 (b) and 16 (c) show the distance distribution and angular distribution of the spherical model B, respectively. FIGS. 16 (a) to 16 (c) are the same as FIGS. 15 (d) to 15 (f). Fig. 16 (d) shows a spherical model C, and Figs. 16 (e) and 16 (f) show the distance distribution and angular distribution of the spherical model C, respectively.

  A spherical model C shown in FIG. 16D is a spherical model in which the outer peripheral portion is thicker than the spherical model B and the volume of the cavity is small. Since there is a cavity inside, as shown in FIG. 16 (d), the normal vector of the polygon is both outward and inward as in the case of the sphere model B. Arrows extending from left to right in FIGS. 16A and 16D respectively indicate examples of distances between polygons.

  In the sphere model B, the normal vector of the outer polygon points outward from the center of the sphere, but the normal vector of the inner polygon points toward the center of the sphere to form a cavity. Therefore, as shown in FIG. 16C, the angular distribution is distributed between 0 ° and 90 ° for a polygon whose normal vector points outward, and 90 for a polygon whose normal vector points inward. It is distributed between ° and 180 °. In the sphere model C, the normal vector of the outer polygon is directed outward from the center of the sphere, but the normal vector of the inner polygon is directed toward the center of the sphere to form a cavity. Since the outer polygon has a larger area than the inner polygon, the area-weighted angular distribution is 90 ° to 180 ° between 0 ° and 90 ° corresponding to the outer polygon as shown in FIG. 16 (f). There is a remarkably high portion around 45 ° compared to that between However, FIG. 16 (c) and FIG. 16 (f) have similar distributions.

  On the other hand, in the sphere model C, the outer peripheral portion is thicker than the sphere model B, and the polygons are uniformly distributed at two kinds of equidistant positions from the center of the sphere, as shown in FIG. The distance distribution between polygons of the model C becomes higher as the distance becomes larger, as in FIG. However, the distance distribution between polygons between the inner and outer peripheral portions becomes uniform, and has a characteristic significantly different from that of FIG.

  As described above, as shown in FIGS. 16 (c) and 16 (f), both of the angular distributions are distributed not only between 0 ° and 90 ° but also between 90 ° and 180 °. There is no difference. Therefore, when the matching between the spherical model B and the spherical model C is performed using only the angular distribution, the possibility of being determined as matching (similar) increases. However, as shown in FIGS. 16 (b) and 16 (e), in the distance distribution, the distance distribution between polygons is significantly different. Therefore, by comparing the sphere model B and the sphere model C using the distance distribution, it can be determined that they are different.

  As in the two examples shown in FIGS. 15 and 16, by using two types of frequency distribution of angle distribution and distance distribution, it is more accurately determined whether or not two polygon models are different. Is possible. Although the above has been described using the three-dimensional polygon model as an example, the same explanation holds for a two-dimensional image such as a regulation image. As illustrated two-dimensionally in FIGS. 15 (a) and 15 (d) and 16 (d), FIG. 15 (a) is a circular image with the inside filled, and FIG. 15 (d) is a border Assuming that the circular image shown in FIG. 16D is a thickly-rounded circular image and these are regarded as restriction images, two types of distance distribution and angular distribution are obtained by the method of the first and second embodiments described above. 15 (b), 15 (e), 15 (e) (f) and 16 (e) (f) (although it will be described later that the spherical The distance distribution and angle distribution of the model and the circular model can be approximated to a similar form to some extent by spherical correction when calculating from the circular model, but they do not completely match). When the spherical model A, the spherical model B and the spherical model C are opaque, the difference between the three spherical models can not be confirmed in the two-dimensional projection image, and the frequency distribution converted from the circular illustration image filled in with all of them Results similar to. However, the power distribution converted from each of the inside-filled circular illustration image, the outlined circular illustration image, and the thickly outlined circular illustration image is converted from the sphere model A, the sphere model B and the sphere model C. Three spherical models can be identified by matching each with the frequency distribution and matching with the three circular illustration images.

<Output data to 5.3D printer>
When it is determined by the frequency distribution comparing unit 20 that “the output should not be restricted (the output is appropriate)” in the step S200, the target model is output to the 3D printer 7 which is a three-dimensional object forming apparatus. On the other hand, when it is determined by the frequency distribution comparing unit 20 that “the output should be regulated (output unsuitable)”, the target model is not output to the 3D printer 7 which is a three-dimensional object forming apparatus. If it takes time to determine whether the output is appropriate or not appropriate, the target model is output to the 3D printer 7, and whether the output is appropriate or improper in parallel with the output processing (three-dimensional object formation processing) of the 3D printer 7 The output stop instruction may be output to the 3D printer 7 if the output is not suitable. At this time, from the user's point of view, it is only possible to confirm that the three-dimensional object formation processing in the 3D printer is started by performing one instruction to output the target model, and in parallel whether the output is appropriate or not appropriate It does not notice that the execution of the process for is started.

<6. Polygon reduction processing>
Prior to the calculation processing of the model frequency distribution in step S100 shown in FIGS. 10 and 14, polygon reduction processing for reducing the number of polygons of the target model may be performed. By performing the reduction of the less important polygon in advance, it is possible to quickly determine whether the output is appropriate or not, by permitting the difference in the minute shape of the polygon model. The polygon reduction processing prior to the calculation processing of the model frequency distribution will be described below. The polygon reduction processing is performed by polygon reduction means realized by the CPU 1 shown in FIG. 8 executing a program stored in the storage device 3.

  FIG. 17 is a diagram showing the basic principle of polygon deletion by the polygon reduction means. A polygon set is actually a polygon group having three-dimensional values in space, but in the example of FIG. 17, a polygon group having two-dimensional values is shown for convenience of explanation. In the example of FIG. 17A, a polygon group consisting of 18 polygons before deletion is shown.

  FIG. 18 is a flowchart showing details of polygon reduction processing by the polygon reduction means. First, the polygon reduction means calculates the area and circumferential length of each polygon for all polygons constituting the target model (step S11). Next, the polygon reduction means selects a polygon having a minimum value obtained by multiplying the calculated area by the circumferential length as a deletion target polygon (step S12). In the example of FIG. 17, as shown in FIG. 17B, a polygon EJK formed of three vertices E, J, and K is selected as a deletion target polygon.

  Subsequently, the polygon reduction means selects the shortest one of the three sides of the deletion target polygon as the deletion target side, and calculates the midpoint of the selected deletion target side (step S13). In the example of FIG. 17, as shown in FIG. 17C, the side JK of the deletion target polygon EJK is selected as the deletion target side. Furthermore, the midpoint O of the side JK is calculated.

  Next, the polygon reduction means deletes the deletion target polygon (step S14). This will reduce the number of polygons by one. Next, the polygon reduction means sequentially selects the deletion vertex sharing polygons that share the vertex with the deletion target side of the deletion target polygon (step S15). When a deletion vertex sharing polygon sharing two deletions with the deletion target side of the deletion target polygon (that is, sharing the side) is selected, the polygon reduction means deletes the deletion vertex sharing polygon (step S16). . In the example of FIG. 17, the polygon JKN formed of three vertices J, K, N is deleted. This further reduces the number of polygons by one.

  When the deletion vertex sharing polygon sharing one vertex with the deletion target side of the deletion target polygon is selected, the polygon reduction means corrects the sharing vertex to the middle point calculated in step S13, and the area and circumference The length is recalculated (step S17). In the example of FIG. 17, as can be seen by comparing FIG. 17C and FIG. 17D, polygon EHJ → polygon EHO, polygon EFK → polygon EFO, polygon HJM → polygon HOM, polygon JMN → polygon OMN, polygon FKN → Change to polygon FON. As a result, in the example of FIG. 17, the polygons which were 18 at the time of FIG. 17 (a) are reduced to 16 at the time of FIG. 17 (d).

  When the processing of step S15 to step S17 is completed, the polygon reduction means determines whether or not the number of all polygons after reduction is less than or equal to the set target value (step S18). Any desired value can be set in advance as the target value. As a result of the determination, if the total number of polygons is not equal to or less than the target value, the process returns to step S12, and the polygon reduction means determines the polygon having the smallest value obtained by multiplying the area and the circumference among the remaining polygons. Is selected as the polygon to be deleted. If it is determined in step S18 that the total number of polygons is equal to or less than the target value, the polygon reduction process is ended. By executing the processing according to the flowchart of FIG. 18, the number of polygons is reduced so as to be equal to or less than the set target value.

<7. Target model division processing>
Prior to the calculation processing of the model frequency distribution shown in FIGS. 10 and 14, target model division processing may be performed to divide the target model. By dividing the target model in advance, it is possible to appropriately determine whether or not the output is appropriate even when the component configurations are different between the regulation model and the target model. The target model division processing prior to the calculation processing of the model frequency distribution will be described below. The target model division processing is performed by target model division means realized by the CPU 1 shown in FIG. 1 executing a program stored in the storage device 3.

  FIG. 19 is a flowchart showing details of target model division processing by the target model division means. The target model division means first performs initial setting (step S21). Specifically, the number P of polygons constituting the polygon set of the read target model, variables p (p = 0,..., P−1) specifying polygons, search status S (p), group attribute G ( p), a variable g specifying a group, and a variable s indicating a search status are set. The search status S (p) takes an integer value of 0 or more. S (p) = 0 is a state in which the polygon p is not classified into a group, S (p)> 0 is a state in which the polygon p is classified into a group attribute G (p) in step S22 executed for the S (p) time Indicates Further, in step S21, S (p) = G (p) = 0, g = 1, and s = 1 are initialized for all p of p = 0,.

  Next, one polygon p for which S (p) = G (p) = 0 is extracted from all the polygons (step S22). In the initial state, since S (p) = G (p) = 0 in all, polygon extraction of p = 0 is always performed at the first time. In the case where there is no polygon p where S (p) = G (p) = 0 in step S22, that is, S (p)> 0 for all p = 0,. , G-1 groups, and the target model division processing is ended as the target model can be divided into g-1 partial target models. In step S22, since there is no state where S (p) <0, S (p) is either “0” or a positive value. When a polygon p with S (p) = G (p) = 0 can be extracted, the search status S (p) = s and the group attribute G (p) = g corresponding to the extracted polygon p are set. Further, the update counter c is set to 0, which is a variable for counting updates.

  In step S22, one polygon p for which S (p) = G (p) = 0 is extracted, and the search status S (p) = s and group attribute G (p) = g corresponding to the extracted polygon p are obtained. If set, one polygon q for which S (q) = s is extracted from all the polygons (step S23). q is a variable for specifying a polygon, and, like polygon p, takes an integer value of q = 0,..., P−1. When there are a plurality of polygons q where S (q) = s, for example, one having the smallest value of q is extracted.

  When one polygon q with S (q) = s is extracted in step S23, three adjacent polygons r1, r2, and r3 sharing an edge with the extracted polygon q are searched from all the polygons, Search status S (r) = s + 1, group attribute G (r) for polygon r satisfying S (r) = G (r) = 0 among the three adjacent polygons r1, r2 and r3 searched = G is set, and the value of c is updated to c c c + 1 (step S24). r is a variable for specifying any one of r1, r2 and r3 and, like polygons p and q, r takes any integer value in r = 0,..., P-1. “Share the sides of the polygon q and the adjacent polygon r” means that two vertices of the polygon q and two vertices of the polygon r are identical. That is, any one side of the adjacent polygon r is identical to any one side of the polygon q.

  In the present embodiment, since the polygon is a triangle, there are always three adjacent polygons sharing one side with respect to the three sides forming the polygon. This condition is not a problem even if this condition is not satisfied when displaying a screen by computer graphics, but when outputting with a 3D printer, this condition is a necessary requirement because it can not physically form a shaped object (if it is satisfied If not, an error warning will be issued, and it is common for printer output not to be executed.) Of the three adjacent polygons extracted, for polygon r satisfying S (r) = G (r) = 0, search status S (r) = s + 1, group attribute G (r) = g, c ← Set to c + 1.

  Then, the process returns to step S23, and when there are other polygons q where S (q) = s from all the polygons, one more is extracted and step S24 is similarly executed. That is, by repeating the processes of steps S23 and S24, the process of step S24 is executed for all polygons in the target model for which S (q) = s. In step S23, if there is no polygon q with S (q) = s, that is, if three adjacent polygons are searched for all the polygons q with S (q) = s, the variable s indicating the search status is 1 Increment (s ← s + 1) (step S25).

  Next, it is determined whether or not the update counter c = 0 (step S26). As a result of the determination, if c is not 0, at least one polygon r satisfying S (r) = G (r) = 0 among the three polygons adjacent to the polygon q in step S24 Means that there is no other polygon p whose group attribute to be included in the same group g is undefined, or the process returns to step S22, and from all the polygons, S (p) = G (p) = 0 The polygon p is further searched, and if one exists, one is extracted, and the search status S (p) = s, the group attribute G (p) = g, and the update counter c = 0 corresponding to the extracted polygon p are set. Do. Then, as described above, the process proceeds to the processing loop of step S23 and step S24, and the process of searching for three adjacent polygons for all polygons q for which S (q) = s is repeated.

  In this manner, the processing of step S22 to step S25 is repeatedly executed, and as a result of the determination in step S26, when the update counter c is 0, that is, all three polygons adjacent to polygon q in step S24 are If there is no group attribute that is already set to the same group g and that should satisfy S (r) = G (r) = 0 to be included in group g, and there is no polygon r at all, classification into group g is considered to be finished. In order to shift to classification into the next group g + 1, the variable g specifying the group is incremented by one (g ← g + 1) (step S27). Then, the process returns to step S22, and classification into a new group g is similarly performed. If, in step S22, a polygon p whose group attribute for which S (p) = G (p) = 0 is not found is not found, the group attributes of all P polygons are set to any value. Therefore, it can be considered that the classification process has ended. At this time, since the group g updated in step S27 is not set as the group attribute of any polygon, all P polygons are classified into g-1 groups.

  By executing the process according to the flowchart of FIG. 19, all P polygons are classified into groups for each polygon set sharing and connecting two vertices. This classified group is a partial target model. In the example of FIG. 19, each polygon of the target model belongs to any of the g-1 partial target models. As a result, the target model is divided into g-1 partial target models.

  The polygon reduction process by the polygon reduction means and the target model division process by the target model division means, which are performed prior to the model frequency distribution calculation process of step S100, may be performed in either order. If possible, it is more efficient to perform target model division processing after performing polygon reduction processing. Further, only one of the polygon reduction processing and the target model division processing may be performed.

<8. Calculation and registration of frequency distribution of regulatory image and regulatory model>
As a modification, the distance distribution and the angle distribution calculated with respect to the regulation image and the regulation model are stored in a place other than the three-dimensional object formation data output control device provided with the frequency distribution comparison means 20 It may be transmitted and registered in the output restricting device via the network.

  FIG. 20 is a hardware configuration diagram of a three-dimensional object formation system including a three-dimensional object formation data output regulating device according to a modification. In the three-dimensional object formation system shown in FIG. 20, the three-dimensional object formation data output control device 101 communicates with a public network such as the Internet in the configuration of the three-dimensional object formation data output restriction device 100 shown in FIG. The network communication unit 8 is provided. The image frequency distribution calculation device 301 can be realized by a general-purpose computer, and as shown in FIG. 20, a CPU (Central Processing Unit) 1a, a RAM (Random Access Memory) 2a which is a main memory of the computer, and the CPU 1a. A large capacity storage device 3a such as a hard disk and flash memory for storing programs executed by the program and data, a key input I / F (interface) 4a such as a keyboard and a mouse, an external device such as a data storage medium and data It includes a data input / output I / F (interface) 5a for communication, a display unit 6a which is a display device such as a liquid crystal display, and a network communication unit 8a for communicating with a public network such as the Internet. Is connected. The network communication unit 8 of the three-dimensional object modeling data output restricting device 101 and the network communication unit 8a of the image frequency distribution calculating device 301 communicate with each other, and the image frequency distribution calculating device 301 regulates the three-dimensional object modeling data output restriction device 101 It is possible to transmit two frequency distributions calculated for an image.

  In FIG. 20, the three-dimensional object formation data output control device 101 and the 3D printer 7 are shown in a separated form, but the three-dimensional object formation data output restriction device 101 is used for most 3D printer products currently on the market. Constituent elements: CPU 1, RAM 2, storage device 3, key input I / F 4 (not keyboard / mouse for general-purpose computer but several buttons of ten keys level), data input / output I / F 5, display unit 6 (several lines A small liquid crystal panel capable of displaying the characters, a touch panel superimposed, and often serving as a key input I / F 4), and a network communication unit 8 (wireless LAN function) are also provided in duplicate although being small scale. Therefore, the 3D printer 7 itself can receive data for three-dimensional object formation directly via an external storage medium or the Internet, and it is possible to operate a three-dimensional object by itself (in particular, for a consumer 3D printer) A lot). That is, in many cases, the three-dimensional object formation data output restricting device 101 and the 3D printer 7 shown in FIG. 20 are accommodated in one case and distributed as a product called "3D printer".

  In the image frequency distribution calculating device 301, an image frequency distribution calculating unit and a frequency distribution transmitting unit are realized by the CPU 1a executing a program stored in the storage device 3a. The image frequency distribution calculating means realized by the image frequency distribution calculating device 301 has the same function as the image frequency distribution calculating means 92 in the image frequency distribution calculating device 300 and the data output regulating device 100 for three-dimensional object modeling. The distance distribution and angle distribution are calculated as two types of frequency distributions. The frequency distribution transmission means transmits distance distribution and angle distribution, which are two types of frequency distributions calculated for the regulation image, to the data output regulation device for three-dimensional object formation 101. In the three-dimensional object formation data output regulating device 101, when the network communication unit 8 receives the frequency distribution from the image frequency distribution calculating device 301, the CPU 1 executes a predetermined program to function as frequency distribution registration means, and the received frequency The distribution is registered in the frequency distribution database 40 realized by the storage device 3.

<9. Cloud-type three-dimensional object formation system>
The present invention is also applicable to a cloud-type three-dimensional object forming system. FIG. 21 is a hardware configuration diagram of a cloud-type three-dimensional object formation system. In the three-dimensional object formation system shown in FIG. 21, the output control terminal 201 and the processing server 202 realize a three-dimensional object formation data output regulation device. In the three-dimensional object formation system shown in FIG. 21, the output control terminal 201 has the same hardware configuration as the computer shown as the three-dimensional object formation data output restricting device 101 in FIG. That is, the output control terminal 201 includes a CPU 11, a RAM 12, a storage device 13, a key input I / F 14, a data input / output I / F 15, a display unit 16, and a network communication unit 18, which are mutually connected via a bus. .

  The processing server 202 can be realized by incorporating a dedicated program into a general-purpose computer. As shown in FIG. 21, the CPU 11a, the RAM 12a, the storage device 13a, the key input I / F 14a, the data input / output I / F 15a, the display unit 16a, and the network communication unit 18a are mutually connected via a bus There is. The network communication unit 18 of the output control terminal 201 and the network communication unit 18a of the processing server 202 communicate with each other to transmit output suitability data based on the determination of output suitability from the processing server 202 to the output control terminal 201. It is possible. In FIG. 21, although the output control terminal 201 and the 3D printer 7 are shown in a separated form, as in the example of FIG. 20, the CPU 11, the RAM 12, and the components of the output control terminal 201 in the 3D printer product. The storage device 13, the key input I / F 14, the data input / output I / F 15, the display unit 16, and the network communication unit 18 may be redundantly provided.

  FIG. 22 is a functional block diagram of a cloud-type three-dimensional object formation system. The processing server 202 constituting the cloud-type three-dimensional object formation system includes an object model reception means 50 and an output suitability data transmission means 60 in addition to each means of the three-dimensional object formation data output regulating device shown in FIG. It is a structure. The target model storage unit 31 stores not the target model itself but two types of frequency distributions calculated for the target model. Further, the model frequency distribution calculating means 10 is configured not to be the processing server 202 but to be provided with the output control terminal 201.

  About what has a function equivalent to the data output regulation device for solid thing modeling shown in Drawing 9, the same numerals are attached and explanation is omitted. The target model receiving unit 50 is a unit that receives the frequency distribution of the target model transmitted from the output control terminal 201 and registers the frequency distribution in the target model storage unit 31. While the CPU 11a executes a predetermined program, the network communication unit It is realized by controlling 18a. The output appropriateness data transmission means 60 is a means for transmitting the output appropriateness data to the output control terminal 201 based on "whether or not the output should be restricted" determined by the frequency distribution comparison means 20, and the CPU 11a This is realized by executing a predetermined program and controlling the network communication unit 18a.

  The processing server 202 is connected to a network such as the Internet and can be accessed from a large number of output control terminals. “Cloud type” of the 3D object modeling system of cloud type is not the output side that outputs 3D objects with the 3D printer, but it is judged whether it should be regulated in the remote computer through the network from the output side Means In a conventional server-type computer, when the access of many users concentrates, the response becomes slow and often bothers users, but in the cloud type, the physical configuration of the computer is dynamically changed by virtualization technology It has the feature that stable responsiveness can always be maintained. The processing server 202 is generally physically implemented by a plurality of computers.

  The processing operation of the cloud-type three-dimensional object formation system shown in FIGS. 21 and 22 will be described. In the output control terminal 201, when the user designates a target model to be output through the key input I / F 14, the CPU 11 extracts the designated target model stored in the storage device 13 and specifies the target model. Model ID which is identification information to be Then, the CPU 11 performs processing on the target model to which the model ID is given, in accordance with the flowchart shown in FIG. 11, to generate a frequency distribution composed of distance distribution and angle distribution, according to the process shown in FIG. . Furthermore, the CPU 11 transmits the generated frequency distribution via the network communication unit 18 to an address such as a URL previously set in the storage device 13. Not only transmission time is greatly shortened by adopting the method of transmitting frequency distribution where the amount of information is significantly smaller than polygon data, the polygon data which is a work is not transmitted to the server side. There is no copy left, so copyright infringement can be avoided. In parallel, the CPU 11 transmits the target model to which the model ID is assigned to the data processing unit 7a of the 3D printer 7 via the data input / output I / F 15. The target model received from the output control terminal 201 is held in the printer queue in the data processing unit 7a, and becomes a standby state as an output job. At this time, it is possible to execute only processing of converting polygon format data, which is pre-processing in 3D printer output, into layered format data, and to set a standby state at the stage of being converted to layered format data. . Since this data processing load is also relatively high, if the output suitability determination is completed during this time, it is possible to prevent the user from feeling an extra waiting time.

  In the processing server 202, when the target model receiving unit 50 receives the frequency distribution of the target model transmitted from the output control terminal 201, the target model receiving unit 50 stores the frequency distribution in the target model storage unit 31. Here, according to the flowchart shown in FIG. 10, the frequency distribution comparing means 20 performs processing, and determines the output suitability of the target model corresponding to the received frequency distribution. The output suitability data that is the result of the output suitability is passed from the frequency distribution comparison unit 20 to the output suitability data transmission unit 60. Then, the output appropriateness data transmission unit 60 transmits the output appropriateness data to which the model ID is added, to the output control terminal 201 that is the transmission source (access source) of the frequency distribution.

  In the output control terminal 201, when the network communication unit 18 receives the output suitability data from the processing server 202, the CPU 11 transmits the received output suitability data to the data processing unit of the 3D printer 7 via the data input / output I / F 15. Send to 7a. The data processing unit 7a refers to the output job in the printer queue with the model ID assigned to the received output suitability data. Then, when the output suitability data is “suitable (output proper)”, the data processing unit 7a changes the output job from the standby state to the output state, and outputs the target model to the output unit 7b. This shows the case where output permission is made. If the output suitability data is “not (output unsuitable)”, the data processing unit 7a discards the output job. That is, they are deleted from the printer queue.

<10. Frequency distribution calculation example>
FIG. 23 and FIG. 24 show frequency distribution calculation cases by the data output regulation device for three-dimensional object formation according to the above embodiment for a sphere and a circle. In both FIG. 23 and FIG. 24, the polygon model or image is shown at the left end, and the frequency distribution is shown at the right side. In the frequency distribution, the upper part shows the distance distribution (Distance), and the lower part shows the angle distribution (Angle).

  FIG. 23A shows a polygon model (5040 polygon) of a sphere a (in fact, a polyhedron). For a polygon model of a sphere as shown in FIG. 23 (a), as shown in FIG. 23 (b), the distance distribution becomes higher as the distance between polygons becomes larger, and only between 0 ° and 90 °. An existing angular distribution is obtained. From the obtained distance distribution and angle distribution, it can be seen that the polygon model of FIG. 23 (a) is for outputting a filled sphere.

  FIG. 23C shows a two-dimensional binary image of the circle b. For a two-dimensional binary image as shown in FIG. 23 (c), a distance distribution and an angle distribution as shown in FIGS. 23 (d) and 23 (e) can be obtained. FIG. 23D shows the result when the correction of the edge direction vector is not performed in step S540 of FIG. 4, and FIG. 23E shows the result when the correction of the edge direction vector is performed. In FIG. 23E, the tone correction in step S520 is also performed, and in this case, the set minimum value SL = 72. The distribution over 90 degrees of the lower graph of FIG. 23 (e) is smaller than that of FIG. 23 (d), and it can be seen that the form is similar to FIG. 23 (b).

  FIG. 24A shows a two-dimensional shading image of the sphere c. For a two-dimensional shading image as shown in FIG. 24A, distance distribution and angle distribution as shown in FIGS. 24B and 24C are obtained. FIG. 24B shows the result in the case where the correction of the edge direction vector is not performed in step S 540 in FIG. 4, and FIG. 24C shows the result in the case where the correction of the edge direction vector is performed. In FIG. 24C, the tone correction in step S520 is also performed, and the set minimum value SL in that case is 72. The peak of the angular distribution in the lower graph of FIG. 24 (c) is clearer than that of FIG. 24 (b), and it can be seen that the form is similar to that of FIG.

  FIG. 25 and FIG. 26 show frequency distribution calculation cases by the data output restricting device for three-dimensional object formation according to the above embodiment with respect to Daruma. In both FIG. 25 and FIG. 26, the polygon model or image is shown at the left end and the frequency distribution is shown at the right side. In the frequency distribution, the upper part shows the distance distribution (Distance), and the lower part shows the angle distribution (Angle).

  FIG. 25 (a) is a diagram showing a polygon model (15944 polygons) of the dazzling d. As shown in FIG. 25 (b), a polygon model in which the basic shape as shown in FIG. 25 (a) is close to a sphere and irregularities are added to the face part shows a tendency similar to that of the sphere in FIG. 23 (b). However, it is possible to obtain distance distribution and angle distribution with more variation than spheres.

  FIG. 25C is a diagram showing a two-dimensional ternary image of Daruma e. Although it looks like a binary image for the convenience of illustration, the inside of the silhouette of Daruma is divided into two values, and it is a ternary image as a whole. For a two-dimensional ternary image as shown in FIG. 25C, a distance distribution and an angle distribution as shown in FIGS. 25D and 25E can be obtained. FIG. 25 (d) shows the result when the correction of the edge direction vector is not performed in step S540 of FIG. 4, and FIG. 25 (e) shows the result when the correction of the edge direction vector is performed. In FIG. 25E, the tone correction in step S520 is also performed, and in this case, the set minimum value SL is 72. The peak of the angular distribution of the lower graph of FIG. 25 (e) is clearer than that of FIG. 25 (d), and it can be seen that the form is similar to that of FIG. 25 (b). Further, it can be seen that the peak of the distance distribution in the upper graph of FIG. 25 (e) is shifted to the right as compared to FIG. 25 (d), and is in a form similar to FIG. 25 (b). This is an effect due to the addition of tone correction in FIG.

  FIG. 26A is a view showing a two-dimensional shading image of the final polishing f. For a two-dimensional shading image as shown in FIG. 26A, distance distribution and angle distribution as shown in FIGS. 26B and 26C can be obtained. FIG. 26 (b) shows the result when the correction of the edge direction vector is not performed in step S540 of FIG. 4, and FIG. 26 (c) shows the result when the correction of the edge direction vector is performed. In FIG. 26C, the tone correction in step S520 is also performed, and in this case, the set minimum value SL is 72. The peak of the angular distribution of the lower graph of FIG. 26 (c) is clearer than that of FIG. 26 (b), and it can be seen that the form is similar to that of FIG. 25 (b). In addition, it can be seen that the peak of the distance distribution in the upper graph of FIG. 26C is shifted to the right as compared to FIG. 26B, and is in a form similar to FIG. 25B. This is an effect due to the addition of the tone correction in FIG.

  When the distribution in FIG. 25 (d) and the distribution in FIG. 25 (e) are compared, when the edge direction vector is corrected, the correlation with the distance distribution as well as the angle distribution in FIG. 25 (b) is not limited. It turns out that it will increase. Also, comparing the distribution of FIG. 26 (b) and the distribution of FIG. 26 (c), when the edge direction vector is corrected, not only the angle distribution of the polishing d of FIG. It can be seen that the correlation also increases. These are the effects of the addition of tone correction in addition to the correction of the edge direction vector.

<11. Modified example etc>
As mentioned above, although preferred embodiment of this invention was described, this invention is not limited to the said embodiment, A various deformation | transformation is possible. For example, in the above embodiment, the image to be processed is in the raster format represented by monochrome 256 gradation pixels, but may be an illustration image in vector format, in which case an edge line is given. The processing of edge point extraction by calculation can be simplified. Further, although the polygon to be processed is a triangle, it may be a polygon having a quadrangle or more.

  Further, in the above embodiment, the frequency distribution of the regulation image and the frequency distribution of the regulation model are registered in the frequency distribution database, and are compared with the frequency distribution created from the target model. It is not necessary to register and check. In addition, although it is recommended to register the restriction image itself in the frequency distribution database, it may not be registered if the permission is not obtained. In addition, although it is recommended not to register the regulatory model itself, it may be registered if permission is obtained.

1, 1a, 11, 11a, 81: CPU (Central Processing Unit)
2, 2a, 12, 12a, 82 ... RAM (Random Access Memory)
3, 3a, 13, 13a, 83 ... storage device 4, 4a, 14, 14a, 84 ... key input I / F
5, 5a, 15, 15a, 85 ... data input / output I / F
6, 6a, 16, 16a, 86 ... display unit 7 ... 3D printer (three-dimensional object formation device)
7a: data processing unit 7b: output unit 8, 8a, 18, 18a: network communication unit 10: model frequency distribution calculating unit 20: frequency distribution comparing unit 30, 31: target Model storage means 40 ... frequency distribution database 41 ... restriction image database 50 ... target model reception means 60 ... output suitability data transmission means 91 ... restriction image storage means 92 ... image frequency distribution calculation Means 93 ··· Image intensity distribution storage unit 100, 101 ··· Data output regulation device for solid object modeling 201 ··· Terminal for output control 202 ··· Processing server 300, 301 ··· Image intensity distribution calculation device

Claims (23)

It is an apparatus that determines whether or not to restrict when outputting a polygon model expressed as a set of polygons to a three-dimensional object formation apparatus as data for three-dimensional object formation,
A database in which a distance distribution and an angle distribution representing the features of a regulated image, which is an image whose output should be regulated, is registered,
A model that calculates a distance distribution that is a frequency distribution of distances between polygons in the target model and an angle distribution that is a frequency distribution of angles unique to the polygons for the target model that is a polygon model to be output Frequency distribution calculation means;
The distance distribution and the angle distribution of the target model calculated by the model frequency distribution calculating means are respectively compared with the distance distribution and the angle distribution of the regulation image registered in the database, and it is determined whether or not the output should be regulated. And frequency distribution matching means,
With regard to the distance distribution and the angle distribution of the restriction image registered in the database, the pixels constituting the edge of the object are extracted as edge points from the restriction image, and the distance between each edge point in the restriction image is It is obtained by an image frequency distribution calculating apparatus having image frequency distribution calculating means for calculating distance distribution which is frequency distribution and angle distribution which is frequency distribution of angles unique to each edge point,
The image frequency distribution calculating means corrects the direction to specify the edge of the edge point with reference to the pixels around the edge point, and then specifies the edge after correction and each edge in the restricted image A data output control device for three-dimensional object formation, wherein an angle formed by directions connecting points is calculated as an inherent angle between the edge points. It is an apparatus that determines whether or not to restrict when outputting a polygon model expressed as a set of polygons to a three-dimensional object formation apparatus as data for three-dimensional object formation,
For a regulated image which is an image whose output is to be regulated, pixels constituting the edge of the object are extracted as edge points, and a distance distribution which is a frequency distribution of distances between the respective edge points in the regulated image, and Image frequency distribution calculating means for calculating an angle distribution which is a frequency distribution of angles unique to edge points;
A model that calculates a distance distribution that is a frequency distribution of distances between polygons in the target model and an angle distribution that is a frequency distribution of angles unique to the polygons for the target model that is a polygon model to be output Frequency distribution calculation means;
Whether to restrict the output by comparing the distance distribution and the angle distribution of the restriction image calculated by the image frequency distribution calculating means with the distance distribution and the angle distribution of the target model calculated by the model frequency distribution calculating means Frequency distribution matching means for determining
Equipped with
The image frequency distribution calculating means corrects the direction to specify the edge of the edge point with reference to the pixels around the edge point, and then specifies the edge after correction and each edge in the restricted image A data output control device for three-dimensional object formation, wherein an angle formed by directions connecting points is calculated as an inherent angle between the edge points.   The image frequency distribution calculation means applies a plurality of filter matrices defined in advance to the value of each pixel of the restricted image, and the maximum value of the plurality of intensities calculated by each filter matrix for each pixel A direction unique to the filter matrix giving the maximum value is calculated as the direction for specifying the edge, a pixel whose edge intensity exceeds a predetermined value is extracted as the edge point, and an edge with the restriction image is calculated. A vector from a point to another edge point is obtained as an inter-edge point vector indicating a direction connecting each edge point in the restricted image, and a direction for identifying an edge of one edge point corresponding to the edge point vector When calculating the angle between the edge points as the unique angle between the edge points, the correction of the direction for specifying the edge of the edge points is performed for the edge point of interest The data for three-dimensional object formation according to claim 1 or 2, characterized in that averaging is performed while weighting the edge strength of the surrounding edge point and the direction for specifying the edge at the edge point of the surrounding area. Output regulation device.   The filter matrix of 16 directions 3 × 3 pixels defined in advance is applied as the filter matrix, and calculation is performed to obtain the intensity in each direction using values set in advance for each direction. The data output regulation device for solid thing modeling according to claim 3.   5. The three-dimensional object according to claim 4, wherein the image frequency distribution calculating means corrects the intensities in the respective directions obtained using the intensity correction amount set in advance for each of the 16 directions. Data output regulation device for object modeling.   The image frequency distribution calculating means subtracts the predetermined setting minimum value from the edge strength calculated for each pixel, multiplies the value obtained by subtraction, and multiplies the number of gradations of the edge strength, The edge strength is corrected by dividing a difference from a set minimum value, and a pixel having a positive value of the corrected edge strength is extracted as the edge point. The data output regulation device for solid thing modeling given in any 1 paragraph.   In calculating the distance distribution, the image frequency distribution calculating means prepares a one-dimensional array composed of a predetermined number of elements, and equally divides the range of the maximum value of the calculated distances into the predetermined number. The distance distribution is calculated by assigning each of the distances to any of the elements based on the distance and counting the number corresponding to the elements. The data output regulation device for solid thing modeling according to any one of claims 6.   In calculating the angle distribution, the image frequency distribution calculating means prepares a one-dimensional array composed of a predetermined number of elements, and equally divides a range from 0 degrees to 180 degrees into the predetermined number, The angle distribution is calculated by assigning to one of the elements based on an angle unique to each of the edge points and counting the value of the element. The data output regulation device for solid thing modeling according to any one of 7.   8. The image frequency distribution calculating means, when counting the value of the assigned element, is weighted by the edge strength of the edge point corresponding to the edge point vector. Item 9. A data output restriction device for three-dimensional object formation according to Item 8. The image frequency distribution calculating means, as the edge intensity for the weighting, De the magnitude of the vector between the edge point, when the maximum value of edge points between the vectors was ^{Demax, De × {Demax 2 -De} 2 The data output control device for three-dimensional object formation according to claim 9, wherein a value obtained by performing correction by multiplying the original edge strength by a value of ^1/2 is used.   The model frequency distribution calculating means obtains a vector from an average coordinate of a polygon in the target model to an average coordinate of another polygon as an inter-polygon vector, and a size of the inter-polygon is a distance between the polygons. The distance between the respective polygons and the inherent angle between the polygons are calculated by setting the angle between the inter-polygon vector and the normal vector of one of the corresponding polygons as the unique angle between the respective polygons. The data output regulation device for three-dimensional object formation according to any one of claims 1 to 10.   In calculating the distance distribution, the model frequency distribution calculating means prepares a one-dimensional array composed of a predetermined number of elements, and equally divides the range of the maximum value of the calculated distances into the predetermined number. The distance distribution is calculated by assigning each of the distances to any of the elements based on the distance and counting the number corresponding to the elements. The data output regulation device for solid thing modeling according to any one of claims 11.   The model frequency distribution calculating means prepares a one-dimensional array composed of a predetermined number of elements when calculating the angle distribution, and equally divides a range from an angle of 0 degrees to 180 degrees into the predetermined number, The angular distribution is calculated by assigning to any one of the elements based on an angle unique to each polygon and counting the value of the elements. The data output regulation device for solid thing modeling according to any one of the above.   The three-dimensional object formation according to claim 12 or 13, wherein the model frequency distribution calculating means is configured to weight the area of each polygon when counting the value of the assigned element. Data output regulation device. The frequency distribution matching unit matches the distance distribution and the angle distribution of the target model with the distance distribution and the angle distribution of the restriction image, respectively.
If the correlation coefficient between the distance distributions and the correlation coefficient between the angle distributions are calculated, and the calculated correlation coefficient between the two is larger than a predetermined positive threshold, the output should be regulated. The data output control device for three-dimensional object formation according to any one of claims 1 to 14, wherein the determination is made. The apparatus further comprises polygon reduction means for reducing polygons so that the number of polygons included in the target model is equal to or less than a predetermined value,
The data output control device for three-dimensional object formation according to any one of claims 1 to 15, wherein the model frequency distribution calculation means performs processing on the target model from which the polygons have been reduced. . It further comprises object model division means for dividing an object model which is a polygon model to be output into a plurality of partial object models,
The three-dimensional object formation data output control device according to any one of claims 1 to 16, wherein the model frequency distribution calculation means performs processing on the partial target model.   The polygon is a triangle, and the target model dividing means divides the polygon so that a certain polygon and three adjacent polygons sharing an edge with the polygon belong to the same partial target model. The data output regulation device for three-dimensional object formation according to 17.   The data output regulation device for three-dimensional object formation according to claim 1, further comprising registration means for receiving the frequency distribution calculated by the image frequency distribution calculation device and registering the received frequency distribution in the database. . A unit for outputting the target model to a connected three-dimensional object forming apparatus;
When it is determined that the output should be restricted by the frequency distribution collating means executed in parallel with the three-dimensional object formation processing by the three-dimensional object formation device, the three-dimensional object formation device Means for outputting an output stop instruction;
The three-dimensional object formation data output regulation device according to any one of claims 1 to 19, further comprising: A configuration in which an output control terminal and a processing server are connected via a network,
The output control terminal includes the model frequency distribution calculating unit.
The processing server is
Said database,
Receiving means for receiving distance distribution and angle distribution of the target model from the output control terminal via a network;
The frequency distribution comparing means;
An output appropriateness data transmitting unit that transmits data based on whether or not the output should be restricted, which is determined by the frequency distribution comparing unit, to the output control terminal;
The data output regulation device for three-dimensional object formation according to claim 1, characterized in that: A data output regulation device for three-dimensional object formation according to any one of claims 1 to 21,
A three-dimensional object formation apparatus for forming a three-dimensional object using the target model permitted to be output by the three-dimensional object formation data output regulation device;
A three-dimensional object formation system characterized by having.   A program for causing a computer to function as a data output regulating device for three-dimensional object formation according to any one of claims 1 to 21.