JP7760808B2 - Dimensional data calculation device, dimensional data calculation method, program, product manufacturing system, and terminal device - Google Patents

Description

This disclosure relates to a dimension data calculation device, a dimension data calculation method, a program, a product manufacturing system, and a terminal device.

Devices that calculate the size of an object have been studied in the past. For example, Patent Document 1 (JP 2011-227692 A) discloses technology that measures the size of specific body parts of a specified person based on a subject image in which the specified person's entire body appears and the person's height input through an input operation.

However, conventional technology has not always been able to calculate each part of an object with high accuracy.

A dimensional data calculation device according to a first aspect determines dimensional data for each part of an object based on first calculated values of dimensional data obtained by polynomial regression of attribute data and second calculated values of dimensional data for a specific part obtained from image data. This makes it possible to obtain the dimensional data for each part of the object as a whole with high accuracy. Then, by using such dimensional data, it is possible to manufacture a product that fits the shape of the object.
The dimension data calculation device according to the second aspect is the dimension data calculation device according to the first aspect, which is capable of calculating with high accuracy dimension data of each part of a person (hereinafter also referred to as the subject), which is an object. By using such dimension data, it is possible to manufacture a product that fits the shape of the subject.
The dimension data calculation device of the third aspect is the dimension data calculation device of the second aspect, and receives image data including: a front image of an object in which the arms are spread apart so as not to overlap the torso in a front view and are positioned so as to fit within the width of the torso in a side view; a side image of the object in which the arms are spread apart so as not to overlap the torso in a front view and are positioned so as to fit within the width of the torso in a side view; and a side image of the object in which the arms are positioned higher than the waist in both the front and side views. Using such image data, dimension data of specific parts of the object can be determined with high accuracy. Furthermore, using such dimension data, products that are more closely matched to the shape of the object can be manufactured.
A fourth aspect of the dimension data calculation device is the dimension data calculation device of any one of the first to third aspects, in which, if the difference between the first calculated value and the second calculated value is within a predetermined value, the dimension data of the specific part is set to the second calculated value. As a result, for the specific part where individual differences in shape are likely to occur, dimension data based on image data is used, making it possible to determine the dimension data of each part of the object as a whole with high accuracy.
A fifth aspect of the dimension data calculation device is the dimension data calculation device of any one of the first to fourth aspects, in which, when the difference between the first calculated value and the second calculated value exceeds a predetermined value, the dimension data of the specific portion is set to the first calculated value, thereby maintaining the reliability of the dimension data as a whole even when irregular image data is acquired.
The dimension data calculation device of the sixth aspect is the dimension data calculation device of the first to fifth aspects, in which the second calculated value of the dimension data of a specific part of the object is calculated based on the perimeter of the virtual figure, so that the second calculated value can be easily calculated.
A seventh aspect of the dimension data calculation device is the dimension data calculation device of the sixth aspect, which calculates a second calculated value of dimension data of a specific portion of an object using a learning device that has been trained to output the perimeter of a specific portion of a predetermined object in response to an input of the perimeter of a predetermined virtual figure. This makes it possible to easily calculate the second calculated value of dimension data of a specific portion of a complex shape using a virtual figure whose perimeter can be easily calculated.
The dimension data calculation device according to the eighth aspect uses image data including a depth map in the dimension data calculation devices according to the first to seventh aspects, and therefore can calculate the second calculated value from a single image data.
The dimension data calculation device of the ninth aspect is a dimension data calculation device according to the first to eighth aspects, which generates a silhouette image of the object from image data and calculates the second calculated value from the silhouette image. Therefore, for specific parts where individual differences in shape are likely to occur, dimension data based on the silhouette image is used, making it possible to determine the dimension data of each part of the object as a whole with high accuracy.
The dimension data calculation device of the tenth aspect is a dimension data calculation device of the first to ninth aspects, which accepts image data of an object transmitted from a terminal device, and if the difference between the first calculated value and the second calculated value exceeds a predetermined value, requests the terminal device to resend the image data, thereby enabling highly reliable dimension data to be calculated.
The dimensional data calculation method of an eleventh aspect determines dimensional data for each part of an object based on a first calculated value of dimensional data obtained from attribute data and a second calculated value of dimensional data for a specific part obtained from image data. This makes it possible to calculate the dimensional data for each part of the object with high accuracy as a whole. Then, by using such dimensional data, it is possible to manufacture a product that fits the shape of the object.
A twelfth aspect of the present invention provides a program for determining dimensional data for each part of an object based on first calculated values of dimensional data obtained from attribute data and second calculated values of dimensional data for a specific part obtained from image data. By having a computer execute such a program, it is possible to obtain the dimensional data for each part of the object as a whole with high accuracy. As a result, it is possible to manufacture a product that matches the shape of the object.
A thirteenth aspect of the present invention provides a product manufacturing system that determines dimensional data for each portion of an object based on first calculated values of dimensional data obtained from attribute data and second calculated values of dimensional data for a specific portion obtained from image data, and a product manufacturing device that uses the determined dimensional data to manufacture a product related to the shape of the object. Thus, a product manufacturing system that manufactures a product that fits the shape of the object can be provided.
In a fourteenth aspect, when the target object is a human, the terminal device outputs guide information prompting the capture of a front image of the target object with the arms spread apart so as not to overlap the torso in a front view and positioned so as to fit within the width of the torso in a side view, a side image of the target object with the arms spread apart so as not to overlap the torso in a front view and positioned so as to fit within the width of the torso in a side view, and a side image of the target object with the arms positioned higher than the waist in both a front view and a side view. By using such guide information, image data that enables highly accurate determination of dimensional data of specific parts of the target object can be transmitted to the dimension data calculation device. As a result, the dimension data calculation device can accurately determine dimensional data of each part of the target object.

FIG. 2 is a schematic diagram showing the configuration of a dimension data calculation device 2520 according to an embodiment of the present invention. FIG. 10 is a schematic diagram showing the configuration of a calculation unit 2524D according to the same embodiment. FIG. 10 is a schematic diagram showing an example of shape data of the bust, waist, and hips on the side according to the embodiment. FIG. 10 is a schematic diagram showing an example of shape data of the bust, waist, and hips on the front side according to the embodiment. FIG. 10 is a schematic diagram showing an example of shape data of the bust, waist, and hips on the side according to the embodiment. 10 is a flowchart for explaining the operation of a dimension data calculation device 2520 according to the same embodiment. FIG. 25 is a schematic diagram showing the concept of a product manufacturing system 2501. FIG. 25 is a schematic diagram showing the configuration of a terminal device 2510. FIG. 10 is a schematic diagram showing the concept of guide information GI. FIG. 25 is a sequence diagram for explaining the operation of the product manufacturing system 2501.

Embodiments of the present invention will be described below using the accompanying drawings. Note that identical components in each drawing will be assigned approximately the same reference numerals, and duplicate explanations will be omitted.

(1) Dimension Data Calculation Device Fig. 1 is a schematic diagram showing the configuration of a dimension data calculation device 2520 according to one embodiment of the present invention. The dimension data calculation device 2520 can be realized by any computer, and includes a storage unit 2521, an input/output unit 2522, a communication unit 2523, and a processing unit 2524. Note that the dimension data calculation device 2520 may also be realized as hardware using an LSI (Large Scale Integration), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or the like.

The storage unit 2521 stores various types of information and is realized by any storage device such as a memory or hard disk. For example, the storage unit 2521 stores weighting coefficients Wsi necessary for performing the information processing described below, in association with the length, weight, etc. of the object. The weighting coefficients are acquired in advance by performing machine learning from training data including attribute data and dimensional data described below. Note that when the object is a "person," the object is treated as different for each gender. In that case, different weighting coefficients are used for men and women as described above. However, in the following explanation, when the object is a person, for convenience, the explanation will be given without distinguishing between genders.

The input/output unit 2522 is realized by a keyboard, mouse, touch panel, etc., and is used to input various information to the computer and output various information from the computer.

The communication unit 2523 is realized by an arbitrary network card, etc., and enables communication with communication devices on the network via wired or wireless connections.

The processing unit 2524 performs various information processing operations and is realized by a processor such as a CPU or GPU and memory. Here, the program stored in the memory unit 2521 is loaded into the computer's CPU, GPU, etc., causing the processing unit 2524 to function as an acquisition unit 2524A, an extraction unit 2524B, a conversion unit 2524C, and a calculation unit 2524D.

The acquisition unit 2524A acquires attribute data including at least total length data of the object. Attribute data indicates the attributes of the object, such as the object's total length, weight, and time elapsed since creation (including age). If the object is a person, height, weight, age, etc. are defined as attribute data.

The acquisition unit 2524A also acquires image data of an object photographed. When image data is captured using a single camera, the acquisition unit 2524A acquires image data of the object photographed from different directions. Specifically, the acquisition unit 2524A accepts image data of the front and side of the object.

The extraction unit 2524B extracts shape data indicating the shape of the object from the image data. Specifically, the extraction unit 2524B extracts the shape data of the object by extracting the object region included in the image data using a semantic segmentation algorithm (such as Mask R-CNN) prepared for each type of object.

The conversion unit 2524C converts the shape data based on the full-length data to create a silhouette. This standardizes the shape data. The conversion unit 2524C may also convert the shape data into monochrome image data using not only the full-length data but also the depth data of the object region, generating a "gradation silhouette image" (new shape data). A gradient silhouette image is not simply black and white binary data, but a single-color, multi-tone monochrome image represented, for example, by data with brightness values ranging from 0 ("black") to 1 ("white") based on the depth data. In other words, the gradient silhouette image data is associated with the depth data and contains even more information regarding the shape of the object. In the following description, gradient silhouette images and simple silhouette images may be referred to simply as silhouette images without distinguishing between them.

As shown in FIG. 2, the calculation unit 2524D has the functions of a first calculation unit 2524D1, a second calculation unit 2524D2, and a determination unit 2524D3, and calculates the dimensional data for each part of the object. In the following description, all calculated values of the dimensional data corresponding to each part of the object may be referred to as the "dimension data set."

The first calculation unit 2524D1 calculates a first calculated value of the dimensional data for each part of the object using the attribute data acquired by the acquisition unit 2524A. Specifically, the first calculation unit 2524D1 calculates the dimensional data for each part of the object by performing quadratic regression on the attribute data using machine-learned weighting coefficients Wsi. The weighting coefficients are optimized for each part of the object, and the weighting coefficient for the i-th part of the object is represented as Wsi. Note that the symbol i = 1 to j, and the symbol j represents the total number of dimension locations for which dimensional data is to be calculated. The symbol s represents the number of elements used in the calculation obtained from the attribute data.

For example, assume that the attribute data of an object consists of total length data h, weight data w, and time-series data a. In other words, the attribute data is a set of elements (h, w, a). In this case, the first calculation unit 2524D1 calculates the value obtained by squaring each element (h, w, a) of the attribute data of the object (also called a quadratic term), the value obtained by multiplying each element (also called an interaction term), and the value of each element itself (also called a linear term). As a result, data having the following nine elements is obtained:
Next, the first calculation unit 2524D1 multiplies each of these nine elements by a weighting factor Wsi to calculate a first calculated value of the dimension data. In this case, since s = 9, the weighting factor Wsi has 9 × i elements. However, the first calculation unit 2524D1 is not limited to using quadratic regression, and may calculate the first calculated value using linear regression or other polynomial regression.

When the object is a "person," the first calculation unit 2524D1 calculates first calculated values of dimensional data such as the neck circumference, back shoulder width, back width, back length, total length, back length, sleeve length, elbow length, arm circumference, upper arm circumference, elbow circumference, wrist circumference, front length, breast length, bust, underbust, chest width, bust point distance, waist, hips, mid-hip, waist length, armpit length, inseam length, knee length, thigh circumference, knee circumference, calf, ankle circumference, and rise.

The second calculation unit 2524D2 calculates a "second calculated value" of the dimensional data of the specific part of the object from the image data. Specifically, the second calculation unit 2524D2 regards the specific part as a virtual figure from multiple pieces of shape data corresponding to the front and side shapes converted by the conversion unit 2524C, and calculates its perimeter. The second calculation unit 2524D2 then calculates the second calculated value of the dimensional data of the specific part of the object based on the perimeter.

When the object is a person, specific features include the bust, hips, and waist. These specific features are extracted based on predetermined rules. For example, in the example shown in FIG. 3, the second calculation unit 2524D2 calculates the widths hB1, hW1, and hH1 of the bust, waist, and hips on the side from the shape data representing the silhouette shape of the side view. Here, a predetermined region L of the shape data is extracted, and the shape data is divided into three parts in the height direction at a predetermined ratio. In the shape data LW, which is the second-highest division from the top, the most concave part in the width direction is determined as the waist position. In the shape data LB, which is the first-highest division from the top, the most convex part is determined as the bust position. In the shape data LH, which is the third-highest division from the top, the most convex part is determined as the hip position. Next, as shown in FIG. 4, the second calculation unit 2524D2 calculates the bust, waist, and hip positions corresponding to the side shape data in the shape data representing the silhouette shape of the front view. The second calculation unit 2524D2 then calculates the widths hB2, hW2, and hH2 of the bust, waist, and hips on the front side, respectively. From the front and side widths calculated in this way, the second calculation unit 2524D2 calculates the perimeter of the specific part when considered as a virtual figure. Any shape can be used as the virtual figure as long as the perimeter can be calculated from the front and side widths. Here, the virtual figure is assumed to be elliptical. The second calculation unit 2524D2 then outputs dimensional data of the specific part from the perimeter of this elliptical shape using a specified inference model. The above-mentioned inference model is constructed by machine learning a model such as linear regression or a neural network using a dataset including the perimeter of the specified virtual figure and the perimeter of the specific part of the specified object.

When the object is a person, the side shape data shown in Figure 5 may be used in addition to the side shape data shown in Figure 3. The side shape data shown in Figure 5 shows the arms positioned higher than the waist. This prevents the waist and arms from overlapping in a side view, allowing the position and width of the waist to be determined with high accuracy. Note that the position and width of the hips, as well as the waist, may also be determined from the side shape data shown in Figure 5.

The determiner 2524D3 determines a dimension data set, which is a collection of dimension data for each part of the object, based on the calculation results by the first calculator 2524D1 and the second calculator 2524D2. Here, if the difference between the first calculated value and the second calculated value of the dimension data for a specific part of the object is within a predetermined value, the determiner 2524D3 adopts the dimension data of the second calculated value as the dimension data for the specific part. Furthermore, if the difference between the first calculated value and the second calculated value of the dimension data exceeds the predetermined value, the determiner 2524D3 outputs an error. Note that if the difference between the first calculated value and the second calculated value of the dimension data exceeds the predetermined value, the determiner 2524D3 may adopt the dimension data of the first calculated value as the dimension data for the specific part instead of outputting an error, or in addition to outputting an error.

Figure 6 is a flowchart illustrating the operation of a dimension data calculation device 2520 according to a modified example of this embodiment.

First, the dimension data calculation device 2520 accepts input of attribute data related to the object via a terminal device used by the user. The dimension data calculation device 2520 then acquires this attribute data (X1). In particular, if the object is a person, the dimension data calculation device 2520 acquires height, weight, age, etc. as attribute data.

The dimension data calculation device 2520 also acquires image data of the object via a terminal device used by the user (X2). The dimension data calculation device 2520 extracts shape data indicating the shape of each part of the object from each image data. The dimension data calculation device 2520 then performs a rescaling process to convert each piece of shape data to a predetermined size based on the total length data.

The terminal device may output guide information to a display screen or the like so that image data of an object with a pre-specified shape can be acquired. For example, if the object is a person, the terminal device prompts the person being photographed to strike a specific pose. Here, the terminal device outputs guide information so that the following images are captured: a front image of the object with arms spread so as not to overlap the torso when viewed from the front and positioned so as to fit within the width of the torso when viewed from the side (Figure 4(a)); a side image of the object with arms spread so as not to overlap the torso when viewed from the front and positioned so as to fit within the width of the torso when viewed from the side (Figure 3(a)); and a side image of the object with arms positioned higher than the waist when viewed from the front and from the side (Figure 5(a)). For convenience, silhouetted images are shown as examples.

Next, the dimension data calculation device 2520 calculates dimension data for each part of the object using the attribute data acquired by the acquisition unit 2524A (X3 to X6). Specifically, the dimension data calculation device 2520 calculates a first calculated value of the dimension data for each part of the object by performing quadratic regression on the attribute data using the machine-learned weighting coefficients Wsi.

Next, the dimension data calculation device 2520 calculates a second calculated value of the dimension data of the specific part of the object based on the image data (X7). Specifically, the second calculation unit 2524D2 of the dimension data calculation device 2520 calculates the perimeter of a virtual figure of the specific part of the object from the front and side images of that part. If the object is a person and the specific part is the waist, the dimension data calculation device 2520 assumes that the cross section along the imaging direction is an ellipse and calculates the perimeter of the ellipse from the image data. Next, the dimension data calculation device 2520 (second calculation unit 2524D2) outputs the dimension data of the waist using a model trained using the calculated perimeter of the ellipse and the actual dimension data of the waist as training data.

The dimension data calculation device 2520 then compares the first calculated value with the second calculated value for the specific portion of the object, and if the difference between them is less than a predetermined value, adopts the second calculated value as the dimension data for the specific portion (X8-Yes, X9). In other words, of the dimension data for each portion calculated from the first calculated value, the dimension data for the specific portion is replaced with the second calculated value to determine the overall dimension data set.

On the other hand, the dimension data calculation device 2520 compares the first calculated value with the second calculated value, and if the difference between them exceeds a predetermined value, outputs an error (X8-No, X10). If the dimension data calculation device 2520 outputs an error, it sends a request to resend the image data to the user's terminal device, etc. However, instead of sending a resend request, the dimension data calculation device 2520 may also use the first calculated value of the dimension data of a specific part. In this case, the entire dimension data set is determined from the first calculated value.

As explained above, the dimension data calculation device 2520 according to this embodiment uses both attribute data and image data to calculate overall dimension data for multiple locations on an object with high accuracy. This effect is particularly pronounced when calculating dimensional data for the human body. Additionally, the inventors discovered that while dimensional data for each part of the human body can generally grasp a certain trend based on height, weight, age, and gender, individual differences are relatively large for specific parts. Specific parts with large individual differences include the bust, hips, and waist. Therefore, when calculating dimensional data for the human body, the dimension data calculation device 2520 according to this embodiment uses attribute data to quickly calculate with a certain degree of accuracy using polynomial regression for parts with little individual difference, and uses image data to perform high-precision calculations for specific parts with large individual differences. This allows dimensional data for each part of the object to be calculated with high accuracy overall. Furthermore, using such dimensional data makes it possible to manufacture products that fit the shape of the object.

In addition, the dimension data calculation device 2520 according to this embodiment uses an inference model that has been trained using the perimeter of a virtual figure that can be calculated from a small number of images and the actual length measured in advance as training data, making it possible to easily calculate a second calculated value of the dimension data of a specific part of a complex shape from a small number of images.

Furthermore, in the dimension data calculation device 2520 according to this embodiment, the determination unit 2524D3 sets the dimension data of the specific part to the second calculated value if the difference between the first calculated value and the second calculated value is within a predetermined value, thereby improving the accuracy of the dimension data of the specific part, which is prone to individual differences in shape.

In addition, in the dimension data calculation device 2520 according to this embodiment, the determination unit 2224D3 outputs an error if the difference between the first calculated value and the second calculated value exceeds a predetermined value. Here, the determination unit 2224D3 may set the dimension data of a specific portion as the first calculated value in response to the error output. This configuration makes it possible to maintain the reliability of the dimension data set even when irregular image data is acquired. Additionally, image data may contain noise due to the shooting environment, etc., which may cause the second calculated value to deviate significantly from the actual dimensions. Therefore, when image data containing noise is acquired and an irregular second calculated value is calculated, the first calculated value is adopted, thereby preventing abnormal values from being included in part of the dimension data set. This allows a dimension data set with a certain degree of reliability as a whole to be output without requiring the image data to be reacquired, thereby improving user convenience.

The dimension data calculation device 2520 may also include a depth data measurement device. By using a depth data measurement device, the image data acquired by the acquisition unit 2524A can be converted into RGB-D (Red, Green, Blue, Depth) data. This allows the perimeter of the virtual shape to be calculated from either front or side image data, without necessarily acquiring both. In other words, since the image data includes a depth map, the second calculated value can be calculated from a single image data. An example of a depth data measurement device is a stereo camera. In this specification, the term "stereo camera" refers to any type of imaging device that can simultaneously capture images of an object from multiple different directions, reproduce binocular parallax, and construct a depth map. In addition to stereo cameras, depth data may also be obtained using a LiDAR (Light Detection and Ranging) device to construct a depth map.

(2) Application to a Product Manufacturing System An example in which the above-described dimension data calculation device 2520 is applied to a product manufacturing system 2501 will be described below.

FIG. 7 is a schematic diagram showing the concept of a product manufacturing system 2501 according to this embodiment.
The product manufacturing system 2501 is a system for manufacturing a desired product 2506, and includes a dimension data calculation device 2520 that can communicate with a terminal device 2510 owned by a user 2505, and a product manufacturing device 2530. As an example, Fig. 7 shows a concept of the overall configuration when the target object 2507 is a person and the product 2506 is a chair. However, the target object 2507 and the product 2506 of the product manufacturing system according to this embodiment are not limited to these.

The terminal device 2510 is realized by any computer. Here, as an example, it is assumed to be realized by a smart device. Specifically, when a user program is installed on the terminal device 2510, it functions as an acquisition unit 2511, a communication unit 2512, a processing unit 2513, and an input/output unit 2514, as shown in FIG. 8. The terminal device 2510 may be carried by the user or installed in a store.

The acquisition unit 2511 accepts input of attribute data indicating the attributes of the object 2507. Examples of "attributes" include the total length, weight, and time elapsed since creation (including age) of the object 2507.

The acquisition unit 2511 also acquires image data of the object 2507. For example, the acquisition unit 2511 is configured with an arbitrary monocular camera. The data acquired by the acquisition unit 2511 is processed by the processing unit 2513 to generate image data of the object. The acquisition unit 2511 may also have a stereo camera function that simultaneously captures images of the object from multiple different directions to reproduce binocular parallax. Here, the acquisition unit 2511 is configured to have a camera function, but the camera function may be provided separately from the acquisition unit 2511. In this case, the image data is not limited to image data captured by the terminal device 2510, and may also be image data captured using, for example, a stereo camera separately installed in the store.

The communication unit 2512 is realized by a network interface such as a network card, and enables wired or wireless communication with communication devices on the network. The function of the communication unit 2512 enables the terminal device 2510 to send and receive various information between the dimension data calculation device 2520 and the product manufacturing device 2530.

The processing unit 2513 is implemented by a processor such as a CPU (Central Processing Unit) and/or a GPU (Graphical Processing Unit), as well as memory, to perform various information processing tasks, and executes various processes by loading programs.

The input/output unit 2514 accepts various types of information as input to the terminal device 2510 and outputs various types of information from the terminal device 2510. Specifically, the input/output unit 2514 is realized by an arbitrary touch panel. The input/output unit 2514 also displays guide information on the screen, as indicated by the dotted line in Figure 9, so that the object 2507 of the desired shape can be photographed. This allows the user to photograph the object 2507 (here, a person) so that it fits within this dotted line. If the image data is photographed so that the object 2507 fits within the dotted line, there is a higher probability that the second calculated value in the dimension data calculation device 2520 will be a normal value. Here, when the object is a person, the input/output unit 2514 outputs guide information that prompts the user to capture a front image of the object with the arms spread so as not to overlap the torso when viewed from the front and positioned so as to fit within the width of the torso when viewed from the side, a side image of the object with the arms spread so as not to overlap the torso when viewed from the front and positioned so as to fit within the width of the torso when viewed from the side, and a side image of the object with the arms positioned higher than the waist when viewed from the front and from the side.

As described above, the dimension data calculation device 2520 can be realized by any computer. Here, the memory unit 2521 of the dimension data calculation device 2520 stores information transmitted from the terminal device 2510 in association with identification information identifying the user 2505 of the terminal device 2510 and/or identification information identifying the object 2507. The memory unit 2521 also stores parameters and the like necessary for executing the information processing described below. For example, the memory unit 2521 stores the weighting coefficient Wsi described above in association with attribute items of the object 2507, etc.

The product manufacturing device 2530 is a device that manufactures a desired product related to the shape of the target object 2507 using the dimension data set calculated using the dimension data calculation device 2520. Note that the product manufacturing device 2530 can be any device that can automatically manufacture and process products, and can be realized, for example, by a 3D printer.

Figure 10 is a sequence diagram illustrating the operation of the product manufacturing system 2501 related to the bookbinding embodiment.

First, the user 2505 inputs attribute data indicating the attributes of the object 2507 into the terminal device 2510 (Y1). Here, the attribute data input includes the total length data, weight data, and temporal data (including age, etc.) of the object 2507.

Next, the object 2507 is imaged multiple times via the terminal device 2510 so that the entire object 2507 is captured from different directions, and multiple pieces of image data of the object 2507 are generated (Y2). Here, the object 2507 is a person, and as described using Figures 3 to 5, a front image of the object is captured with the arms spread so as not to overlap the torso when viewed from the front and positioned so as to fit within the width of the torso when viewed from the side, a side image of the object with the arms spread so as not to overlap the torso when viewed from the front and positioned so as to fit within the width of the torso when viewed from the side, and a side image of the object with the arms positioned higher than the waist when viewed from the front and from the side are captured.

Then, these multiple pieces of image data and attribute data are transmitted from the terminal device 2510 to the dimension data calculation device 2520. At this time, the terminal device 2510 may determine whether the object included in the image data (i.e., reflected in the image data) is a pre-registered object. More specifically, the processing unit 2513 of the terminal device 2510 determines whether the object included in the image data is a pre-registered object using an "object identification model" that identifies whether each pixel is a specific object (Y3). For example, if the object is a "person," the processing unit 2513 determines whether a person is reflected in the image data, and if a person is not reflected, outputs this to the input/output unit 2514. This allows the user to confirm whether the image data is appropriate before transmitting the image data to the dimension data calculation device 2520 (Y3 - Yes, Y4).

When the dimension data calculation device 2520 receives multiple pieces of image data and attribute data from the terminal device 2510, it uses this data to calculate dimension data for each part of the object 2507 and outputs a dimension data set (Y5). Note that the terminal device 2510 displays specified dimension data on its screen according to its settings.

Then, the product manufacturing device 2530 manufactures the desired product 2506 based on the dimension data set calculated by the dimension data calculation device 2520 (Y6).

As described above, the dimension data calculation device 2520 calculates the dimension data set of the object 2507 with high accuracy, making it possible to provide a desired product 2506 related to the shape of the object 2507. As the desired product 2506, for example, a chair that fits a person based on their shape can be manufactured. Also, for example, clothing customized to the subject's body shape can be manufactured.

When the object is a person, not only can front and side images of the object be acquired in which the arms are spread apart so as not to overlap the torso in the front view and positioned so as to fit within the width of the torso in the side view, but also side images of the object in which the arms are positioned higher than the waist in the front and side views can be acquired, thereby obtaining side images in which the arms do not appear in the waist and hip area. This enables highly accurate extraction of the waist and hip areas. As a result, waist and hip measurement data, which vary significantly among individuals, can be calculated with high accuracy while reducing the computational load. Additionally, when capturing a side image of a person, the arms and torso may overlap in the side view, making it difficult to accurately capture the silhouette shape of the torso. This is due, for example, to the fact that many people arch their backs, and that many people actually lower their arms at an angle forward or backward even when they think they are lowering their arms straight down. Therefore, the inventors have made it possible to improve the accuracy of the measurement dataset by outputting guide information that encourages the capture of side images of different body types and acquiring images prompted by this.

In addition, the product manufacturing system 1001 can manufacture organ models by measuring the shape of various organs, such as the heart. Also, for example, various healthcare products can be manufactured by measuring a person's waist shape. Also, for example, a figure product of a person can be manufactured by measuring the person's shape.

In the above explanation, the dimension data calculation device 2520 and the product manufacturing device 2530 are described as separate devices, but they may also be configured as an integrated unit.

The present disclosure is not limited to the above-described embodiments as they are. The present disclosure can be embodied by modifying its components in the implementation stage without departing from the spirit of the disclosure. Furthermore, the present disclosure can be embodied in various forms by appropriately combining the multiple components disclosed in the above-described embodiments. For example, some components may be omitted from all of the components shown in the embodiments. Furthermore, components may be appropriately combined in different embodiments.

2501 Product manufacturing system 2505 User 2506 Product 2507 Target object 2510 Terminal device 2511 Acquisition unit 2512 Communication unit 2513 Processing unit 2514 Input/output unit 2520 Dimension data calculation device 2521 Storage unit 2522 Input/output unit 2523 Communication unit 2524 Processing unit 2524A Acquisition unit 2524B Extraction unit 2524C Conversion unit 2524D Calculation unit 2524D1 First calculation unit 2524D2 Second calculation unit 2524D3 Determination unit 2530 Product manufacturing device GI Guide information

JP 2011-227692 A

Claims (9)

an acquisition unit that acquires attribute data including at least total length data of the object;
a first calculation unit that calculates first calculated values of dimensional data of a plurality of portions of the object including a specific portion by performing polynomial regression on the attribute data using coefficients learned by machine learning;
a receiving unit that receives image data of the object;
a second calculation unit that calculates a second calculated value of dimensional data of the specific portion of the object from the image data;
a determination unit that determines dimensional data of each portion of the object based on the first calculated value and the second calculated value;
Equipped with
the determination unit sets the dimensional data of the specific portion to the second calculated value when a difference between the first calculated value and the second calculated value is within a predetermined value.
Dimensional data calculation device. an acquisition unit that acquires attribute data including at least total length data of the object;
a first calculation unit that calculates first calculated values of dimensional data of a plurality of portions of the object including a specific portion by performing polynomial regression on the attribute data using coefficients learned by machine learning;
a receiving unit that receives image data of the object;
a second calculation unit that calculates a second calculated value of dimensional data of the specific portion of the object from the image data;
a determination unit that determines dimensional data of each portion of the object based on the first calculated value and the second calculated value;
Equipped with
the determination unit sets the dimensional data of the specific portion to the first calculated value when a difference between the first calculated value and the second calculated value exceeds a predetermined value.
Dimensional data calculation device. The object is a person.
3. The dimension data calculation device according to claim 1 or 2 . The reception unit,
a front image of the object in which the arms are spread so as not to overlap the torso when viewed from the front and the arms are positioned so as to fit within the width of the torso when viewed from the side;
a side image of the object in which the arms are spread so as not to overlap the torso when viewed from the front and the arms are positioned so as to fit within the width of the torso when viewed from the side;
Side images of the object with the arms positioned higher than the waist in front and side views;
Accepting image data including:
4. The dimension data calculation device according to claim 3 . The second calculation unit
calculating a perimeter of a virtual figure from the image data, and calculating a second calculated value of dimensional data of the specific portion of the object based on the perimeter;
The dimension data calculation device according to any one of claims 1 to 4 . the second calculation unit calculates a second calculated value of dimensional data of the specific part of the object using a learning device that has been trained to output the perimeter of the specific part of a predetermined object in response to an input of the perimeter of a predetermined virtual figure.
6. The dimension data calculation device according to claim 5 . The image data is image data including a depth map.
The dimension data calculation device according to any one of claims 1 to 6 . a generation unit that generates a silhouette image of the object from the image data, wherein the second calculation unit calculates the second calculated value from the silhouette image.
The dimension data calculation device according to any one of claims 1 to 7 . a communication unit that communicates with a terminal device that generates image data of the object, the reception unit receiving the image data of the object transmitted from the terminal device;
the determination unit requests the terminal device to retransmit the image data when a difference between the first calculated value and the second calculated value exceeds a predetermined value;
The dimension data calculation device according to any one of claims 1 to 8 .