JPH048833B2 - - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION TECHNICAL FIELD The present invention relates to an image processing method for inspecting color defects on the surface of an object to be inspected and shape defects such as irregularities, foreign matter contamination, scratches, or cracks occurring on the surface.

BACKGROUND ART In conventional image processing apparatuses, an object to be inspected is imaged by a television camera, and the image signal from the television camera is binarized. Then, the binarized image data is stored in an image memory, and the image data is subjected to arithmetic processing to detect color defects and shape defects of the object to be inspected.

In the binarization process mentioned above, since the amount of information in the image data is small, it is difficult to obtain accurate data due to the influence of electrical noise mixed in during the processing process or fluctuations in the amount of light received by the TV camera. There was a problem that I couldn't do.

Purpose An object of the present invention is to provide an image processing method that is resistant to noise and can detect defects on the surface of an object to be inspected with high accuracy.

Embodiment FIG. 1 shows an image processing device 1 according to an embodiment of the present invention.
2 is a block diagram of the image processing apparatus 1, and FIG. 2 is a diagram for explaining the operation of the image processing apparatus 1. Image processing device 1
This is an apparatus for inspecting color defects such as color unevenness on the surface of the object to be inspected 2, and shape defects such as irregularities, foreign matter contamination, scratches, or cracks occurring on the surface. The light source 3 is arranged at an angle that does not cause specular reflection on the surface of the object to be inspected 2, and is arranged at an angle that prevents specular reflection from the surface of the object to be inspected.
to illuminate. The industrial television camera 4 images the light from the object 2 to be inspected. Industrial TV camera 4
The analog image data from the video processing circuit 5
The image data is supplied to an analog/digital converter 6 in the frame memory F2, where it is converted into digital image data and stored in the frame memory F2. Frame memory F2 is 1
Since each pixel has a capacity of 8 bits, the second
For example, if the image of the object 2 to be inspected shown in FIG.
Stored as a step-by-step grayscale image.

The digital image data output from the analog/digital converter 6 is also provided to a differentiating circuit 7. The differentiation circuit 7 extracts edges in the image by directional differentiation of the grayscale image of the input object 2 to be inspected. To extract edges from a grayscale image, a differential method is generally used because it is sufficient to extract changes in density in the image, and this differential circuit 7 extracts the edge value corresponding to the grayscale difference between the edges of the image and the length of the edge. Direction is required. Data indicating the edge direction output from the differentiating circuit 7 is encoded and stored in the direction code memory 8. Since the edge line with continuous edges outputted from the differentiating circuit 7 is wide as shown in FIG. 2, it is thinned by the thinning processing circuit 9 to a line with a width of one pixel as shown in FIG. 2. Furthermore, the edge value of the thinned edge line is compared with a predetermined threshold value in a threshold processing circuit 10, and pixels having edge values below the threshold value are erased. Through this processing, image data due to electrical noise mixed in during the pixel processing process is removed.

The threshold value of the threshold processing circuit 10 is high, the contrast of the original image is insufficient,
Alternatively, when there is a lot of electrical noise, the threshold-processed edge line becomes discontinuous as shown in FIG. 24. In order to make this a complete line drawing, the thin line extension processing circuit 11 calculates an evaluation function between the pixel of interest and its surrounding points, starting from the end point of the discontinuous edge line, and calculates an evaluation function between the pixel of interest and its surrounding points, and Extend edge lines to surrounding points. This evaluation function can be expressed, for example, as in the first equation.

H _i = |e _i | ² × cos (∠e ₀ −∠e _i ) …(1) where i = 1, 2, …, 8 Here, |e _i | is the differential of any surrounding point of the pixel of interest It is the magnitude of the value, ∠e _i is the differential direction value of the pixel, and ∠e ₀ is the differential direction value of the pixel of interest. The edge lines that have been extended until they intersect with other edge lines become continuous as shown in FIG. 2, and are stored in the frame memory F1 as a complete line drawing.
Stored in

Frame memories F1, F2 and direction code memory 8 are connected to data bus 12. The processing circuit 13 performs processing for detecting color defects and shape defects in the object to be inspected 2 via the data bus 12 .

FIG. 3 is a diagram for explaining the above-mentioned directional differentiation and thinning processing. Reference symbols A to I shown in FIG. 31 correspond to the densities of nine arbitrarily extracted pixels, respectively. The horizontal and vertical difference values of reference mark E are expressed by the second and third equations.

ΔV=(G+H+I)−(A+B+C)…(2) ΔV=(A+D+C)−(C+F+I)…(3) Differential value |e| E at pixel _E is expressed by the fourth equation.

|e| _E =√(△) ² +(△) ² (4) Further, the direction value ∠e _E at the pixel E is expressed by the fifth equation.

∠e _E = tan ^-1 (△V/△H) + π/2 ...(5) In this way, directional differentiation is performed for each pixel, and since the edge line with continuous edges indicated by the differential value is wide, Line thinning processing is performed to narrow the line to a line with a width of 1 pixel.

FIG. 3 2 shows the minute values |e| corresponding to pixels A to I.
It is a figure showing _A ~|e| _I . |e| _A ~ |e| _I is
Focusing on the pixel E of the differential value of each pixel, compare the differential values of the two pixels in the direction perpendicular to the direction of ∠e _E with the differential value |e| _E of the pixel E, and | _e | 2
For example, π/4≦
When ∠e _E ≦3π/4, |e| _E > |e| _D and |e
If | _E ≧|e| _F , a flag is set at the corresponding address on the thinned image. In this way, the image data from the analog/digital converter 5 is sequentially scanned and flags are set. By performing this for all pixels, the thinned image shown in FIG. 2 can be obtained.

FIGS. 4 and 5 are diagrams for explaining mask regions M and N that intersect in the edge direction of a pixel E having an edge flag. The processing circuit 13 raster-scans the frame memory F1 to detect the presence or absence of an edge flag. When an edge flag is detected, the edge direction of the pixel E is read out from the direction code memory 9, and a direction intersecting with the direction is read out from the direction code memory 9. Laterally symmetrical mask areas M and N are set. The mask region M has a plurality of (three in this embodiment) region portions m ₁ to m ₃ , and the mask region N has region portions m ₁ to m 3 .
It has the same number of area portions n ₁ to n ₃ as m ₃ . These area portions m ₁ to m ₃ and n ₁ to _{n 3} are, for example, 3 pixels×
It has a size of three pixels, and is arranged in a row in the intersecting directions with the pixel E as the center. The density of each pixel corresponding to the address in the mask areas M, N is read out from the frame memory F2, and the average density in each of the mask areas M, N is calculated. The reason why the average density is used here is to remove image noise by smoothing.

Next, the difference or ratio (≧1) of each average concentration of area portions m ₁ , n ₁ , area portions m ₂ , n ₂ , and area portions m ₃ , n ₃
are calculated respectively, and the maximum value among these three sets is calculated and compared with a predetermined discrimination level. For example, the difference I ₁ and the ratio of the average density of the pixel E of interest
I ₂ is shown by the sixth and seventh equations.

I ₁ = MAX (｜ ₁ − ₁ ｜, ｜ ₂ − ₂ ｜, ₃ − ₃ ｜) …(6) I ₂ = MAX ( ₁ / ₁ , ₂ / ₂ , ₃ / ₃ ) … (7) Here ₁ , ₂ , ₃ , ₁ , ₂ , and ₃ are the respective average densities of the mask area portions _m1 to _m3 and _n1 to _n3 , and have the relationships of ₁ ≧ ₁ , ₂ ≧ ₂ , and ₃ ≧ ₃ . Now let S ₁ be the value of the predetermined discrimination level,
If the ratio is S ₂ , when I ₁ ≦S ₁ or I ₂ ≦S ₂ , the flag is erased in frame memory F1, and when I ₁ > S ₁ or I ₂ > S ₂ , it is erased in frame memory F1. Leave a flag. Below, use the same method to save frame memory F1.
When flag processing is performed on all pixels of
Only the strong edges of the defect will ultimately remain, while the noise and weak edges of the defect can be erased.

By setting three sets of each of the mask regions M and N as described above, it is possible to extract defects with relatively smooth density differences, which was previously difficult with the differential method, as shown in Figure 5. It is possible. Number of pairs of mask areas M and N and each mask area part
The distances between m ₁ to m ₃ and n ₁ to _{n 3} are preferably selected to be appropriate values depending on the target defect. Furthermore, instead of erasing weak flags on the frame memory F1, only the flags remaining in the frame memory where another 1-bit data can be stored may be stored.

Effects As described above, according to the present invention, a defective part of an object to be inspected can be extracted as a contour line, and a defect can be detected with high accuracy. Furthermore, it is also possible to detect defects with relatively smooth density differences, which has been difficult with the differential method.

[Brief explanation of drawings]

FIG. 1 is a block diagram of an image processing device 1 according to an embodiment of the present invention, FIG. 2 is a diagram for explaining the operation of the image processing device 1, and FIG. 3 is a diagram showing directional differentiation and thinning processing. FIG. 4 and FIG. 5 are diagrams for explaining a region intersecting the edge direction of a pixel with an edge flag. 1...Image processing device, 2...Object to be inspected, 6...
...analog/digital converter, 7...differentiation circuit,
8... Direction code memory, 9... Thinning processing circuit, 10... Threshold processing circuit, 11... Thin line extension processing circuit, F1, F2... Frame memory.

Claims (1)

[Scope of Claims] 1. An imaging means for imaging an object to be inspected, an analog/digital converter for converting an analog signal output from the imaging means into a digital signal, and converting image data from the analog/digital converter into binary values. a binarization processing gate circuit that extracts only the image of the object to be inspected from within the imaging screen through conversion processing; a first frame memory that stores gray scale images of the object to be inspected; , a differentiating circuit for obtaining an edge value corresponding to the difference in density of an image edge and a value indicating the direction in which the edge extends; a direction code memory for storing the direction value obtained by the differentiating circuit as code data; a thinning processing circuit that thins the edges of the thinned image, and a threshold that level-discriminates the edge value of the thinned image with a predetermined threshold and erases pixels with edge values below the threshold. a processing circuit, a thin line extension processing circuit that extends threshold-processed edges to form a continuous thin line, a second frame memory for storing an image subjected to the thin line extension processing, and a second frame memory for storing an image subjected to the thin line extension processing; a processing circuit that processes data, detects edges of the thin line extension processed image, reads edge directions of a plurality of pixels in which edges have been detected from the direction code memory, and reads a direction intersecting the edge direction of the plurality of pixels. The density differences or density ratios of areas having a predetermined number of bits that are symmetrical on both the left and right sides extending in the area are compared with each other, and the results of this comparison calculation are compared with a predetermined discrimination level, and among the plurality of pixels, An image processing device that detects pixels that are above the discrimination level or pixels that are below the discrimination level.