JP3134280B2 - Picture inspection method - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an inspection method for performing quality control and process control by comparing the state of a defect or the like included in a picture of a printed matter during printing with a reference product in an in-line manner. The present invention relates to a pattern inspection method suitable for suppressing and improving the reliability of inspection results.

[0002]

2. Description of the Related Art As means for inspecting a picture in-line, for example, the means shown in FIG. 9 is employed. That is,
The printing paper 21 supplied from the roll-shaped unwinding web 19 is printed by the printing unit 21 in each color, a desired pattern is printed, and is fed to the next process side. A video camera 22 for picking up an image is arranged at a position facing the printed printing paper 20. The captured image is processed by the processing circuit 2
3 is input. In addition, one of the rollers of the printing unit 21 is provided with a rotary encoder 24 to control the timing of taking in the picture.

FIG. 10 shows an example of a picture 25 of a printed matter. The picture 25 includes pictures R and S, for example.
Further, the printed matter to be inspected includes, for example, a defect 26 having a lower gradation value (density value) than the picture R or S and a defect 27 having a higher gradation value than these. FIG. 11 is a line MM of FIG.
The X-ray shows the gradation value profile of a picture without defects 26 and 27, showing the gradation value profile in the cross section.
The dotted line shows the gradation value profile when the defect 26 and the defect 27 exist. The two-dot chain line Y indicates the defect 26,
27 is a curve showing a gradation value profile of an image obtained by performing MAX filtering processing on a reference pattern without 27, and a dashed line Z-line is a curve showing the gradation value of an image subjected to MIN filtering processing.

FIG. 12 is a block diagram showing an example of the processing circuit 23 shown in FIG. However, the processing circuit itself is not a conventional example, but was developed prior to the present invention. The reference pattern captured from the camera is an n matrix (n × n
The pixel is subjected to the MAX filtering process by the MAX filter a and the MIN filtering process by the n matrix MIN filter b. Here, the MAX filtering process is to select the highest value among the tone values of the target pixel of the captured image and n × n−1 pixels near the target pixel, and use that value as the target image tone value. The replacement process is performed. On the other hand, the minimum filtering process replaces the smallest value with the gradation value of the pixel of interest.

The reference images subjected to the MAX filtering process and the MIN filtering process are temporarily stored in frame memories c and d, respectively, and are subjected to a difference process e,
f is performed. The difference processing is based on the FIX function. The tone value of the original reference image is A, the gradation value of the inspection image to be inspected and B, and that the tone values A and MAX filtering and A _1, MIN filtering the treated ones A ₂ Then, the FIX function is FIX (B-A ₁ ),
Defined by FIX _(A 2 -B). As shown in FIG. 11, A ₁ is a Y line, A ₂ is a Z line, and B is defects 26 and 27.
The gradation values are expressed by the X-rays having. Furthermore, B-A ₁ or _A 2 -B 0 as a definition of FIX function
Or, in the case of minus, the FIX function value is set to 0. (B-A ₁₎ from the above FIX becomes all values minus except for portions of the defects 27 (B-A ₁₎ is zero. On the other hand, (A ₂ -B) becomes negative in all values except for the defect 26, and FIX (A ₂ -B) becomes 0. By performing the above FIX function processing, the defects 26 and 27 can be accurately extracted.

Next, as shown in FIG. 12, magnitude comparison processing g, h is performed to determine defects 26, 27 of the image subjected to the FIX difference processing. This is done by comparing a predetermined threshold value i on the MAX side or a threshold value j on the MIN side with the gradation value of the difference-processed image. Defects are identified based on whether or not they exist. The comparison efficiency is output as defect information.

[0007]

A defect inspection of an inspection image is performed by the above-described picture inspection method, and quality control and process control of a printed material are performed. However, this method has the following problems. For example, as shown in FIG.
When a picture composed of black spots, white, and the like is picked up by a CCD camera for each pixel, a signal output as shown in (a) or (b) occurs, and the picture cannot be accurately captured. That is,
In the conventional pattern inspection method, a phase shift occurs between the pixel of the CCD camera and the pattern, and it is difficult to stably detect a defect having a visual field of one pixel of the CCD camera. Therefore, a so-called moiré phenomenon occurs in which a tone value (density value) is different each time a picture is taken in at the same place. As countermeasures against malfunctions due to this moiré phenomenon, imaging in an out-of-focus state where the focus of the camera is slightly shifted to remove the spatial frequency component causing moiré, and the place where moiré occurs is detected in advance and the place is detected from other places There is a method of loosening the threshold value of the defect determination. FIG. 7 shows a defect 28 of an image captured in a focused state.
FIG. 8 shows the relationship between the threshold 28 and the defect 28a of an image captured in an out-of-focus state by the former measure. As shown in FIG. 8, defects 28a in the countermeasure would be particularly blunt completely if the infinitesimal defects becomes impossible defect determination unless lower the threshold 29. However, there is a problem that when the threshold value is lowered, erroneous detection is likely to occur. On the other hand, in the case of the latter countermeasure, there is a problem that the algorithm for specifying the moiré is difficult, and the moiré is recognized as the moiré up to the portion of the pattern which is not the moiré, so that the judgment becomes unnecessarily weak.

[0008] The present invention has been made in view of the above circumstances, high detection capability of the infinitesimal defects suppresses moire, can improve reliability, and can improve the S / N of the image It is intended to provide a picture inspection method.

[0009]

SUMMARY OF THE INVENTION In order to achieve the above object, the present invention provides a procedure for generating a reference image in which a pattern serving as a reference for inspection is picked up by an out-of-focus camera and moire is suppressed. A procedure for generating an inspection image in which a pattern to be inspected is captured by a camera in an out-of-focus state and suppressing moiré, a comparison procedure for comparing the reference image and the inspection image with each other and generating a difference image between them, Threshold processing the difference image,
A pattern inspection method for performing a procedure of detecting a minute defect of about a pixel included in a pattern, before or after the comparison procedure, the reference image to be compared with each other and the inspection image or the difference image representing a comparison result. A pattern inspection method for performing area filtering processing for adding the density value of a peripheral pixel to the density value of a target pixel. More specifically, as a pre-process of the comparison procedure, a maximum value that replaces the density value of the target pixel with the maximum density value or the minimum density value among the density values of the target pixel and its neighboring pixels in the reference image. It is characterized by performing a filtering process or a minimum filtering process.

[0010]

First, a reference image and an inspection image to be inspected are imaged in an out-of-focus state to create a reference image and an inspection image. Next, the reference image is subjected to MAX filtering processing and MIN
Perform filtering processing. Next, area filtering processing of the reference image and the inspection image is performed, and information included in the neighboring pixels is taken into the target pixel. Although the luminance information is reduced by only one pixel of the image captured in the out-of-focus state, the information as the area is increased accordingly. Therefore, area filtering is performed to improve the defect detection accuracy. Further, the S / N ratio of the image is also improved by the addition processing of the neighboring pixels. A comparison procedure is performed for performing FIX difference processing on the reference image and the inspection image that have been processed. Next, threshold processing for comparing the gradation value of the processed image with a predetermined threshold value is performed to determine and output the defect.

[0011]

An embodiment of the present invention will be described below with reference to the drawings. FIG. 1 is a block diagram showing an omentum of a pattern inspection method according to the present invention, FIG. 2 is a block diagram for explaining the inspection method of this embodiment in detail, and FIG. 3 is an inspection method according to another embodiment. FIG. 4 is a block diagram for explaining, FIG. 4 is a plan view for explaining the contents of the area filtering process,
FIG. 5 is a diagram for explaining the area filtering process.

As shown in FIG. 1, an inspection pattern 1 and a reference pattern 2 are first imaged by a television camera or the like. In this case, the imaging process 3 is performed in an out-of-focus state. The out-of-focus imaging is a so-called blur imaging. As described above, the unfocused inspection image 4 and the similarly unfocused reference image 5 are created. The reference image 5 is subjected to pre-processing 6 using MAX and MIN filters. Next, an area filtering process 7 of the inspection image 4 and the reference image 5 subjected to the MAX and MIN filtering processes is performed. The two images 4 and 5 that have been subjected to this processing are subjected to FIX difference processing by the comparison procedure 8. The gradation value of the image after the difference processing (difference image) and a predetermined threshold 9
Are compared in the magnitude comparison processing 10. Defects included in the inspection pattern 1 are determined by comparing this difference value with a predetermined threshold (allowable value). In the above-described example, the case where the comparison procedure 8 is performed after the area filtering processing 7 has been described. However, as shown by the two-dot chain line route in FIG. May be performed first, the difference image is subjected to the area filtering processing 7, and then the magnitude comparison processing 10 is performed. In this case, the difference value is a signed difference and not an absolute value difference. This cancels the noise component.

Embodiment 1 FIG. 2 shows an embodiment in which an area filtering process 7 is performed before the FIX difference process (comparison procedure 8). In the figure, reference numeral 1
1 indicates a processing path of the inspection image, and reference numerals 12 and 13 indicate MA
4 shows a processing path of a reference image subjected to X filtering processing and MIN filtering processing. Inspection image processing path 1
At 1, the area filtering processing (FIG. 1) is directly performed by the n × n neighborhood adding circuit r. In this embodiment, n = 3. On the other hand, in the MAX filtering processing path 12, n ×
The filtering process is performed by n (3 × 3) MAX filters a, and the result is stored in the frame memory c.
Further, an area filtering process is performed by an n × n (3 × 3) neighborhood adding circuit p. Further, in the MIN filtering processing path 13, the filtering processing b by the 3 × 3 MIN filter b is performed, and the result is stored in the temporary frame memory d. Further, an area filtering process is performed by the 3 × 3 neighborhood adding circuit q. The inspection image and the reference image that have been subjected to the area filtering processing of the 3 × 3 neighborhood adding circuits r, p, and q are processed by the FIX difference circuits e and f, and the thresholds i and j are respectively determined by the magnitude comparison circuits g and h. The output is compared via a gate k and output k.

Next, the processing operation of the 3 × 3 neighborhood adding circuit will be described with reference to FIGS. As shown in FIG.
× 3 curve of the gradation values of the infinitesimal <br/> defect 14 around the inspection image before the area filtering process of the addition circuit is indicated by reference numeral 15. The gradation value of the defect 14 included in the inspection image is wide in area for out-of-focus imaging and is smaller than the threshold value 16, so that the defect cannot be detected. On the other hand, 3x
The curve of the gradation value of the inspection image around the defect 14 after the area filtering processing of the three-neighbor addition circuit is performed is indicated by reference numeral 17 by a two-dot chain line. As described above, by performing the area filtering, the area accuracy spread to the periphery can be utilized for the defect determination. Also, the S / N ratio of the image is improved by the area filtering. That is, in the defect determination, the area determination is more stable than the luminance value determination. On the other hand, FIG.
Indicates the content of the area filtering processing by the 3 × 3 area filter 18. In the figure, E corresponds to the center position of the defect 14 in FIG. 5, for example, and corresponds to the target pixel. A,
Pixels of B, C, D, F, G, H, and I correspond to peripheral pixels around E. The gradation value of each pixel of A to I is represented by A '
[E] = ( A '+ B' + C ', where [E] is the gradation value of the target pixel E after the area filtering processing.
+ D '+ E' + F '+ G' + H '+ I' ) / 9.
By performing the above processing for all the pixels, the area filtering processing of the out-of-focus image is performed.

Embodiment 2 FIG. 3 shows another embodiment 2 of the present invention. In the second embodiment, the reference image and the inspection image are processed by the FIX difference circuits e and f, and then the area filtering processing is performed by the n × n neighborhood adding circuits p and q. The other processing is the first embodiment shown in FIG.
Is the same as In this case, as described above, the FIX difference processing is a signed difference. This is because the noise component is not canceled by the absolute value difference. According to the second embodiment, substantially the same effects as in the first embodiment can be obtained. Further, the number of the n × n neighboring addition circuits can be reduced by one.

In the above, each embodiment has been described in connection with the case where the invention is applied to in-line printed matter, but it is needless to say that the present invention is not limited to this. Further, the area filter 18 is not limited to n = 3. Further, the picture 25 shown in FIG. 10 is for explaining the present invention in an easy-to-understand manner, and the present invention is applied to any other pictures. As the camera, for example, a video camera having a built-in CCD is used.

[0017]

According to the present invention, the following remarkable effects are obtained. 1) Moire can be suppressed, and the area judgment is adopted as the defect judgment instead of the judgment based on the luminance value of each pixel, so that the defect judgment can be performed accurately and stably. 2) defect, particularly high detection capability of the infinitesimal defects, thereby improving the reliability of the inspection. 3) The S / N of the image can be improved. 4) Since the defect judgment can be performed automatically, the quality control and the process control of the printed matter can be performed in-line.

[Brief description of the drawings]

FIG. 1 is a block diagram showing an omentum of an inspection method of the present invention.

FIG. 2 is a block diagram of a first embodiment of the present invention.

FIG. 3 is a block diagram of a second embodiment of the present invention.

FIG. 4 is an explanatory plan view for explaining an area filtering process in each embodiment.

FIG. 5 is a diagram for explaining the effect of the area filtering processing of each embodiment.

FIG. 6 is an explanatory diagram for explaining a moire phenomenon.

FIG. 7 is a diagram for explaining defect determination in a focused image.

FIG. 8 is a diagram for explaining defect determination in an out-of-focus image.

FIG. 9 is a configuration diagram showing an example of a printing line system to which the present invention is applied.

FIG. 10 is a plan view showing an example of a picture of a printed matter.

FIG. 11 is a diagram for explaining a MAX filtering process and a MIN filtering process.

FIG. 12 is a block diagram showing a reference example of a picture inspection method.

[Explanation of symbols]

Reference Signs List 1 inspection pattern 2 reference pattern 3 out-of-focus imaging processing 4 inspection image 5 reference image 6 preprocessing by MAX and MIN filters 7 area filtering processing 8 comparison procedure (FIX difference processing) 9 threshold 10 large / small comparison processing 11 inspection Image processing path 12 MAX filtering processing path of reference image 13 MIN filtering processing path of reference image 14 Defect 15 Tone value curve of inspection image 16 Threshold value 17 Tone value curve of inspection image after area filtering processing 18 Area filter 19 Unwinding web 20 Printing paper 21 Printing unit 22 Video camera 23 Processing circuit 24 Rotary encoder 25 Pattern 26 Defect 27 Defect

──────────────────────────────────────────────────続 き Continuation of front page (56) References JP-A-7-186375 (JP, A) JP-A-5-81407 (JP, A) JP-A-62-185466 (JP, A) (58) Survey Field (Int.Cl. ⁷ , DB name) G01N 21/84-21/958 G06T 1/00-7/00

Claims (2)

(57) [Claims] 1. A procedure for generating a reference image in which a pattern serving as an inspection reference is captured by a camera in an out-of-focus state and suppressing moiré, and a pattern to be inspected is captured using a camera in an out-of-focus state to remove moiré. A procedure for generating a suppressed inspection image, a comparison procedure for comparing the reference image and the inspection image with each other to generate a difference image between the two, and a threshold processing for the difference image, and processing the minute image such as a pixel included in a picture. In the pattern inspection method of performing a procedure for detecting a defect, before or after the comparison procedure,
A pattern inspection method for subjecting the reference image and the inspection image to be compared with each other or the difference image representing the comparison result to an area filtering process for adding a density value of a peripheral pixel to a density value of a pixel of interest. . 2. A maximum filtering for replacing a maximum density value or a minimum density value of density values of a pixel of interest and its neighboring pixels with a density value of the pixel of interest in a reference image as preprocessing of the comparison procedure. The picture inspection method according to claim 1, wherein a processing or a minimum filtering processing is performed.