JP2004219072A - Method and apparatus for detecting streak defect of screen - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To detect black streaks without being limited to white streaks and to properly detect streaks having various sizes. <P>SOLUTION: This apparatus for detecting the streak defect of the screen has pre-processing means 60-63 for imaging the screen of a target 1 to be inspected and extracting the image of a part to be inspected from the screen to perform flattening processing, a contracted image forming means 64 for forming contracted images of one or more stages from the pre-processed image, a line detecting filter processing means 65 for applying a line detecting filter in different directions in order to enhance the streaks with respect to each of the contracted images, a statistical data processing means 66 for calculating statistical data on the basis of the brightness values of pixels in the whole region of each image of which the streak defects are enhanced in the different directions and a defect candidate presence deciding means 67 for setting the threshold of the brightness value on the basis of the statistical data to extract a defect candidate from the statistical data and the threshold. The preprocessing means 60-63 take the difference between the image of the part to be inspected and a background image to form a background difference image for removing streaks other than the target to be inspected and removing the irregularity of the background difference image. <P>COPYRIGHT: (C)2004,JPO&NCIPI

Description

[0001]
TECHNICAL FIELD OF THE INVENTION
The present invention relates to a method and an apparatus for detecting streak defects on a screen that automatically and accurately detect streak defects in an inspection process in the manufacture of a display device such as a liquid crystal panel or a projector as an applied product thereof.
[0002]
[Prior art]
In a conventional line unevenness detection method in LCD panel inspection, each pixel of a grayscale image of an LCD panel is set as a target pixel, and for each target pixel, a vertical direction, a horizontal direction and at least two pixels are set within a predetermined range centered on the target pixel. Generating a line unevenness extraction image by performing a process of adding pixel values for each of the three oblique directions and performing a process of setting the maximum value of the calculation results as the pixel value of the pixel of interest. The method includes a step of detecting line unevenness by performing a binarization process on the unevenness extracting image using a predetermined threshold value (for example, see Patent Document 1).
[0003]
[Patent Document 1]
JP-A-10-240933 (page 1, FIG. 1)
[0004]
[Problems to be solved by the invention]
The line unevenness detection method in the conventional LCD panel inspection is based on the following method. For each target pixel of the grayscale image of the LCD panel, a pixel is set in a vertical direction, a horizontal direction, and at least two oblique directions within a predetermined range centered on the target pixel. A value addition operation is performed, and a process for setting the maximum value of the calculation results to the pixel value of the pixel of interest is performed, thereby generating a line unevenness extraction image and detecting line unevenness having a higher luminance than the background. Therefore, line unevenness lower in luminance than the background, for example, black stripes cannot be detected.
In general, it is necessary to extract a defect using a different threshold value for a thin streak and a thick streak. However, in the conventional example, since consideration is not given for each size, consistency with a visual inspection cannot be obtained. There was a problem.
[0005]
The present invention has been made in order to solve such a problem, and not only white streaks, but also black streaks can be detected, and a streak defect detection method and apparatus for a screen that can appropriately detect streaks of various sizes. The purpose is to get.
[0006]
[Means for Solving the Problems]
A method of detecting a streak defect on a screen according to the present invention includes the steps of reducing a captured image of a screen to be inspected into a plurality of stages to create a plurality of reduced images, and emphasizing a streak defect for each reduced image. Applying a line detection filter in different directions to detect, calculating statistical data based on luminance values of pixels in a predetermined area of each image in which streak defects are emphasized in different directions, Setting a threshold value of the luminance value based on the threshold value, and determining whether there is a defect candidate based on the statistical data and the threshold value.
[0007]
With this configuration, an image obtained by capturing an image of the screen to be inspected is reduced in a plurality of stages to create a plurality of reduced images, and different directions are used to emphasize and detect streak defects in each reduced image. Apply a line detection filter to, calculate statistical data based on the luminance value of the pixel in the entire region of each image in which the streak defect is emphasized in different directions, set a threshold value of the luminance value based on the statistical data, Since the presence or absence of a defect candidate is determined from the statistical data and the threshold value, it is possible to easily determine in a short time whether or not each image is a non-defective product having no white streaks and no streaks of various sizes.
[0008]
In addition, before performing the step of creating the reduced image, capturing a screen of the inspection target, extracting an image portion of the inspection target from the screen, and extracting the extracted image of the inspection target and the background image And a step of performing a flattening process to remove the influence of unevenness in the background difference image by providing a process of creating a background difference image in which the difference between Since the image reduced in the step of creating the image does not have any stripes caused by illumination or a lens other than the object to be inspected and has no influence of unevenness, the image obtained by the step of applying the line detection filter is a line defect of the object to be inspected. Only the emphasis will be.
[0009]
Further, in the screen streak defect detection method according to the present invention, the step of applying a line detection filter in the different direction includes a horizontal line detection filter that performs horizontal line enhancement processing and a vertical line detection filter that performs vertical line enhancement processing. This is performed by a filter, a + 45 ° oblique line detection filter, and a −45 ° oblique line detection filter.
With this configuration, it is possible to emphasize streak defects appearing horizontally, vertically, and diagonally on the screen.
[0010]
Further, in the streak defect detection method for a screen according to the present invention, the step of applying the line detection filter includes adding the median value of the gradation of the image as an offset value at the same time as applying the line detection filter.
With this configuration, for the image subjected to the line detection filter processing, statistical data based on the luminance value of each pixel is calculated for not only the white stripe but also the black stripe, and the white stripe and the black stripe are calculated based on the statistical data. The threshold value of the luminance value is set, and the presence or absence of a defect candidate is determined from the statistical data and these threshold values. Can be facilitated in a short time.
[0011]
Further, in the screen streak defect detection method according to the present invention, the statistical data in the step of calculating the statistical data includes obtaining a luminance value of each pixel in a predetermined region of each image in which the streak defect is enhanced in a different direction. Since the average value, the standard deviation, the maximum value, and the minimum value in the entire predetermined area obtained from the obtained luminance value are set, the threshold values of the luminance values of the white stripe and the black stripe are set based on the average value and the standard deviation. be able to.
[0012]
Furthermore, in the screen streak defect detection method according to the present invention, after the step of determining the presence or absence of the defect candidate, extraction of a white streak or black streak defect candidate from the image having the defect candidate is performed, A step of performing blob processing for obtaining data, and calculating a defect evaluation value by a predetermined formula based on evaluation value reference data of defect candidates of white stripes or black stripes obtained by the blob processing and statistical data of luminance values of the entire predetermined area. And a step of performing an evaluation value calculation process.
[0013]
With this configuration, for a defect candidate image, white stripe or black stripe defect candidates are extracted, and blob processing for obtaining evaluation value reference data is performed. An evaluation value calculation process for calculating a defect evaluation value by a predetermined formula based on the evaluation value reference data of the black streak defect candidate and the statistical data of the luminance value of the entire screen, thereby obtaining an evaluation value obtained by the evaluation value calculation process. Can determine the rank of the white streak or the black streak, the streak defect can be detected with high accuracy regardless of the size of the defect of the white streak or the black streak, and the target of the blob processing is the defect. Since only images with candidates are available, the calculation time is short, and the ranking of defects can be performed in a short time.
[0014]
Further, in the screen streak defect detection method according to the present invention, the evaluation value reference data in the step of performing the blob processing is a maximum luminance, a minimum luminance, and an area of a defect candidate of a white stripe or a black stripe, and the evaluation value calculation is performed. The defect evaluation value in the step of performing the process is determined by a predetermined formula based on information on the maximum luminance, minimum luminance, and area of the defect candidate of the white stripe or the black stripe, and the average value and the standard deviation of the luminance values of the entire predetermined region. Therefore, it is possible to quantitatively evaluate the degree of the white streak and the black streak defect in the defect candidate.
[0015]
In addition, the screen streak defect detection device according to the present invention includes a reduced image creating unit configured to reduce an image obtained by capturing an image of a screen to be inspected in a plurality of stages to create a plurality of reduced images; Line detection filter processing means for applying a line detection filter in different directions in order to emphasize and detect defects; and statistical data based on luminance values of pixels in a predetermined region of each image in which streak defects are enhanced in different directions. And a defect candidate presence / absence determining means for setting a threshold value of the luminance value based on the statistical data and determining the presence / absence of a defect candidate from the statistical data and the threshold value.
[0016]
With this configuration, the reduced image creating unit creates a plurality of reduced images by reducing the image obtained by capturing the screen of the inspection target in a plurality of stages, and the line detection filter processing unit applies a stripe to each reduced image. Line detection filters are applied in different directions in order to highlight and detect defects, and the statistical data processing means generates statistical data based on the luminance values of pixels in all regions of each image in which streak defects are emphasized in different directions. Calculation, the defect candidate presence / absence determining means sets a threshold value of the luminance value based on the statistical data, and determines whether or not there is a defect candidate from the statistical data and the threshold value. It can be easily determined in a short time whether the product is a non-defective product having no streak.
[0017]
Further, before the reduced image creating means creates a reduced image, the image input means captures a screen of the inspection target, and the display area extracting means extracts a screen portion of the inspection target from the image, and sets The difference processing unit extracts the difference between the image of the inspected portion and the background image, and creates a background difference image from which a luminance change caused by something other than the object to be inspected is removed. Is performed, the image reduced by the reduced image creating unit has no lines generated by objects other than the object to be inspected, and is not affected by unevenness. Therefore, a line detection filter is applied by the line detection filter processing unit. The image is an image in which only the streak defect to be inspected is emphasized.
[0018]
Further, in the screen streak defect detection device according to the present invention, the line detection filter processing means includes a horizontal line detection filter that performs horizontal line enhancement processing, a vertical line detection filter that performs vertical line enhancement processing, An oblique line detection filter and a -45 ° oblique line detection filter are used.
With this configuration, it is possible to emphasize streak defects appearing horizontally, vertically, and diagonally on the screen.
[0019]
Further, in the screen streak defect detecting apparatus according to the present invention, the line detection filter processing means applies a line detection filter and simultaneously adds a median value of the gradation of the image as an offset value.
With this configuration, statistical data based on the luminance value of each pixel is calculated for the image subjected to the line detection filter processing, and a threshold value of the luminance value is set for not only the white stripe but also the black stripe based on the statistical data. Since the defect candidates are extracted from these threshold values, it is not necessary to separately perform the line detection filter processing that requires a long processing time for the white stripe and the black stripe, and the processing time can be reduced.
[0020]
Further, in the screen streak defect detection device according to the present invention, the statistical data calculated by the statistical data processing means obtains a luminance value of each pixel in a predetermined area of each image in which the streak defect is enhanced in a different direction. Since the average value, standard deviation, maximum value, and minimum value of the entire screen obtained from the obtained luminance value are used, the threshold values of the luminance values of the white stripe and the black stripe should be set based on the average value and the standard deviation. Can be.
[0021]
In the screen streak defect detecting apparatus according to the present invention, extraction of a white streak or black streak defect candidate and blob for obtaining evaluation value reference data are performed on an image having a defect candidate determined by the defect candidate presence / absence determining means. Blob processing means for performing the processing, evaluation value reference data of the white stripe or black streak defect candidate obtained by the blob processing means, and statistical data of the luminance value of the entire predetermined area by a predetermined formula.
Evaluation value processing means for performing an evaluation value calculation process for calculating a defect evaluation value.
[0022]
With such a configuration, the blob processing unit performs defect extraction of a white stripe or a black stripe and performs blob processing for obtaining evaluation value reference data, and thereafter, the evaluation value processing unit performs the blob processing obtained by the blob processing. Or, by performing an evaluation value calculation process of calculating a defect evaluation value by a predetermined formula based on the evaluation value reference data of the defect candidate of the black streak and the statistical data of the luminance value of the entire predetermined area, the evaluation value calculation process is performed. Based on the evaluation value, the rank of white streak or black streak defect can be determined, streak defects can be detected with high accuracy regardless of the size of the defect in the white streak or black streak, and the target of blob processing Is only an image with a defect candidate, the calculation time is short, and the defect can be ranked in a short time.
[0023]
Further, in the screen streak defect detection device according to the present invention, the evaluation value reference data obtained by the blob processing means by the blob processing is a maximum luminance, a minimum luminance, and an area of a defect candidate of a white streak or a black streak. The defect evaluation value for which the evaluation value processing means performs the evaluation value calculation process is based on information on the maximum luminance, the minimum luminance, and the area of the defect candidate of the white stripe or the black stripe, and the average value and the standard deviation of the luminance values of the entire predetermined area. Thus, the degree of the white streak and the black streak defect in the defect candidate can be quantitatively evaluated.
[0024]
BEST MODE FOR CARRYING OUT THE INVENTION
FIG. 1 is a block diagram showing a configuration of a screen streak defect detection apparatus according to Embodiment 1 of the present invention.
In FIG. 1, reference numeral 1 denotes a liquid crystal light valve to be inspected, and reference numeral 2 denotes a projector, which is an image projection device. The liquid crystal light valve 1 can be set from outside. Reference numeral 3 denotes a pattern generator, which is a pattern generation device for outputting various patterns to the liquid crystal light valve 1. Reference numeral 4 denotes a screen. Reference numeral 5 denotes a CCD camera, which is an imaging means for photographing an image projected on the screen 4. A CCD having a resolution higher than the resolution is mounted.
Reference numeral 6 denotes a computer device that controls the pattern generator 3 and the CCD camera 5 and detects a streak defect of the liquid crystal light valve 1. Reference numeral 7 denotes a display device connected to the computer device 6.
[0025]
The computer device 6 includes an image input unit 60, a display area extraction unit 61, a background image difference processing unit 62, a flattening processing unit 63, a reduced image creation unit 64, a line detection filter processing unit 65, and statistical data. It comprises a calculating means 66, a defect candidate presence / absence determining means 67, a blob processing means 68, and an evaluation value processing means 69.
Image data of a captured image captured by the CCD camera 5 is input to the image input means 60 of the computer device 6. The captured image is stored in storage means (not shown).
The display area extracting unit 61 extracts only the image portion of the inspection target part from the captured image.
The background image difference processing unit 62 obtains a difference between the image in the display area and the previously created background image to obtain a background difference image from which streaks caused by objects other than the inspection target are removed.
[0026]
The flattening processing unit 63 removes the influence of unevenness in a wide range of the background difference image.
The reduced image creating unit 64 creates a reduced image of a plurality of stages from the flattened image.
The line detection filter processing unit 65 applies a line detection filter in a different direction to each reduced image to emphasize and detect a streak defect.
The statistical data calculation means 66 calculates an average value, a standard deviation, a maximum value, and a minimum value of the brightness values of the entire screen from the brightness values of each pixel in the entire region of the image in which the streak defect is emphasized.
[0027]
The defect candidate presence / absence determination means 67 sets threshold values of white stripes and black stripes based on the average value, standard deviation, maximum value, and minimum value of the luminance value, and determines the presence / absence of a defect candidate from these values and the threshold values. I do.
The blob processing means 68 extracts a white streak or a black streak as a defect candidate and obtains the maximum luminance, the minimum luminance, and the area.
The evaluation value processing means 69 determines the defect by a predetermined formula based on the information of the maximum luminance, the minimum luminance, the area of the defect candidate of the white stripe or the black stripe obtained by the blob processing, the average value of the luminance value of the entire screen, and the standard deviation. Calculate the evaluation value.
[0028]
Next, the operation of the streak defect detection device according to the first embodiment of the present invention will be described.
FIG. 2 is a flowchart for explaining the operation of the streak defect detection device according to the first embodiment. The operation shown in FIG. 2 is realized by a program executed on the computer device 6.
First, the liquid crystal light valve 1 to be inspected is set in the projector 2, the pattern generator 3 is controlled by the computer 6 to display a pattern of a specific brightness on the liquid crystal light valve 1, and the pattern is displayed on the screen 4 by the projector 2. Projected onto Then, the image projected on the screen 4 is photographed by the CCD camera 5, the image of the photographed data is output to the computer device 6, and the computer device 6 performs a streak defect detection process to detect a streak defect of the liquid crystal light valve 1. The detection result is displayed on the display device 7.
[0029]
Here, the operation of the streak defect detection by the computer device 6 will be described based on the flowchart of FIG.
First, an image projected on the screen 4 is photographed by the CCD camera 5, and an image of the photographed data is taken into the image input means 60 of the computer device 6. At this time, the photographed data is converted by an A / D converter (not shown). , And 4096-gradation digital data are taken into the computer device 6 (step S1).
Next, the display area extracting means 61 extracts a display area for extracting only the screen portion of the inspected portion from the captured image data (step S2).
This extraction screen is a pattern matching process for the coordinates of the four corners of the image of the part to be inspected (for four small areas of several tens of pixels × several tens of pixels near the four corners of the image data, four corner reference images prepared in advance and The pattern can be extracted by performing pattern matching processing and specifying the coordinates of the four corners).
[0030]
Subsequently, the background image difference processing means 62 performs a background image difference process for removing a defective luminance change caused by something other than the liquid crystal light valve 1 such as an illumination or a lens (step S3).
In the background image difference processing, the background image shown in FIG. 3B is subtracted from the image of the inspection target image data shown in FIG. The background difference image shown in FIG. 3C is created by adding 2048 (a value of 1/2 of 4096 gradations) as an offset value so as not to be a negative value. The image is a difference image corresponding to each pixel in the image. Note that this background image is created by imaging a plurality of samples having as few defects as possible, creating an averaged image thereof, and extracting only the screen portion of the inspected portion from the image.
[0031]
Note that the graph shown in FIG. 5A is a dotted line of the image before the background difference processing, which is an image in which only the screen portion of the inspected part in FIG. It is a plot of luminance values in a portion. The graph shown in FIG. 5B is obtained by plotting the luminance values at the dotted line portions of the background image in FIG. 4B similar to FIG. 3B. The graph shown in (c) of FIG. 5 is obtained by plotting the luminance values at the dotted line portions of the image after the background difference processing in (c) of FIG. 4 similar to (c) of FIG. 4 and 5, it can be seen that the background image difference processing removes defective luminance changes caused by something other than the liquid crystal light valve 1, such as illumination and lenses.
[0032]
Next, the flattening processing unit 63 performs a flattening process on the background difference image subjected to the background image difference process (Step S4). The flattening process is a process for flattening a gradual change in luminance of the background difference image subjected to the background image difference process, and is performed to remove the influence of unevenness over a relatively wide range.
Such flattening processing is performed by filter processing or morphology processing.
[0033]
Further, the reduced image creating means 65 performs a size changing process for reducing the flattened image (step S5).
As shown in FIG. 6, this size change process is to create a reduced image of a flattened image in five stages of 1/2, 1/4, 1/8, 1/16 and 1/32. is there. For example, a 1/2 reduced image is created by setting an average value of four pixels of an image subjected to a flattening process as one pixel. In addition, a 1/4 reduced image is created with an average value of four pixels of the 1/2 reduced image as one pixel.
[0034]
Therefore, assuming that the flattened image is 1200 × 1000 and has 1.2 million pixels, a 1/2 reduced image is 600 × 500 and 300,000 pixels, and a 縮小 reduced image is 300 × 250 and 75,000 pixels. , 1/8 reduced image is 150 × 125, 18,750 pixels, 1/16 reduced image is 75 × 62, 4650 pixels, and 1/32 reduced image is 38 × 31, 1178 pixels.
As described above, the five-step reduced image is created from the flattened image because the line detection filter described later can only detect a streak defect of a predetermined size by emphasizing it. This is to detect streak defects of various sizes.
[0035]
Next, the line detection filter processing means 65 performs a line detection filter process on each of the reduced images of the five stages (step S6). In this line detection filter processing, it is difficult to detect minute-level white / black streak / labis-judge defects as it is. Therefore, three lines of one reduced image are emphasized so as to emphasize only streak defects in the image. Are made, and one type of line detection filter is applied to each image, and a defect emphasis calculation is performed using a total of four types of line detection filters.
[0036]
The four types of line detection filters include a horizontal line detection filter that performs horizontal line enhancement processing, a vertical line detection filter that performs vertical line enhancement processing, and an oblique line detection filter that performs + 45 ° line enhancement processing. , An oblique line detection filter that performs a line emphasis process of −45 °.
Accordingly, three copies of each of the five reduced images shown in FIG. 8A are created, and one type of line detection filter processing is performed on each of the images. As shown in FIG. 8B, four images obtained by performing horizontal line detection processing, vertical line detection processing, + 45 ° line detection processing, and −45 ° line detection processing from each reduced image are obtained, and a total of 20 images are obtained. An image in which the streak defect is enhanced is obtained.
In the above description, the image subjected to the size change processing by the reduced image creation unit 64 is an image that has been flattened. However, the image captured by the image input unit 60 is immediately reduced by the reduced image creation unit 64, It goes without saying that even if the line detection filter processing is performed by the line detection filter processing means 65, the defect emphasis calculation can be performed.
[0037]
All of these four types of line detection filters detect a vertical line, a horizontal line, and + 45 ° as a detection target for a small area of several pixels × several pixels (7 × 7 pixels in FIG. 7) including the periphery of the pixel of interest. In order to detect whether there is a diagonal line or a component of a −45 ° diagonal line, when the component exists, it is emphasized by a convolution operation based on the relationship between the luminance value of the pixel of interest and the surrounding pixels. Is a filter in which the weighting is performed.
7A is a horizontal line detection filter that emphasizes horizontal lines, FIG. 7B is a vertical line detection filter that emphasizes vertical lines, and FIG. 7C is a diagonal line that emphasizes + 45 ° lines. FIG. 7D shows another example of a diagonal line detection filter that emphasizes a line of −45 °.
[0038]
In the image to which the four types of line detection filters are applied as shown in FIG. 7, white streaks appear as gradations having a positive value, whereas black streaks appear as gradations having a negative value. In the image processing format, the image data usually takes only a positive value, so the black streak component becomes 0 as it is, and the processed image data cannot be detected because there is no black streak data. In this manner, the graph in which the offset value is not added as shown in FIG. 9A indicates that white streaks appear but black streaks do not appear.
Therefore, when the screen is represented by 12 bits of 4096 gradations, half the value of 2048 is added as an offset value to the result of the filter processing so that a black streak can be detected from the same image. As a result, the data of the black streak also appears as a gradation having a positive value, so that the white streak and the black streak can be detected by one filtering process. The graph to which the offset value is added as shown in FIG. 9B indicates that both white stripes and black stripes appear. When the image format is an 8-bit gray scale, 256 gradations are obtained, and a half value of 128 is added to the result of the filter processing as an offset value.
[0039]
Next, the statistical data calculating means 66 calculates the respective pixels in the entire region of the image in which the 20 streak defects obtained by performing the four types of line detection filter processing on the reduced images of the five stages are emphasized. , And a statistical data calculation process for obtaining the average value, standard deviation, maximum value, and minimum value of the entire screen is performed (step S7).
That is, for each of the five reduced images subjected to the horizontal line detection processing, the luminance value of each pixel in the entire area is obtained, and the average value, standard deviation, maximum value, and minimum value of the luminance value of the entire screen are obtained. In addition, for each of the five reduced images subjected to the vertical line detection processing, the luminance value of each pixel in the entire area is obtained, and the average value, standard deviation, maximum value, and minimum value of the entire screen are obtained. Further, with respect to each of the five reduced images subjected to the + 45 ° oblique line detection processing, the luminance value of each pixel in the entire area is obtained, and the average value, standard deviation, maximum value, and minimum value of the entire screen are obtained. Furthermore, for each of the five reduced images subjected to the -45 ° oblique line detection processing, the luminance value of each pixel in the entire area is obtained, and the average value, standard deviation, maximum value, and minimum value of the entire screen are obtained. .
[0040]
Then, the defect candidate presence / absence determination means 67 outputs the average value, standard value, and standard value obtained from the 20 images that have been subjected to the horizontal line detection process, the vertical line detection process, the + 45 ° oblique line detection process, and the −45 ° oblique line detection process. The horizontal line threshold, the vertical line threshold, the + 45 ° oblique line threshold, and the −45 ° oblique line threshold of each image are calculated by the following equations based on the deviation, the maximum value, and the minimum value.
Horizontal line threshold = average (average value of horizontal line detection processing image) ± a ₁ * Σ (standard deviation of horizontal line detection processed image)
Vertical line threshold = average (average value of vertical line detection processed image) ± a ₂ * Σ (standard deviation of vertical line detection processed image)
+ 45 ° oblique line threshold = average (average value of + 45 ° oblique line detection processed image) ± a ₃ * Σ (standard deviation of + 45 ° oblique line detection processed image)
−45 ° oblique line threshold = average (average value of −45 ° oblique line detection processed image) ± a ₄ * Σ (standard deviation of -45 ° oblique line detection processed image)
a ₁ , A ₂ , A ₃ , A ₄ Is a fixed constant.
Note that two values are calculated as thresholds as a calculation result of one formula. The threshold obtained by + is a threshold for white line detection, and a line detection processing image that is equal to or more than this threshold is a white line. The threshold value detected as a defect candidate and obtained with-becomes a threshold value for black streak detection, and a line streak image that is equal to or less than the threshold value is detected as a black streak defect candidate.
[0041]
However, before that, it is checked whether there is a streak defect candidate in the line detection processing image, that is, the maximum value and the minimum value of the luminance value of the entire screen obtained from each image subjected to the horizontal line detection processing, the vertical line detection processing is performed. The maximum value and the minimum value of the luminance value of the entire screen acquired from each image, the maximum value and the minimum value of the luminance value of the entire screen acquired from each image subjected to the + 45 ° oblique line detection processing, and the −45 ° oblique line detection processing is performed. Based on the maximum and minimum values of the luminance value of the entire screen obtained from each of the images and the calculated threshold value, it is determined whether or not a candidate for a streak defect exists in each detection processing screen (step S8).
[0042]
For each image subjected to the horizontal line detection processing, if the maximum value of the luminance value of the image exceeds the horizontal line threshold (+ calculation), the image is regarded as a defect candidate having a white stripe, and the next particle analysis processing (blob processing) is performed. Proceed to).
If the minimum value of the luminance value of the image does not exceed the horizontal line threshold (−calculation), the image is processed as a black streak image and the process proceeds to the next blob processing. When the maximum value of the luminance value does not exceed the horizontal line threshold value (+ calculation) and the minimum value of the luminance value exceeds the horizontal line threshold value (−calculation), it is determined that there is no white line or black line and that it is a good product. The next blob processing is not performed.
[0043]
Also, for each image subjected to the vertical line detection processing, when the maximum value of the luminance value of the image exceeds the vertical line threshold (+ calculation), the image is regarded as a defect candidate having a white stripe, and the next blob processing is performed. Proceed to.
If the minimum value of the luminance value of the image does not exceed the vertical line threshold value (-calculation), the image is processed with the black streak and the process proceeds to the next blob processing. When the maximum value of the luminance value does not exceed the horizontal line threshold value (+ calculation) and the minimum value of the luminance value exceeds the horizontal line threshold value (−calculation), it is determined that there is no white line or black line and that it is a good product. The next blob processing is not performed.
[0044]
Also, for each image subjected to the + 45 ° oblique line detection processing, if the maximum value of the luminance value of the image exceeds the + 45 ° oblique line threshold (+ calculation), it is determined as a defect candidate having a white stripe. Proceed to the next blob processing.
If the minimum value of the luminance value of the image does not exceed the + 45 ° oblique line threshold value (−calculation), the process proceeds to the next blob process as an image having black stripes. If the maximum value of the luminance value does not exceed the + 45 ° oblique line threshold (+ calculation) and the minimum value of the luminance value exceeds the + 45 ° oblique line threshold (−calculation), white stripes and black stripes The next blob processing is not performed as a non-defective product.
[0045]
Furthermore, for each of the images subjected to the -45 ° oblique line detection processing, if the maximum value of the luminance value of the image exceeds the -45 ° oblique line threshold (+ calculation), the defect candidate having a white stripe is detected. To the next blob processing.
If the minimum value of the luminance value of the image does not exceed the -45 ° oblique line threshold value (−calculation), the image is processed with the black streak and the process proceeds to the next blob processing. If the maximum value of the luminance value does not exceed the −45 ° oblique line threshold (+ calculation) and the minimum value of the luminance value exceeds the −45 ° oblique line threshold (−calculation), white streaks, The next blob treatment is not performed as a non-defective product having no black stripes.
[0046]
Next, the blob processing unit 68 extracts a defect candidate only for an image for which it is determined that there is a white stripe or black stripe defect candidate, and obtains an evaluation value thereof. In the image, the vertical line detection processing image, the + 45 ° oblique line detection processing image, and the −45 ° oblique line detection processing image, the blob processing for obtaining the maximum luminance, the minimum luminance, and the area of the defect candidate of the white stripe or the black stripe is performed. (Step S9).
In order to perform the blob processing, the image must be binarized. For this purpose, a horizontal line threshold, a vertical line threshold, a + 45 ° oblique line threshold, and a −45 ° oblique line threshold are used. FIG. 10 shows an image binarized to perform the blob processing.
[0047]
For example, if it is determined that there is a white stripe defect candidate in the image subjected to the horizontal line detection processing, the horizontal line threshold calculated with + is used, and the portion beyond that is binarized as a white stripe defect candidate. In the binarized image, a plurality of defect candidates exist as clusters, and the area of all of them is determined, and the image before binarization is applied within the range of the binarized cluster region. From the maximum luminance value.
If it is determined that there is a black streak defect candidate in the image subjected to the horizontal line detection processing, the horizontal line threshold calculated by-is used, and the portion below the threshold is binarized as a black streak defect candidate. As in the case of the white stripe, the binarized image has a plurality of defect candidates in clusters. The area of all of them is determined, and within the range of the binarized cluster, the area is determined. The minimum luminance value is obtained from the image before the value conversion.
[0048]
Further, if it is determined that there is a white stripe defect candidate in the image subjected to the vertical line detection processing, the vertical line threshold calculated with + is used, and the portion beyond that is binarized as a white stripe defect candidate. In the binarized image, a plurality of defect candidates exist as clusters, and the area of all of them is determined, and the image before binarization is applied within the range of the binarized cluster region. From the maximum luminance value.
If it is determined that there is a black streak defect candidate in the image subjected to the vertical line detection processing, the vertical line threshold calculated by-is used, and the portion below the threshold value is binarized as a black streak defect candidate. As in the case of the white stripe, in the binarized image, a plurality of defect candidates exist as clusters, and the area is determined for all of them, and within the range of the binarized cluster region, The minimum luminance value is obtained from the image before binarization.
[0049]
Further, if it is determined that there is a white streak defect candidate in the image subjected to the + 45 ° oblique line detection processing, the + 45 ° oblique line threshold calculated by + is used, and the portion beyond that is regarded as a white streak defect candidate by 2 Value. In the binarized image, a plurality of defect candidates exist as clusters, and the area of all of them is determined, and the image before binarization is applied within the range of the binarized cluster region. From the maximum luminance value.
If it is determined that there is a black streak defect candidate in the image subjected to the + 45 ° oblique line detection processing, the + 45 ° oblique line threshold calculated by − is used, and the portion below that is regarded as a black streak defect candidate. Value. As in the case of the white stripe, in the binarized image, a plurality of defect candidates exist as clusters, and the area is determined for all of them, and within the range of the binarized cluster region, The minimum luminance value is obtained from the image before binarization.
[0050]
Further, if it is determined that there is a white streak defect candidate in the image subjected to the −45 ° oblique line detection processing, the −45 ° oblique line threshold calculated by + is used, and the portion beyond that is used as the white streak defect candidate. And binarized. In the binarized image, a plurality of defect candidates exist as clusters, and the area of all of them is determined, and the image before binarization is applied within the range of the binarized cluster region. From the maximum luminance value.
If it is determined that there is a black streak defect candidate in the image subjected to the −45 ° oblique line detection processing, the −45 ° oblique line threshold calculated by − is used, and the portion below that is used as the black streak defect candidate. And binarized. As in the case of the white stripe, in the binarized image, a plurality of defect candidates exist as clusters, and the area is determined for all of them, and within the range of the binarized cluster region, The minimum luminance value is obtained from the image before binarization.
[0051]
Then, the evaluation value processing means 69 outputs a white line or a black line in the horizontal line detection processing image, the vertical line detection processing image, the + 45 ° oblique line detection processing image, and the −45 ° oblique line detection processing image obtained by the blob processing. A calculation process of an evaluation value for calculating a defect evaluation value based on the information on the maximum luminance, the minimum luminance, and the area of the defect candidate, and the average value and the standard deviation of the luminance values of the entire screen is performed (step S10).
The defect evaluation value (Ev) of a white streak or a black streak in a defect candidate is obtained by calculation according to the following equation.
White line
Ev (i, n) = K (i) * S (n) * (Lmax (n) -Lave (i)) / σ (i)
Black stripe
Ev (i, n) = K (i) * S (n) * (Lave (i) -Lmin (n)) / σ (i)
K (i) = reduced screen coefficient, i = screen number, n = blob number,
Lmax (n) = maximum luminance of defect candidate, Lmin (n) = minimum luminance of defect candidate,
S (n) = Area of defect candidate, Lave (i) = Average luminance of entire screen,
σ (i) = standard deviation of luminance of the whole screen
Then, the maximum value of all the evaluation values is obtained, and the rank of the white streak or black streak defect of the panel to be inspected is determined based on this value.
[0052]
According to this embodiment, the computer device 6 first compares only the screen portion of the part to be inspected from among the images captured by the CCD camera 5 including the entire screen of the liquid crystal light valve 1 to be inspected. Performs a display area extraction process to extract, performs a background image difference process to remove streaks caused by something other than the liquid crystal light valve, and then performs the background image difference process on the background difference image to remove the effects of unevenness over a relatively wide range. A flattening process for removing, and a size changing process for reducing the flattened image in five stages, and a defect of a horizontal line, a vertical line, and a diagonal line of ± 45 ° with respect to the reduced image of five stages, respectively. An image in which line detection filter processing is performed by four types of line detection filters in order to emphasize and detect, and 20 streak defects obtained by such processing are emphasized. , The median value of the gradation of the image is added as an offset value, the brightness value of each pixel in each of the entire regions is obtained, and the average value, standard deviation, maximum value, and minimum value of the brightness value of the entire screen are obtained. Performs statistical data calculation processing, sets horizontal, vertical, and diagonal thresholds to determine white stripes from the average value and standard deviation obtained in the statistical data calculation processing, and sets horizontal, vertical, and diagonal lines to determine black stripes Set a threshold, horizontal line to determine the maximum value of the screen brightness value these white lines, vertical line, perform a primary determination whether there is no white line defect in the screen by whether or not exceeds the diagonal line threshold, Since the minimum value of the screen luminance value exceeds these horizontal lines, vertical lines, and oblique line thresholds for determining these black stripes, the primary determination is made as to whether there is a black stripe defect on the screen. There are no white or black streaks in the image. , It can facilitate determining further whether there is no good streaks of various sizes in a short time.
[0053]
Further, the image of the defect candidate determined to have a defect is subjected to blob processing for obtaining the maximum luminance, the minimum luminance, and the area of the white streak or the black streak of the defect candidate as preprocessing of the evaluation value calculation. Evaluation for calculating a defect evaluation value by a predetermined formula based on information on the maximum luminance, minimum luminance, and area of the white or black streak of the defect candidate obtained by the blob processing, and the average value and standard deviation of the luminance value of the entire screen. Since the value calculation process is performed and the rank of the white streak or black streak defect is determined by the maximum value of the evaluation values obtained by the evaluation value calculation process, regardless of the size of the defect size of the white streak or the black streak, A streak defect can be detected with high accuracy, and since the blob processing is performed only on images with defect candidates, the calculation time is short, and the defect ranking is performed in a short time. Has become can be.
[0054]
In this embodiment, the inspection object is described as a liquid crystal light valve. However, display parts such as a liquid crystal panel, a plasma display, an organic EL display, and a DMD (direct mirror device), and a display device and a product inspection using the same are provided. Can also be applied.
[Brief description of the drawings]
FIG. 1 is a block diagram illustrating a configuration of a screen streak defect detection device according to an embodiment.
FIG. 2 is a flowchart for explaining the operation of the streak defect detection device on the same screen.
FIG. 3 is a diagram showing a background image difference process of the streak defect detection device on the same screen.
FIG. 4 is a diagram showing, with dotted lines, cutout positions of luminance values in a front image, a background image, and a rear image of the background image difference processing of the streak defect detection device on the same screen.
FIG. 5 is a graph showing a luminance value cut out from each image of FIG. 4;
FIG. 6 is a diagram showing a procedure for creating a reduced image of the streak defect detection device on the same screen.
FIG. 7 is a diagram showing examples of various line detection filters used in the streak defect detection device on the same screen.
FIG. 8 is a diagram showing an image subjected to line detection filtering processing by the streak defect detection device on the same screen.
FIG. 9 is a graph showing luminance values of various images subjected to line detection filtering processing by the streak defect detection device on the same screen.
FIG. 10 is a view showing an image binarized for performing a blob process in the streak defect detection device on the same screen.
[Explanation of symbols]
1. Liquid crystal light valves to be inspected, 2 projectors, 3 pattern generators, 3 screens, 5 CCD cameras, 6 computer devices, 7 display devices, 64 reduced image creation means, 65 line detection filter processing means, 66 statistical data calculation means, 67 Defect candidate presence / absence determination means.

Claims (16)

Creating a plurality of reduced images by reducing the image of the screen of the inspection target in a plurality of stages,
Applying line detection filters in different directions to highlight and detect streak defects for each reduced image;
Calculating statistical data based on luminance values of pixels in a predetermined region of each image in which streak defects are emphasized in different directions,
Setting a threshold value of the luminance value based on the statistical data, and determining the presence or absence of a defect candidate from the statistical data and the threshold value;
A streak defect detection method for a screen. Before performing the step of creating the reduced image, imaging a screen of the object to be inspected, and extracting an image portion of the inspected portion from the screen,
A step of creating a background difference image from which a difference between the extracted image of the inspected part and the background image is removed to remove a luminance change caused by other than the inspected object;
Performing a flattening process for removing the effect of unevenness in the background difference image;
2. The method according to claim 1, further comprising the step of: The step of applying a line detection filter to the different directions includes a horizontal line detection filter that performs horizontal line enhancement processing, a vertical line detection filter that performs vertical line enhancement processing, a + 45 ° oblique line detection filter, and −45 degrees. 3. The method for detecting streak defects on a screen according to claim 1 or 2, wherein the method is performed by a filter for detecting an oblique line of angle. 4. The streak defect on a screen according to claim 1, wherein the step of applying the line detection filter adds the median of the gradation of the image as an offset value at the same time as applying the line detection filter. Detection method. The statistical data in the step of calculating the statistical data is obtained by obtaining the luminance value of each pixel in a predetermined region of each image in which a streak defect is enhanced in different directions, and averaging the entire predetermined region obtained from the obtained luminance value. 5. The method according to claim 1, wherein the values are a value, a standard deviation, a maximum value, and a minimum value. After the step of determining the presence or absence of the defect candidate, a step of extracting a white stripe or black stripe defect candidate and performing a blob process of obtaining evaluation value reference data for the image with the defect candidate,
performing an evaluation value calculation process of calculating a defect evaluation value by a predetermined formula based on evaluation value reference data of a defect candidate of a white stripe or a black stripe obtained by the blob process and statistical data of luminance values of the entire predetermined region;
The method for detecting a streak defect on a screen according to any one of claims 1 to 5, further comprising: 7. The method according to claim 6, wherein the evaluation value reference data in the step of performing the blob processing is a maximum luminance, a minimum luminance, and an area of a defect candidate of a white stripe or a black stripe. The defect evaluation value in the step of performing the evaluation value calculation processing is determined based on information on the maximum luminance, minimum luminance, and area of the defect candidate of the white stripe or the black stripe, and the average value and the standard deviation of the luminance values of the entire predetermined region. 7. The screen streak defect detecting method according to claim 6, wherein the method is obtained by the following equation. Reduced image creation means for creating a plurality of reduced images by reducing the image of the screen of the inspection target in a plurality of stages,
Line detection filter processing means for applying a line detection filter in different directions to enhance and detect streak defects in each reduced image;
Statistical data processing means for calculating statistical data based on luminance values of pixels in a predetermined area of each image in which streak defects are emphasized in different directions,
A defect candidate presence / absence determination unit that sets a threshold value of the luminance value based on the statistical data, and determines the presence / absence of a defect candidate from the statistical data and the threshold value.
A streak defect detection device for a screen, comprising: Inspection unit extracting means for imaging a screen of the inspection target and extracting an image portion of the inspection target from the screen,
Background difference processing means for taking a difference between the extracted image of the part to be inspected and the background image and creating a background difference image from which a luminance change caused by other than the object to be inspected is removed;
Flattening processing means for performing flattening processing for removing the influence of unevenness in the background difference image and sending the reduced image creating means;
10. The screen streak defect detecting device according to claim 9, further comprising: The line detection filter processing means includes a horizontal line detection filter that performs horizontal line enhancement processing, a vertical line detection filter that performs vertical line enhancement processing, a + 45 ° oblique line detection filter, and a −45 ° oblique line detection filter. 11. The screen streak defect detecting apparatus according to claim 9, wherein: 12. The screen streak defect detecting apparatus according to claim 9, wherein said line detection filter processing means adds a median value of gradation of the image as an offset value at the same time as applying a line detection filter. The statistical data calculated by the statistical data processing means obtains a luminance value of each pixel in a predetermined area of each image in which a streak defect is enhanced in a different direction, and calculates an average over the entire predetermined area obtained from the obtained luminance value. 13. The screen streak defect detecting apparatus according to claim 9, wherein the detected values are a value, a standard deviation, a maximum value, and a minimum value. Blob processing means for extracting a white streak or black streak defect candidate and performing blob processing for obtaining evaluation value reference data for an image having a defect candidate determined by the defect candidate presence / absence determining means;
Evaluation value for performing an evaluation value calculation process of calculating a defect evaluation value by a predetermined formula based on evaluation value reference data of a defect candidate of a white stripe or a black stripe obtained by the blob processing means and statistical data of luminance values of the entire predetermined area. Processing means;
14. The screen streak defect detecting device according to claim 9, wherein 15. The screen streak defect detection apparatus according to claim 14, wherein the evaluation value reference data obtained by the blob processing means by the blob processing is a maximum luminance, a minimum luminance, and an area of a defect candidate of a white stripe or a black stripe. . The defect evaluation value for which the evaluation value processing means performs the evaluation value calculation processing includes the maximum luminance, the minimum luminance, the information of the area of the defect candidate of the white stripe or the black stripe, the average value of the luminance value of the entire predetermined area, and the standard deviation. 15. The screen streak defect detecting apparatus according to claim 14, wherein the screen streak defect is obtained by a predetermined formula based on the formula.

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