JP2004219176A - Method and apparatus for detecting pixel irregulality failing - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a method for detecting a pixel irregulality failing capable of automatically detecting the pixel irregulality flaw of a screen and enabling the quantitative detection of the flaw, and a detector therefor. <P>SOLUTION: This detection method for the pixel irregulality flaw has a process for imaging a screen 10 to be inspected by a CCD camera 6, a process for taking the difference with a background image 14 from the image taken in a computer 7 by imaging to form an inspection image (background difference image) 15, a process for performing filter processing which enhances the brightness difference between the adjacent pixels of the inspection image by a Sobel filter, a process for dividing the inspection image into a plurality of areas to statistically calculate a brightness value at every divided area 16 and a process for quantitatively evaluating the pixel irregulality flaw 20 on the basis of the statistical data due to statistical calculation. <P>COPYRIGHT: (C)2004,JPO&NCIPI

Description

[0001]
TECHNICAL FIELD OF THE INVENTION
The present invention relates to a pixel unevenness detection method for automatically detecting a pixel unevenness defect on a screen in an inspection process of a liquid crystal panel or the like, and to a detection apparatus therefor.
[0002]
[Prior art]
Defects appearing on a screen of a liquid crystal display device and the like include point defects, line defects, and surface defects (also called spot / uneven defects). Since these defects deteriorate the image quality, they are subject to a visual inspection of the display. Conventionally, a visual inspection by a person has been usual, but recently, an automatic inspection has been performed. There have already been many proposals for an automatic inspection method for each of the above-mentioned defects, and Patent Document 1 is an example.
This patent document 1 discloses a method of separating and extracting each of defects at points, lines, and planes using three levels of thresholds while preventing false detection of false defects.
[0003]
[Patent Document 1]
JP-A-9-288037 (claims, paragraphs [0019] to [0048], FIGS. 1 to 10)
[0004]
[Problems to be solved by the invention]
However, a state in which the luminance of the display element unit exists over a certain range, such as an image having a sloppy or rough feeling (a defect that gives such a feeling is referred to as a “pixel unevenness defect” here) is described above. It did not belong to the category of point, line, and surface defects, and was out of the scope of the detection target.
[0005]
Accordingly, an object of the present invention is to provide a method for detecting a pixel unevenness defect and a device for detecting the same, which enable automatic detection of the above-described pixel unevenness defect on the screen and enable quantification of the defect. And
[0006]
[Means for Solving the Problems]
A method for detecting a pixel unevenness defect according to the present invention includes a step of imaging a screen to be inspected, a step of creating an inspection image by taking a difference from a background image from an image captured by the imaging, and a step of forming adjacent pixels of the inspection image. Performing a filter process for enhancing the brightness difference of the inspection image, dividing the inspection image into a plurality of areas, and performing a statistical calculation of the brightness value for each of the divided areas, and performing pixel unevenness based on the statistical data obtained by the statistical calculation. And a step of quantitatively evaluating the defect.
[0007]
In the present invention, the pixel unevenness defect is a defect due to a variation in luminance of each pixel of a display device such as a liquid crystal panel, and a plurality of high- and low-luminance pixels coexist in a display area or in an entire region. It is in the state of doing. For this reason, even if an attempt is made to display a uniform image, a variation in luminance of a fine size is conspicuous, and the display has a rough, grainy, and rugged feel.
On the other hand, what is generally referred to as an unevenness defect is a state in which a certain area of the display area has a difference in luminance from another area. This refers to a state in which a bright area or a dark area is present over a wide area compared to the surrounding area.
[0008]
In order to detect the above-mentioned pixel unevenness defect, in the present invention, an inspection target screen is imaged, and an inspection image is created by taking a difference between the image and a background image. Then, by filtering this inspection image, the luminance difference between adjacent pixels is emphasized. By this image enhancement filter processing, if it exists in the inspection image, the component of the pixel unevenness defect can be clarified. That is, the pixel unevenness defect can be qualitatively detected by the filtering process.
Further, by dividing the inspection image into a plurality of areas and performing statistical calculation of luminance values for each divided area, the luminance value distribution of each divided area can be found. This makes it possible to specify the region where the pixel unevenness has occurred, and to quantitatively evaluate the pixel unevenness based on the statistical data.
[0009]
In the present invention, the step of creating the inspection image includes a step of extracting a display area from an image including the screen to be inspected, and applying a geometric deformation to the display area to make it a rectangle.
Since the inspection image is created based on an image obtained by capturing the screen of the inspection target, if the inspection target screen is an image projected on the screen by, for example, a projector, the image includes an edge portion of the screen. Or the display area may be oblique to the screen. Therefore, when an inspection image is created, a display area is extracted from an image including a screen to be inspected, and the display area is subjected to geometric deformation to form a rectangle, thereby distorting the image as described above. Deformation and the like can be corrected.
[0010]
Further, in the present invention, in order to reduce the influence of a pattern that separates pixels called a black matrix, expansion processing is performed on each pixel of the inspection image before performing the filtering processing. This is because the brightness difference between the pixel portion and the black matrix portion is emphasized by the filter process because the brightness between the pixel portion and the black matrix portion is different, and it becomes difficult to distinguish the pixel portion from the pixel unevenness defect. Thus, the influence of the black matrix portion is removed from the inspection image in advance.
[0011]
In the filter processing, a Sobel filter or a Laplacian filter is used.
These filters are two-dimensional differential filters and have a function of enhancing the edge or contour of an image, that is, a pixel unevenness defect, so that the subsequent statistical processing facilitates detection of the defect.
[0012]
Further, in the present invention, in the statistical calculation for each of the divided areas, a standard deviation of a luminance value for each divided area is obtained, and a maximum standard deviation extracted therefrom is used as an evaluation value of the pixel unevenness defect.
As described above, the luminance value distribution of each divided area can be obtained by statistically calculating the luminance value of each pixel for each divided area. Further, since the standard deviation of the luminance value of each divided area can be obtained, the maximum standard deviation is extracted from those standard deviations, and this maximum standard deviation is used as the evaluation value of the pixel unevenness defect. It is. The maximum standard deviation indicates a location where pixel unevenness defects are intensively generated, and can serve as a scale for quantitatively determining the presence or absence of a defect, the quality of a product (including parts, the same applies hereinafter), and the like. Therefore, by using this as the evaluation value, the pixel unevenness defect can be quantitatively evaluated.
[0013]
Therefore, the presence / absence of a pixel unevenness defect is determined by comparing the maximum standard deviation with a preset threshold value. That is, if the maximum standard deviation is equal to or less than the threshold value, it is determined that there is no pixel unevenness defect, and the product is regarded as a non-defective product. By comparing the maximum standard deviation with the threshold value in this way, it is possible to automatically determine whether the product is good or defective, thereby enabling an automatic inspection.
Further, by setting the threshold value in several stages for each good product rank, it is possible to rank pixel unevenness defects and to classify products.
These statistical data can be used for quality control.
[0014]
A pixel unevenness detecting apparatus according to the present invention uses the pixel unevenness detecting method according to any one of claims 1 to 7.
More specifically, the inspection apparatus includes an imaging unit and an inspection apparatus main body that performs image processing. The inspection apparatus main body is generally configured by a computer. By incorporating an inspection program for performing each of the above-described processes into this computer, it is possible to automatically inspect for pixel unevenness defects.
[0015]
BEST MODE FOR CARRYING OUT THE INVENTION
Hereinafter, embodiments of the present invention will be described with reference to the drawings.
FIG. 1 is a configuration diagram of a pixel unevenness defect detection apparatus according to an embodiment of the present invention.
In this embodiment, for example, the screen 10 to be inspected is a screen of a liquid crystal panel (also referred to as a liquid crystal light valve) 2 using a TFT element by the projector 1. When performing an inspection, the image 4 is projected on the screen 3 by the projector 1. The image 4 is drawn by giving a predetermined pattern to the liquid crystal panel 2 by the pattern generator 5. For example, an image 4 is picked up by a CCD camera 6 as an image pickup means, and the image data is converted from an analog signal to a digital signal by an A / D converter (not shown), and is taken into a computer 7 which is a main body of the inspection apparatus. At this time, the image data is represented by, for example, 12-bit data with black being “0” and white being “4095” for each pixel by the A / D converter with a luminance value of 4096 gradations. Further, the computer 7 detects the pixel unevenness defect by processing the image data of the image 4 captured in the memory by a method described later. In detecting a defect, a filter process for emphasizing the defect is performed. Further, an evaluation value for quantitatively evaluating the detected defect is calculated. These inspection results are displayed on the display device 8.
[0016]
FIG. 2 is a flowchart used in the pixel unevenness defect detection processing, and FIG. 3 is a schematic diagram of an input image, a display area correction image, a background image, and a background difference image. This detection processing is automatically performed according to the inspection program incorporated in the computer 7 or the image processing apparatus.
The processing procedure will be described according to the flowchart of FIG.
(1) Display area extraction processing (step S1)
Extracting a display area means extracting only a screen portion to be inspected from an input image captured by imaging. For example, FIG. 3A shows an input image at the time of imaging. As shown in this figure, the input image 11 at the time of imaging includes the edge portion 31 of the screen 3 or the display area image 12 corresponding to the screen portion is not exactly rectangular but is distorted obliquely with respect to the screen 3. May go out. This is due to the fact that the screen 3 and the CCD camera 6 as the imaging means are not strictly parallel to each other, or that the screen 3 is distorted due to the characteristics of the lens or the like of the CCD camera 6. In addition, even when an image of a liquid crystal panel or the like is directly captured instead of indirectly, distortion or deformation of the input image may occur. Of course, in the case where the image pickup means and the screen of the inspection target are accurately set so as not to include the edge portion as described above and the image is picked up, for example, the field of view of the image pickup means is set accurately so that the entire screen portion of the detection target fits. In this case, the display area extraction processing and the correction processing described below can be omitted, and the image captured by imaging directly becomes the original image.
[0017]
When the input image 11 is distorted as shown in FIG. 3A, only the display area image 12 of the screen portion is extracted as shown in FIG. A display area correction image 13 that is deformed and corrected to an accurate rectangle is created. Here, the display area correction image 13 is an original image to be actually inspected.
In the image correction processing by the geometric deformation, the coordinates of the four corners of the display area image 12 that does not include the edge portion 31 of the screen 3 are detected by pattern matching processing, and the coordinates match the coordinates of the four corners of the rectangle. This is performed by converting the coordinates to.
By setting the size of the rectangle set at this time to, for example, 1200 × 1000 pixels, the image size of the original image 13 becomes 1200 × 1000 pixels. This size is not particularly fixed, and may be set to a size equal to or close to the pixel size of the CCD camera.
[0018]
(2) Background image difference processing (step S2)
The background image difference process is a process of subtracting a previously created background image 14 as shown in FIG. 3C from the original image 13 created as described above. The background image 14 is, for example, an image obtained by imaging about 20 liquid crystal panels with few defects with the apparatus configuration of the present invention and averaging the images, and is created in advance in the same manner as the original image 13. . The background image 14 is stored in the memory of the computer 7. As a result of the background image difference processing, a background difference image as shown in FIG. If so, the pixel difference defect 20 exists in the background difference image (inspection image) 15.
[0019]
(3) Expansion processing (step S3)
The dilation processing is performed, for example, when a liquid crystal light valve includes a black matrix as a light-shielding portion such as a driving element, in order to reduce the influence of the black matrix.
In this expansion processing, for example, a maximum filter process is performed in which a region including eight neighboring pixels centered on a certain pixel of interest, that is, a maximum value in a 3 × 3 pixel region centered on the pixel of interest is replaced with the pixel of interest. Things. By this processing, the white region expands and the black matrix portion, which is a minute black region, is reduced, so that the components of the black matrix can be reduced. This expansion processing is not limited to the maximum filter, and a morphology processing or the like for expanding a white area may be used.
[0020]
(4) Sobel filter processing (step S4)
The Sobel filter is a type of filter that detects and enhances edges and contours of an image (here, a defect), and expresses a difference (inclination) between adjacent pixels as a luminance value, thereby obtaining an edge of the image. And contours. For example, when applying the Sobel filter in the region of 3 × 3 pixels as shown in FIG. 4, the luminance values L _R E-pixels in the center is calculated by the following formula.
_LR = (X ² + Y ² ) ^1/2
Where X = (C + 2F + I)-(A + 2D + G)
Y = (A + 2B + C)-(G + 2H + I)
Using this Sobel filter, the filter is applied to the lower right corner while shifting one pixel at a time from the upper left corner of the image to the right and downward, and the luminance value of each pixel is calculated. An example is shown in FIG. FIG. 7A shows the luminance value of each pixel before processing, and FIG. 7B shows the calculation result of the luminance value after Sobel filter processing.
As is apparent from the result, after the filtering process, the difference in the luminance value between the adjacent pixels is detected and emphasized, so that the edge and the outline of the image are clearly displayed.
Other filters for such image (defect) enhancement processing include a Laplacian filter or the like, and this filter may be used.
[0021]
(5) Area division processing (step S5)
After the above-mentioned Sobel filter processing, the inspection image 15 is divided horizontally and vertically into a plurality of areas. Here, as shown in FIG. 6, the area is divided into 16 4 × 4 areas 16. The purpose of dividing the image into a plurality of areas is to statistically process the brightness value of each of the divided areas 16 described below to specify the location of the pixel unevenness defect and to quantify the pixel unevenness defect. is there.
It should be noted that as the number of area divisions of an image increases, the pixel unevenness defect in a local region can be detected, and the detection accuracy increases. However, in this embodiment, since the actual pixel unevenness defect appears in such a manner as to cover an area about one-fourth of the display area, it is considered that such a division is sufficient. × 4 16 divisions are selected.
[0022]
(6) Statistical calculation in each divided area (step S6)
Statistical calculation of the luminance value of each pixel in the divided area is performed for each divided area 16. Then, the standard deviation σ of the luminance value is obtained for each divided area 16.
For example, FIG. 7 is a graph three-dimensionally representing the standard deviation σ of the luminance value for each divided area. The graph of FIG. 7 is displayed on the display device 8.
[0023]
(7) Extraction of maximum standard deviation (step S7)
This is a step of obtaining the maximum value (maximum standard deviation) σmax from among the standard deviations σ of the luminance values for the divided areas obtained as described above. The maximum standard deviation σmax represents a region where the pixel unevenness defect occurs most intensively, and a portion where the luminance value distribution in the pixel unevenness defect is the highest. Therefore, in the present embodiment, the maximum standard deviation σmax is used as an evaluation value when detecting a pixel unevenness defect.
Therefore, in detecting a pixel unevenness defect, a predetermined threshold value is set, and if the maximum standard deviation σmax is equal to or smaller than the threshold value, it is determined that there is no pixel unevenness defect. Further, the threshold value can be set in several steps for each good product rank. As a result, it is possible to assign a rank (grade) in a non-defective product of the pixel unevenness defect and to classify the product. For example, when a liquid crystal panel is used as a light valve of a projector for each of R (red), G (green), and B (blue) colors, the relative luminous efficiency is best when green (wavelength λ = 555 nm). Since it is high, the threshold value for setting the rank of the green light valve is set to the strictest value smaller than in other cases.
[0024]
Since this embodiment is configured as described above, it is possible to quantitatively and automatically detect a pixel unevenness defect present on the screen of a display device such as a liquid crystal panel.
In addition, since pixel unevenness defects are evaluated using the maximum standard deviation, the quality data of products and parts can be aggregated and analyzed, which can be used for quality control, and a method aimed at further improving quality is developed. It is also possible to build.
[0025]
The present invention is not limited to a liquid crystal panel using the above-described TFT element, and displays such as a liquid crystal panel, a plasma display, an organic EL display, and a DMD (direct mirror device) using other diode elements. It can be used for inspection of body parts and display devices and products using them, and it is needless to say that the use of such components is not excluded from the scope of the present invention.
[Brief description of the drawings]
FIG. 1 is a configuration diagram of a pixel unevenness defect detection device according to an embodiment of the present invention.
FIG. 2 is a flowchart showing a process for detecting a pixel unevenness defect.
FIG. 3 is a schematic diagram of an input image, a display area correction image, a background image, and a background difference image.
FIG. 4 is an explanatory diagram showing an example of a Sobel filter.
FIG. 5 is an explanatory diagram showing examples of numerical values before and after processing by a Sobel filter.
FIG. 6 is an explanatory diagram showing an example of area division of an image.
FIG. 7 is a graph showing a standard deviation of luminance values for each divided area.
[Explanation of symbols]
Reference Signs List 1 projector, 2 liquid crystal panel, 3 screen, 4 images, 5 pattern generator, 6 CCD camera, 7 computer, 8 display device, 10 inspection target screen, 11 input image, 12 display area image, 13 original image (display area correction image) ), 14 background image, 15 inspection image (background difference image), 16 divided areas, 20 pixel unevenness defect, 31 screen edge

Claims (8)

Imaging a screen to be inspected;
Taking a difference from the background image from the image captured by the imaging to create an inspection image;
Performing a filtering process to enhance the luminance difference between adjacent pixels of the inspection image,
Dividing the inspection image into a plurality of areas, and performing a statistical calculation of a luminance value for each of the divided areas;
A step of quantitatively evaluating the pixel unevenness defect based on the statistical data by the statistical calculation,
A method for detecting pixel unevenness defects. 2. The method according to claim 1, wherein the step of creating the inspection image includes a step of extracting a display area from an image including the screen to be inspected, and applying a geometric deformation to the display area to form a rectangle. Method for detecting pixel unevenness defects. 3. The method according to claim 1, wherein an expansion process is performed on each pixel of the inspection image before performing the filtering process. 4. 4. The method according to claim 1, wherein a Sobel filter or a Laplacian filter is used in the filtering. 5. The statistical calculation for each divided area, wherein a standard deviation of a luminance value for each divided area is obtained, and a maximum standard deviation extracted from the standard deviation is used as an evaluation value of the pixel unevenness defect. The method for detecting a pixel unevenness defect according to any one of the above. 6. The method according to claim 5, wherein the presence or absence of a pixel unevenness defect is determined by comparing the maximum standard deviation with a preset threshold value. 7. The method according to claim 6, wherein the threshold value is set in several steps for each non-defective product rank, and the rank among non-defective products is determined simultaneously. An apparatus for detecting a pixel unevenness defect, comprising using the method for detecting a pixel unevenness defect according to claim 1.

Images