JP2003187222A - Image defect detector and image defect detection method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an image defect detector capable of automatically setting a threshold for detecting a defect of a displayed image. <P>SOLUTION: The image defect detector is provided with an image pickup means, an area setting means for setting N detection areas and N reference areas, a comparison value calculation means for calculating N comparison values respectively based on a plurality of pieces of pixel display data in the respective areas to be detected and a plurality of pieces of pixel display data in the reference areas, a rearrangement means for rearranging the N comparison values in an order of values of the N comparison values and a threshold setting means for setting the threshold for judging presence/absence of the defect of the image based on difference between the J-th comparison value and the (J+w)th comparison value from the smallest value of the comparison values (J and w are integers satisfying each of inequalities 1≤J≤N-w, 1≤w≤N) among the N comparison values rearranged by the rearrangement means and a predetermined reference value for setting the threshold. <P>COPYRIGHT: (C)2003,JPO

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image defect detecting device, and more particularly, it sets a threshold value for detecting a defect in an image displayed on a display device to detect an image defect with high accuracy. The present invention relates to an image defect detecting device.

[0002]

2. Description of the Related Art A display device or the like which requires a uniform transmission characteristic, such as a display device using a liquid crystal panel, is inspected in its production process for a defect in its display image. Such defect inspection is mainly performed by visual inspection by an operator. Since the visual inspection is easily affected by differences in the surrounding environment, individual differences, etc., it is difficult to obtain an appropriate inspection result.

In view of such a background, the inventors of the present invention automatically detect the defect in the display image in order to automate the defect inspection for detecting the defect in the display image from the visual inspection to the automatic inspection. A device is proposed in Japanese Patent Application No. 2000-107015. This image defect detection device digitizes pixel data representing pixels forming an image, calculates a comparison value based on the digitized pixel data, and based on the calculated comparison value and a preset threshold value. Automatically detect defects in the displayed image.

[0004]

In order to automatically detect a defect in a display image by such an image defect detecting device, a threshold value for detecting a defect in the display image must be set in advance. I have to. The threshold value to be set in advance is determined by a skilled person who visually observes a large amount of image data collected at the production site.

However, the work of collecting a large amount of image data at a production site requires a lot of time and labor, and a skilled worker visually observes the large amount of collected image data to set a threshold value. The problem is that it requires a lot of time and effort.

The present invention has been made to solve the above problems, and an object thereof is to provide an image defect detecting device capable of automatically setting a threshold value for detecting a defect in a displayed image, and An object is to provide an image defect detection method.

[0007]

An image defect detecting apparatus according to the present invention comprises an image pickup means for picking up an image composed of a plurality of pixels, and N detection areas for detecting a defect in the image ( N is an integer greater than or equal to 2), for displaying a plurality of pixels in each detection target area, and area setting means for setting N reference areas corresponding to the N detection areas in the image. A comparison value is calculated for each detection area based on the plurality of pixel display data and the plurality of pixel display data for displaying the plurality of pixels in the reference area corresponding to each detection target area. A value calculation means, a sorting means for sorting the N comparison values calculated by the comparison value calculation means in that order, and the comparison among the N comparison values sorted by the sorting means. Small value J-th comparison value (J + w) th comparison value of the difference from the (J and w, 1 ≦ J ≦ N-
w, an integer that respectively satisfies 1 ≦ w ≦ N), and a threshold value for setting a threshold value for determining the presence or absence of a defect in the image based on a predetermined threshold value setting reference value Based on the setting means, the threshold value and the N comparison values,
A defect detecting means for detecting a defect in the image is provided, whereby the above object is achieved.

The sorting means may sort the N comparison values in ascending order.

In the threshold value setting means, the difference between the Jth comparison value and the (J + w) th comparison value from the smallest comparison value is the predetermined threshold setting reference value. When it is larger than the above, the (J + w) th comparison value may be set as the threshold value.

When the difference between the J-th comparison value and the (J + w) -th comparison value is not larger than the predetermined threshold setting reference value, the rearrangement means inputs it by the user. The threshold value may be set based on the obtained data.

The image defect detection device detects defects in a plurality of types of images, and the region setting means sets the N detection regions and the N reference regions for each defect of the plurality of types of images. The comparison value calculating means calculates the N comparison values for each defect of the plurality of types of images, and the rearranging means calculates the N comparison values for each defect of the plurality of types of images. The threshold value setting means sets the threshold value for each defect of the plurality of types of images, and the defect detection means sets the threshold value for each of the defects of the plurality of types of images. And the N comparison values calculated for each defect of the plurality of types of images, the defects of the image may be detected for each of the defects of the plurality of types of images.

The plurality of types of image defects may include point defects, spot defects, line defects, and uneven defects.

The image is a hue system (red, green, blue, etc.)
The comparison value calculating means calculates the N comparison values for each color component, and the rearranging means rearranges the N comparison values for each color component, The threshold value setting unit may set the threshold value for each color component, and the defect detection unit may detect a defect in the image for each color component.

An image defect detecting method according to the present invention includes an image pickup step for picking up an image composed of a plurality of pixels, and N detection regions for detecting defects in the image (N is
2 or more), a region setting step of setting N reference regions corresponding to the N detection regions, and a plurality of pixel display data for displaying a plurality of pixels in each detection target region And a comparison value calculating step of calculating a comparison value for each detection area based on a plurality of pixel display data for displaying a plurality of pixels in the reference area corresponding to each detection target area, Of the N comparison values rearranged in the order of the N comparison values calculated in the comparison value calculation step, and the N comparison values rearranged in the rearrangement step. From the smallest comparison value, the difference between the Jth comparison value and the (J + w) th comparison value (J and w are 1 ≦ J ≦ N−w, 1 ≦ w.
≤ N) and a threshold value setting step of setting a threshold value for determining the presence or absence of a defect in the image based on a predetermined threshold value setting reference value,
A defect detection step of detecting a defect in the image based on the threshold value set based on the threshold value setting step and the N comparison values calculated by the comparison value calculation step. The above-mentioned object is achieved by that.

[0015]

BEST MODE FOR CARRYING OUT THE INVENTION An image defect detecting apparatus according to this embodiment will be described. The image defect detection apparatus according to the present embodiment automatically sets a threshold value for detecting a defect in an image displayed by a display device or the like that requires uniform transmission characteristics such as a display screen of a liquid crystal panel. Set to.

FIG. 1 is a block diagram of an image defect detecting apparatus 100 according to this embodiment. Image defect detection device 10
0 detects a defect in the image displayed on the display screen of the liquid crystal panel. The image defect detecting device 100 includes an image capturing unit 3 having a lens 1 and a camera 2. The imaging unit 3
An image displayed on the display screen of the liquid crystal panel 25 is captured by the camera 2 through the lens 1.

An AD converter 4 is connected to the image pickup section 3. The AD converter 4 performs analog-digital (AD) conversion on image data representing an image captured by the image capturing unit 3. The output of the AD converter 4 is given to the memory 5,
The memory 5 stores the image data AD-converted by the AD converter 4.

The image data stored in the memory 5 is RG
The B separator 6 separates each color component of red (R), green (G), and blue (B). The image data separated by the RGB separator 6 is stored in the memory 7 for each of the R, G and B components. Here, the RGB separator 6 separates into R, G, and B, but may be separated into another hue system.

The image defect detecting apparatus 100 includes an area setting section 1
Equipped with 7. The area setting unit 17 includes N detection areas (N is an integer of 2 or more) for detecting defects in the image captured by the image capturing unit 3, and N reference areas corresponding to the N detection areas. The area and the area are set as the imaging area of the image taken by the imaging unit 3.

The image defect detecting apparatus 100 is provided with an inspection section 8. FIG. 2 is a block diagram of the inspection unit 8. The inspection unit 8 includes an area storage unit 18, a comparison value calculation unit 19, and a defect detection unit 20. In the area storage unit 18,
The position data indicating the positions of the N detection regions set by the region setting unit 17 and the position data indicating the positions of the N reference regions respectively corresponding to the N detection regions are stored. The comparison value calculation unit 19 includes a plurality of pixel data in each detection area stored in the area storage unit 18 among a plurality of pixel data forming the image data stored in the memory 7, and a reference corresponding to each detection area. A comparison value is calculated for each detection area based on the pixel data in the area. The defect detecting unit 20 detects a defect in the image captured by the image capturing unit 3 based on the N comparison values calculated by the comparison value calculating unit 19 and a threshold value that is automatically set as described later. To detect.

The image defect detecting apparatus 100 includes a rearrangement unit 1
It has 0. The rearrangement unit 10 includes a comparison value calculation unit 19
The N comparison values calculated by are rearranged in the order of the values of the N comparison values.

The image defect detecting apparatus 100 is provided with a threshold value setting section 12. The threshold value setting unit 12 automatically sets a threshold value for determining the presence or absence of a defect in the image captured by the image capturing unit 3. Threshold setting section 1
2 includes a distribution determination unit 11. Distribution determination unit 11
Is the difference between the Jth comparison value and the (J + w) th comparison value from the smaller comparison value among the N comparison values rearranged by the rearrangement unit 10 (where J and w are 1
≦ J ≦ N−w, integers that respectively satisfy 1 ≦ w ≦ N),
Based on a predetermined threshold setting reference value, the distribution state of the rearranged N comparison values is determined, and when it is determined to be a predetermined distribution state, the (J + w) th comparison Set the value as a threshold. The threshold value setting unit 12 is provided with a threshold value register 13, and the threshold value register 13 stores the threshold value set by the distribution determination unit 11.

The image defect detecting apparatus 100 includes a result output unit 1
5 and a threshold value input unit 16. Result output part 1
5 displays the distribution state of the N comparison values when the distribution determination unit 11 determines that the distribution state of the N comparison values is not the predetermined distribution state. The threshold input unit 16 has N
When it is determined that the distribution state of the plurality of comparison values is not the predetermined distribution state and the distribution state of the N comparison values is displayed on the result output unit 15, the operation is performed to input the threshold value. The image defect detection device 100 is provided with a central processing unit (CPU) 14 that controls the overall operation of the image defect detection device 100.

FIG. 3 is an explanatory diagram of image defects detected by the image defect detecting apparatus 100. Image defect detection device 100
Can detect multiple types of defects. The plurality of types of defects include a point defect 21, a spot defect 22, a line defect 23, and an unevenness defect 24. The point defect 21 is 1
It is assumed that the value of the pixel display data for displaying one pixel is extremely larger (or smaller) than the value of the pixel display data representing the surrounding pixels. The spot defect 22 is a pixel display data value for displaying a plurality of pixels adjacent to each other in a plane along the horizontal direction and the vertical direction, and a pixel display data value for displaying pixels around the pixel display data. Is not as extreme as the point defect 21, but is a fairly large (or small) defect. Like the spot defect 22, the line defect 23 is not as extreme as the point defect 21, but is a pixel display having a value considerably larger (or smaller) than the value of the pixel display data for displaying the pixels around it. It is assumed that the plurality of pixels represented by the data are linearly adjacent to each other along a predetermined direction. In the example illustrated in FIG. 3, a line defect 23 in which a plurality of pixels are linearly adjacent in the horizontal direction and a line defect 23 in which the plurality of pixels are linearly adjacent in the vertical direction are shown. The unevenness defect 24 is a defect in which a plurality of pixels having a difference from a value of pixel display data for displaying pixels around the unevenness defect is smaller than that of the spot defect 22 are adjacent to each other over a wider range than the stain defect 22. To do.

FIG. 4 is a flowchart showing an image defect detecting method by the image defect detecting apparatus 100 according to the embodiment.

First, N detection areas for detecting image defects and N reference areas respectively corresponding to the N detection areas are set in the image pickup areas by operating the area setting section 17. The data is stored in the area storage unit 18 provided in the inspection unit 8.

The N detection areas and the N reference areas are
It is set for each of the R, G, and B color components. The N detection areas and the N reference areas set for each of the R, G, and B color components are further set according to the types of defects detected by the image defect detection apparatus 100.

FIG. 5 is a diagram for explaining a method of setting the detection area 26 for detecting the point defect 21 and the reference area 27 as the imaging area. First, the detection area 26 is set as the imaging area 28. The detection area 26 is set to a matrix size of 3 pixels × 3 pixels. The point defect 21 is usually 1
Although it is a pixel defect, AD in the AD converter 4
Since the image data becomes dull due to the conversion or the like, a defect of one pixel may affect the surrounding pixels. Therefore, the detection area 26 considers such a bluntness of image data,
It is preferable to set the matrix size such as 3 pixels × 3 pixels larger than 1 pixel. Next, a reference area 27 of 3 pixels × 3 pixels is set so as to be adjacent to the detection area 26. In the example shown in FIG. 5, an example in which the detection area 26 is adjacent to the left side is shown.

In this way, N detection regions 26 for detecting the point defects 21 are set so as to cover the entire image pickup region 28, and N reference regions respectively adjacent to the N detection regions 26 are set. To set.

The detection area and the reference area for detecting the spot defect 22 are set by a matrix size larger than the matrix size of the detection area 26 and the reference area 27 for detecting the point defect 21 described above.

FIG. 6 is a diagram for explaining a method of setting the detection area 26A and the reference area 27A for detecting the line defect 23 in the imaging area 28. For example, in order to detect the line defect 23 along the vertical direction, the detection region 26A and the reference region 27A having the elongated matrix size along the vertical direction are set. The matrix size of the detection area 26A and the reference area 27A is, for example, 2 pixels (horizontal direction) × 50 pixels (vertical direction). In order to detect the line defect 23 along the horizontal direction, it suffices to set a detection region and a reference region having an elongated matrix size along the horizontal direction.

FIG. 7 is a diagram for explaining a method of setting the detection area 26B for detecting the unevenness defect 24 and the reference area 27B in the imaging area 28. The detection area 26B and the reference area 27B for detecting the non-uniformity defect 24 are set by a matrix size larger than the matrix size of the detection area and the reference area for detecting the spot defect 22 described above. The matrix size of the detection area 26B and the reference area 27B is, for example, 3
It is 0 pixels × 30 pixels. In the unevenness defect 24, since the pixel display data gradually changes over a plurality of pixels, it is preferable to set the reference region 27B with an appropriate distance from the detection region 26B.

In this way, the detection area and the reference area are set as the image pickup areas according to the types of defects detected by the image defect apparatus 100 (S31).

Next, the comparison value calculation unit 19 respectively sets a plurality of pixel display data for displaying a plurality of pixels in each detection area and a plurality of pixels in the reference area corresponding to each detection target area. A comparison value is calculated for each detection region based on a plurality of pixel display data to be displayed.

For example, with respect to the detection area 26 and the reference area 27 for detecting the point defect 21, 3 in the detection area 26 are used.
Of the total value of 9 pixel display data for displaying each of × 3 = 9 pixels, and the 9 pixel display data for displaying each of 3 × 3 = 9 pixels in the reference area 27, The absolute value of the difference from the total value is calculated as a comparison value (S
32).

For example, the value of pixel display data for displaying one pixel is 0 or more and 255 represented by 8 bits.
When the ideal value of the pixel display data is 128 represented by 8 bits, the detection area 26
The total value of 3 × 3 = 9 pixel display data is 1150
If the total value of 3 × 3 = 9 pixel display data in the corresponding reference area 27 is 1100, then 1150-1
A comparison value of 50 is calculated with 100 = 50.

Similarly to the point defect 21, the total value of the pixel display data in the detection area and the reference area are also applied to the detection area and the reference area for detecting the spot defect 22, the line defect 23 and the unevenness defect 24. The absolute value of the difference from the total value of the pixel display data in 27 is calculated as a comparison value.

Next, it is judged whether or not the comparison values have been calculated for all the set N detection areas (S3).
3). When it is determined that the comparison values have not been calculated for all the set N detection areas 26 (NO in S33), the detection areas 26 for which the comparison values have not been calculated yet.
And an address designating the reference area 27 is generated (S3
4) The process returns to S32, and the comparison value is calculated for the detection region 26 and the reference region 27 designated by the address generated in S34.

When it is determined that the comparison values have been calculated for all N detection areas 26 (YE in S33).
S), the comparison value calculation unit 19 stores the N comparison values corresponding to the calculated N detection regions in the comparison value storage unit 9 (S).
35). In this way, the comparison value calculation unit 19 calculates N comparison values based on the N detection regions 26 and the N reference regions 27 corresponding to the N detection regions 26.

Then, it is judged whether or not N comparison values have been calculated for the image data in all the R, G and B color components (S36). When it is determined that N comparison values have not been calculated for all image data of color components (NO in S36), the process returns to S32, and comparison values are calculated for image data of color components for which comparison values have not yet been calculated. To calculate.

When it is determined that N comparison values have been calculated for the image data of all color components (YES in S36), all types of point defect 21, spot defect 22, line defect 23 and uneven defect 24 are obtained. It is determined whether or not N comparison values have been calculated for the defect of (S37). When it is determined that N comparison values have not been calculated for all types of defects (NO in S37), the process returns to S31, and detection areas and reference areas are set for the types of defects that have not been calculated. .

When it is determined that N comparison values have been calculated for all types of defects (YE in S37).
S), the sorting unit 10 sorts each of the N comparison values stored in the comparison value storage unit 9 in ascending order for each color component and each defect type (S38).

FIG. 8 is a graph showing a comparison value distribution curve 61 plotted so as to connect the values of the N comparison values rearranged by the rearrangement unit 10. In FIG. 8, the horizontal axis represents the rank of each comparison value sorted in ascending order, and the vertical axis represents the value (absolute value) of each comparison value. The example shown in FIG. 8 is a comparison value calculated based on the rearranged N comparison values calculated based on the detection area 26 and the reference area 27 for detecting the point defect 21 in the R color component. The distribution curve is shown.

If the transmission characteristics of the liquid crystal panel 25 are completely uniform over the entire display screen, the pixel display data in the detection area and the pixel display data in the reference area will be equal, so the value of each comparison value will be Originally, it should be zero. However, as can be seen from the fact that the light passing through the peripheral portion of the display screen tends to be weaker than that in the central portion, the transmission characteristics differ in different regions on the display screen. Therefore, even if the comparison value is calculated with the inspection area 26 and the reference area 27 adjacent to each other as shown in FIG. 6, the value of the comparison value does not become zero but shows a certain value.

When the N comparison values are rearranged in ascending order, the comparison values are normally distributed as shown by the comparison value distribution curve 61. In the comparative value distribution curve 61, from the first rank to the intermediate rank representing the (N / 2) th rank, which is 1/2 of the maximum rank, the value of the comparative value is close to the intermediate rank. Hardly increases. From the middle rank to the maximum rank representing the Nth comparison value, the value of the comparison value increases greatly as the distance from the middle rank approaches the maximum rank.

FIG. 9 is a flowchart showing the detailed operation of the threshold value setting unit 12 in the image defect detecting method according to the embodiment. First, an intermediate rank that is ½ of the maximum rank is set to the rank variable J that represents the rank of the rearranged comparison values (S51).

Then, based on (Equation 1) shown below,
The difference d between the J-th comparative value and the (J + w) -th comparative value is calculated (S52).

D = H (J + w) −H (J) (Equation 1), where H (J): a function indicating the value of the comparison value of the Jth rank, J: a rank variable indicating the rank of the comparison value , W: rank width, d: difference between the J-th comparative value and the (J + w) -th comparative value, rank J and rank width w are 1 ≦ J ≦ N−w, 1 ≦ w ≦ N
Are integers that respectively satisfy.

(Equation 1) corresponds to a calculation for obtaining the slope of the comparison value distribution curve 61. The rank width w is predetermined according to the total number N of comparison values. For example, the total number N of comparison values is 10
When the rank is 00 and the middle rank is 500, the rank width w is set to 5.

Then, the difference d obtained in S52
Is greater than a predetermined threshold setting reference value S (S53). When it is determined that the difference d is larger than the predetermined threshold setting reference value S (YES in S53), the value H (J + w) of the (J + w) th comparison value is set as the threshold. (S57).

When it is determined that the difference d is not larger than the predetermined threshold setting reference value S (NO in S53), 1 is added to the rank variable J (S54).
Then, it is determined whether or not the value of the rank variable J is equal to or higher than the maximum rank (S55). When the value of the rank variable J is smaller than the maximum rank (NO in S55), the process returns to S52 and the difference d is obtained.

When it is determined that the value of the rank variable J is equal to or higher than the maximum rank (YES in S55), the rearranged N comparison values are sorted from the middle rank as shown in the comparison value distribution curve 61. It is judged that the distribution is not so large that the distance increases and the position approaches the maximum rank. In this case, the threshold cannot be automatically set, and therefore the user outputs a user setting screen for setting the threshold to the result output unit 15 (S56).

The threshold setting reference value S is, for example, (H (J + w) -H) when the total number N of comparison values is 1000.
When the maximum value of (J)) is about 50, it is set to 10.

FIG. 10 is a graph showing a comparative value distribution curve 61A which is not distributed so that the value of the comparative value greatly increases even when the comparative value approaches the maximum rank away from the intermediate rank. Similar to FIG. 6, the horizontal axis represents the order of the rearranged comparative values, and the vertical axis represents the value (absolute value) of each comparative value.

In the comparative value distribution curve 61A, the value of the comparative value does not increase significantly as in the comparative value distribution curve 61 shown in FIG. 6 even when the intermediate rank approaches the maximum rank, so the difference d is predetermined. It does not become larger than the threshold setting reference value S. Therefore, the threshold cannot be set automatically.

FIG. 11 is an explanatory diagram of a user setting screen for the user to set the threshold when the threshold cannot be automatically set. In the example shown in FIG. 11, a graph showing the comparison value distribution curve 61A is displayed on the left half of the screen, and samples corresponding to N, which is the number of rearranged comparison values, are displayed on the right half of the screen. The number, the minimum value, the maximum value, and the intermediate value of the N comparison values can be displayed. Below the graph showing the comparison value distribution curve 61A, the values of the comparison values of arbitrary ranks are displayed. You can do it. In this case, the images captured by the image capturing unit 3 may be displayed simultaneously. The user can input an arbitrary threshold value based on the information displayed on the screen.

When the comparative value distribution curve 61A shown in FIG. 10 is obtained, the liquid crystal panel 25 to be inspected may be a non-defective product having no image defect, but the liquid crystal panel 25 is a defective product having an image defect. Even in some cases, depending on the type of defect to be inspected, for example, because the matrix size of the detection area 26 and the reference area 27 is inappropriate, the comparison value of the detection area 26 and the reference area 27 ( Difference)
Does not have a value that appropriately represents the image defect, so that the comparison value distribution curve 61A is obtained in which the value of the comparison value is not distributed so as to greatly increase, even though the liquid crystal panel 25 has the image defect. There is a risk. In order to correctly detect the image defect even in such a case, when the comparison value distribution curve 61A shown in FIG. 10 is obtained, the user setting screen shown in FIG. 11 is displayed.
By operating the threshold input unit 16, the user can set the threshold.

For the point defects in the G and B color components, the threshold value can be set in the same manner.

Since the matrix sizes of the detection area and the reference area are different depending on the type of defect, the calculated comparison value differs depending on the type of defect. For example, the comparison value for detecting spot defects is about 10 times larger than the comparison value for detecting point defects. In this case, the comparison value for detecting the spot defect is normalized to 1/10 and processed, and the comparison calculated based on the detection region and the reference region for detecting the point defect as shown in FIG. 8 or 10. It is displayed as a graph having a comparative value of the same value as the value graph. Similarly, the comparison values for detecting line defects and uneven defects are multiplied by weighted coefficients, normalized and processed, and displayed as a graph as shown in FIG. 8 or 10.

As described above, according to the present embodiment, of the N comparison values rearranged by the rearrangement unit 10, the Jth comparison value from the smaller comparison value is (J + w).
The difference from the second comparison value (where J and w are 1 ≦ J ≦ N−
w, an integer that respectively satisfies 1 ≦ w ≦ N), and a threshold value for setting a threshold value for determining the presence or absence of an image defect based on a predetermined threshold value setting reference value S. Since the setting unit 12 is provided, the threshold value for detecting a defect in the display image can be automatically set.

Further, since the threshold value setting unit 12 sets a threshold value for each defect of a plurality of types of images, it is possible to optimally determine the presence or absence of a defect according to the type of defect of the image.

Further, since the threshold value setting unit 12 sets a threshold value for each color component, it is possible to optimally determine the presence or absence of a defect according to each color component.

Furthermore, since the comparison value is normalized according to the type of image defect, the threshold values can be easily compared and analyzed, and the user can easily set the threshold value. .

Further, the threshold value setting unit 12 determines that the difference between the Jth comparison value and the (J + w) th comparison value from the smallest comparison value is a predetermined threshold setting reference value. When it is larger than the threshold value, the (J + w) th comparison value is set as the threshold value, so that the threshold value can be easily set.

Further, when the difference between the Jth comparison value and the (J + w) th comparison value is not larger than the predetermined threshold value setting reference value, the threshold value setting section 12
Since the threshold value is set based on the data input by the user, it is possible to easily verify whether or not the comparison value is distributed based on the distribution model assumed in advance, and it is assumed in advance. When the distribution is not based on the distribution model, the user can quickly set the threshold.

Although an example of detecting a defect in an image displayed on the display screen of the liquid crystal panel has been shown, the present invention is not limited to this. The present invention can also be applied to the inspection of products that require uniformity in output. For example, for the inspection of defects in the image displayed by an element such as a solid-state image sensor that requires uniform output, the inspection of the uniformity of the coating state, and the inspection of the defects in the image displayed on the cathode ray tube. The present invention can be applied.

Further, although the example in which the AD converter 4 is connected to the camera 2 is shown, the AD converter 4 may be built in the camera 2.

Furthermore, an example has been shown in which the image data is AD-converted by the AD converter 4 and then the image data is separated into R, G, and B color components by the RGB separator 6.
The image data may be AD-converted after being separated for each of the R, G, and B color components.

[0069]

As described above, according to the present invention, it is possible to provide an image defect detecting device and an image defect detecting method capable of automatically setting a threshold value for detecting a defect in a display image. it can.

[Brief description of drawings]

FIG. 1 is a block diagram of an image defect detection device according to an embodiment.

FIG. 2 is a block diagram of an inspection unit included in the image defect detection device according to the embodiment.

FIG. 3 is an explanatory diagram of an image defect detected by the image defect detection device according to the embodiment.

FIG. 4 is a flowchart showing an image defect detecting method according to an embodiment.

FIG. 5 is a diagram illustrating a method of setting a detection area for detecting a point defect and a reference area as an imaging area according to the embodiment.

FIG. 6 is a diagram illustrating a method of setting a detection area for detecting a line defect and a reference area as an imaging area according to the embodiment.

FIG. 7 is a diagram illustrating a method of setting a detection area and a reference area for detecting a non-uniformity defect as an imaging area according to the embodiment.

FIG. 8 is a graph showing an example of a distribution of comparison values rearranged by the rearrangement unit of the image defect detection apparatus according to the exemplary embodiment.

FIG. 9 is a flowchart showing a detailed operation of a threshold value setting unit in the image defect detecting method according to the embodiment.

FIG. 10 is a graph showing another example of the distribution of comparative values rearranged by the rearrangement unit of the image defect detection device according to the exemplary embodiment.

FIG. 11 is an explanatory diagram of a user setting screen of the image defect detecting apparatus according to the embodiment.

[Explanation of symbols]

3 Imaging unit 10 Sorting section 12 Threshold setting section 17 Area setting section 19 Comparison value calculator 20 Defect detection section d difference Reference value for S threshold setting

   ─────────────────────────────────────────────────── ─── Continued front page    F-term (reference) 2G086 EE10                 5B057 AA01 BA29 CA01 CA08 CA12                       CA16 CB01 CB08 CB12 CB16                       CC02 CE16 DA03 DB02 DB06                       DB09 DC22                 5C061 BB03 BB05 CC05 CC09 EE21                 5G435 AA17 AA19 BB12 CC09 KK10                 5L096 AA02 AA06 BA03 CA02 EA45                       FA17 GA07 GA17 GA40 GA51                       LA04

Claims (8)

[Claims] 1. An image pickup unit for picking up an image composed of a plurality of pixels, and N detection regions for detecting defects in the image (N
Is an integer of 2 or more), area setting means for setting N reference areas respectively corresponding to the N detection areas in the image, and a plurality of pixels for displaying a plurality of pixels in each detection area. Comparison value calculating means for calculating a comparison value for each detection region based on the pixel display data of 10 and a plurality of pixel display data for displaying a plurality of pixels in the reference region corresponding to each detection region. A rearrangement means for rearranging the N comparison values calculated by the comparison value calculation means in the order of the values, and the comparison value among the N comparison values rearranged by the rearrangement means. The difference between the Jth comparison value and the (J + w) th comparison value from the smallest value of
≦ J ≦ N−w, integers that respectively satisfy 1 ≦ w ≦ N),
Threshold setting means for setting a threshold for determining the presence or absence of a defect in the image based on a predetermined threshold setting reference value, and comparing the threshold with the N pieces An image defect detecting apparatus, comprising: a defect detecting unit that detects a defect in the image based on the value. 2. The image defect detection device according to claim 1, wherein the rearrangement unit rearranges the N comparison values in ascending order. 3. The threshold value setting means sets the difference between the Jth comparison value and the (J + w) th comparison value from the smallest comparison value for the predetermined threshold value setting. The image defect detection apparatus according to claim 1, wherein the (J + w) th comparison value is set as the threshold value when it is larger than a reference value. 4. The rearranging means, when the difference between the J-th comparison value and the (J + w) -th comparison value is not larger than the predetermined threshold setting reference value,
The image defect detection device according to claim 1, wherein the threshold value is set based on data input by a user. 5. The image defect detection device detects defects in a plurality of types of images, and the region setting unit sets the N detection regions and the N reference regions to defects in the plurality of types of images. The comparison value calculation unit calculates the N comparison values for each defect of the plurality of types of images, and the rearrangement unit sets the N comparison values of the plurality of types of images. Sorting for each defect, the threshold value setting unit sets the threshold value for each defect of the plurality of types of images, and the defect detection unit is set for each defect of the plurality of types of images. The image defect according to claim 1, wherein a defect in the image is detected for each defect in the plurality of types of images based on a threshold value and N comparison values calculated for each defect in the plurality of types of images. Detection device. 6. The defect of the plurality of types of images includes a point defect, a spot defect, a line defect, and an uneven defect.
The image defect detection device described. 7. The image has color components of a hue system (red, green, blue, etc.), and the comparison value calculation means calculates the N comparison values for each color component, The sorting means sorts the N comparison values for each color component, the threshold setting means sets the threshold value for each color component, and the defect detecting means sets the threshold value for each color component. The image defect detection device according to claim 1, which detects an image defect. 8. An imaging process for imaging an image composed of a plurality of pixels, and N detection regions for detecting defects in the image (N
Is an integer of 2 or more), an area setting step of setting N reference areas corresponding to the N detection areas, and a plurality of pixels for displaying a plurality of pixels in each detection target area. Based on the display data and a plurality of pixel display data for displaying each of a plurality of pixels in the reference area corresponding to each detection target area, a comparison value calculating step of calculating a comparison value for each detection area, and A rearrangement step of rearranging the N comparison values calculated by the comparison value calculation step in the order of the values of the N comparison values; and the N comparison values rearranged by the rearrangement step. The difference between the J-th comparison value and the (J + w) -th comparison value from the smaller one of the comparison values (J and w are 1
≦ J ≦ N−w, integers that respectively satisfy 1 ≦ w ≦ N),
A threshold value setting step of setting a threshold value for determining the presence or absence of a defect in the image based on a predetermined threshold value setting reference value; and the threshold value setting step. An image defect detecting method comprising: a defect detecting step of detecting a defect in the image based on the threshold value and the N comparison values calculated by the comparison value calculating step.