WO2015015945A1 - Defect candidate specification device, defect candidate specification method, defect determination device, defect inspection device, defect candidate specification program, and recording medium - Google Patents

Abstract

A defect candidate specification device according to the present invention is provided with a defect candidate specification unit (21) for specifying defect candidates in image data on the basis of defect candidate information that is updated with second closed areas, which are obtained through the execution of a second binarization process based on a second threshold that is different from a first threshold, as new defect candidates if the number of second closed areas is greater than the number of first closed areas, which are obtained through the execution of a first binarization process using the first threshold.

Description

Defect candidate identification device, defect candidate identification method, defect determination device, defect inspection device, defect candidate identification program, and recording medium

The present invention relates to a defect candidate identification device, a defect determination device, and a defect inspection device related to inspection of defects present in a flat plate such as a sheet, a glass plate, or a panel, or a plate having a gentle curvature.

In general, in the manufacturing process of various industrial products such as liquid crystal panels, various defects such as surface scratches may occur in some products, so captured images are used to detect product defects within the manufacturing process. The tests used are widely performed.

For example, as an inspection method using a captured image, as disclosed in Patent Document 1, the image is divided into small areas, luminance values are averaged for each small area, and a difference from the original image is obtained. A technique for extracting a pixel having a certain brightness or more as a defective portion is known.

In addition, as a simple defect determination method for defect detection using a captured image, a range of gradation values of a normal part of the captured image is set using a threshold value, and the gradation value of the captured image is binary with the threshold value. There is a method using binarization processing in which a part that deviates from a threshold value is determined as a defect candidate.

Japanese Patent Publication “Patent No. 4893788” (issued March 7, 2012)

However, the defect determination method using a simple binarization process has the following problems.

In the defect determination method using the binarization process, it is necessary to set a threshold value for performing the binarization process on the normal luminance value side in order to prevent a defect from being overlooked. However, when the threshold is set to the normal gradation value side in this way, if the size or shape of each defect candidate is determined from the binarized image, the size of each defect is apparently determined to be one size larger. In addition, the shape is not correctly determined. In particular, when a plurality of defect candidates exist adjacent to each other, each defect is determined to be one size larger, so that the adjacent defect candidates are merged, and each of the defect candidates is small in size. Even if it is a simple defect candidate, the entire group of a plurality of adjacent defect candidates is determined as a single defect candidate having a very large size.

Such a problem becomes particularly noticeable when the size, shape, number, etc. of defect candidates are used for defect determination. For example, when the defect candidate size information is used for defect determination, when a defect candidate of a certain size or smaller is determined to be normal and a defect candidate of a certain size or larger is determined to be a defect, the defect candidate size is increased due to the above problem. Therefore, a defect candidate having a small size that should be determined as normal is determined as a defect. Furthermore, when the defect detected in the inspection process is corrected in the downstream correction process based on the defect detection information in the inspection process, there is a big problem in process management such as useless load in the correction process.

The present invention has been made in view of the above-described problems, and an object of the present invention is to provide a defect candidate specifying device and a defect candidate specifying method capable of reducing the processing load in defect determination and accurately performing defect determination. It is to provide.

In order to solve the above-described problem, a defect candidate specifying device according to an aspect of the present invention performs first binarization using a first threshold value on image data, using a first binarization process. Based on the binarization processing means and the image data after binarization processing generated by executing the first binarization processing, the first closed region that is the region surrounded by the closed curve in the image data is identified First closed area identifying means, defect candidate information creating means for creating defect candidate information including information for specifying the defect candidate in image data, with the first closed area as a defect candidate, Defect candidate specifying means for specifying defect candidates in image data based on defect candidate information, target area setting means for setting an area including at least one of the first closed areas in the image data as a target area, and the image data Then, for each target region, at least one second binarized image after executing the second binarization process based on the second threshold value different from the first threshold value at least once. On the basis of the second binarization processing means for generating data and the image data after the second binarization processing generated by the execution of the second binarization processing, a closed curve in the image data A second closed region identifying means for identifying a second closed region that is an enclosed region, the number of first closed regions for each of the target regions, and at least one second second binary value The number of second closed regions in the image data after the image processing is compared, and when the number of second closed regions is larger, the second closed region is set as a new defect candidate, and the defect candidate information Is updated to defect candidate information that includes information for identifying the new defect candidate. A defect candidate information updating unit that, the defect candidate specifying means, a defect candidate information updated by the defect candidate information updating means is characterized by identifying the defect candidate in the image data.

In order to solve the above-described problem, a defect candidate specifying method according to an aspect of the present invention is a defect candidate specifying method for specifying a defect candidate from image data. A closed curve in the image data based on the first binarization processing step for executing one binarization processing and the image data after the binarization processing generated by the execution of the first binarization processing Including a first closed region identifying step for identifying a first closed region that is an area surrounded by, and information for specifying the defect candidate in the image data with the first closed region as a defect candidate A defect candidate information creating step for creating defect candidate information; a defect candidate identifying step for identifying a defect candidate in image data based on the defect candidate information; and a region including at least one of the first closed regions in the image data. A second binarization process based on a second threshold value different from the first threshold value is executed at least once for each target region for the target region setting step set as an elephant region and the image data A second binarization processing step for generating at least one image data after the second binarization processing, and the second binarization generated by executing the second binarization processing. Based on the processed image data, a second closed region identifying step for identifying a second closed region that is a region surrounded by a closed curve in the image data, and for each target region, the first closed region When the number of the second closed areas in the image data after the second binarization processing is larger than the number of the second closed areas, the number of the second closed areas is larger As a new defect candidate and the defect candidate information as the new defect candidate. A defect candidate information update step for updating to defect candidate information including information for specifying a complement, and the defect candidate specification step is based on the defect candidate information updated by the defect candidate information update step, A defect candidate in the image data is specified.

According to one aspect of the present invention, it is possible to reduce the processing load in determining a defect and perform the defect determination with high accuracy.

It is a schematic block diagram of the external appearance inspection apparatus which concerns on Embodiment 1 of this invention. It is a schematic block diagram of the image processing part with which the appearance inspection apparatus shown in FIG. 1 is provided. It is a flowchart which shows the flow of the defect determination process in the image process part shown in FIG. (A)-(e) is the figure which showed typically the flow of the defect determination process by the image process part shown in FIG. It is a figure which shows the number transition of the closed area at the time of changing the 2nd threshold value in the image processing part shown in FIG. It is the figure which showed the other example which shows the number transition of the closed area at the time of changing the 2nd threshold value in the image process part shown in FIG. It is a figure which shows transition of the threshold value used for the binarization process used with the external appearance inspection apparatus which concerns on Embodiment 2 of this invention. It is a figure which shows transition of the threshold value used for the binarization process used with the external appearance inspection apparatus which concerns on Embodiment 3 of this invention. It is a figure which shows the number transition of the closed area at the time of using the transition of the threshold value used for the binarization process shown in FIG. It is a figure which shows transition of the threshold value used for the binarization process used with the external appearance inspection apparatus which concerns on Embodiment 4 of this invention. It is a figure which shows the number transition of the closed area at the time of using the transition of the threshold value used for the binarization process shown in FIG.

Embodiment 1
An embodiment of the present invention will be described as follows.

FIG. 1 is a schematic diagram showing a configuration of an appearance inspection apparatus 1 according to the present embodiment. In the present embodiment, an example in which the appearance inspection apparatus 1 is used as an apparatus for inspecting the appearance of a panel in a liquid crystal display manufacturing process will be described. The appearance inspection apparatus 1 may be an apparatus that inspects other things than the liquid crystal display, such as an apparatus that inspects a silicon substrate. Alternatively, the appearance inspection apparatus 1 can be applied to, for example, an apparatus that inspects graphics, characters, and the like formed on a printed material, in addition to the above inspection.

(Overview of the appearance inspection apparatus 1)
As shown in FIG. 1, the appearance inspection apparatus 1 includes a camera 101, an illuminator 201, a stage 301, an image processing unit 100, a controller 200, and an apparatus control unit 300. On the stage 301, a work 302 as an inspection object is placed.

The camera 101 that is the imaging unit and the image processing unit 100 that is a defect determination device that determines defects from image data generated by the camera 101 constitute a defect inspection apparatus.

The camera 101 is an imaging unit that images the workpiece 302 to be inspected placed on the stage 301, and is a CCD (Charge Coupled Device) camera or a line sensor camera, for example. The illuminator 201 is an illuminator using, for example, an LED light source.

The controller 200 is a control unit that sends control signals to the camera 101, the illuminator 201, and the stage 301 to perform desired control.

Specifically, the controller 200 controls the position of the stage 301 by transmitting a control signal to the stage 301. Thereby, the stage 301 is driven according to the control signal from the controller 200.

The controller 200 executes control to transmit light for irradiating the stage 301 to the illuminator 201. Thereby, the illuminator 201 adjusts the irradiation direction and the amount of light based on the signal from the controller.

The controller 200 controls the imaging of the workpiece 302 to be inspected by transmitting a control signal to the camera 101. As a result, the camera 101 captures the entire workpiece 302 or a predetermined range based on the control signal from the controller 200, and outputs the image data to the image processing unit 100.

In this embodiment, a line sensor camera is used as the camera 101, LED bar illumination in which a plurality of LEDs are arranged in a line is used as the illuminator 201, and a stage that is driven in one axial direction is used as the stage 301.

The device control unit 300 transmits a predetermined control signal to the controller 200 based on an instruction input from the outside or when a predetermined condition is satisfied, and causes the controller 200 to control the operation of each unit described above. .

Further, the apparatus control unit 300 generates a predetermined control signal different from the control signal transmitted to the controller 200 based on an instruction input from the outside or when a predetermined condition is satisfied. Also send to 100.

The image processing unit 100 executes predetermined image processing on the image data output from the camera 101 based on a control signal (command) from the device control unit 300, and sends the result to the device control unit 300. Output.

(Details of the image processing unit 100)
The image processing unit 100 will be described in detail below.

FIG. 2 is a block diagram illustrating a schematic configuration of the image processing unit 100.

The image processing unit 100 includes an input unit 11, a data recording unit 12, an output unit 13, and a control unit (defect determination device) 14.

The input unit 11 accepts data input from the outside. This data includes the image data output by the camera 101 and the control data output by the apparatus control unit 300.

The data recording unit 12 stores data for executing predetermined image processing, image data input by the camera 101, and other data necessary for image processing.

The output unit 13 outputs the result of the image processing executed by the control unit 14 to the device control unit 300.

The control unit 14 selects defect candidates specified by the defect candidate specifying unit (defect candidate specifying device) 21 and the defect candidate specifying unit 21 for performing processing for specifying defect candidates in the image data input from the input unit 11. A defect determination unit 22 that performs processing for determining a true defect based on a predetermined standard is included.

The defect candidate specifying unit 21 includes a binarization processing unit 41, an identification unit 42, a target area setting unit 43, a threshold value calculation unit 44, a defect candidate information creation unit (defect candidate information creation unit) 45, a threshold value determination unit 46, and a defect candidate. An information updating unit 47 and a specifying unit (defect candidate specifying means) 48 are included.

A binarization processing unit (first binarization processing unit, second binarization processing unit) 41 binarizes image data from the input unit 11 or the data recording unit 12 using a threshold value. A processing unit that executes processing. The threshold value may be either the first threshold value or the second and third threshold values calculated by a calculation process described later. The first threshold may be a predetermined value or a value calculated by a dynamically determined threshold determination method.

When the binarization processing unit 41 functions as a first binarization processing unit, the first binarization processing using the first threshold value is performed on the image data input from the input unit 11. . In addition, when the binarization processing unit 41 functions as a second binarization processing unit, each target region set by executing the first binarization processing is different from the first threshold value. The second binarization process using the two threshold values is executed at least once to generate image data after at least one second binarization process.

Further, when the binarization processing unit 41 functions as a second binarization processing unit, the second threshold value is changed in a stepwise manner for each target region set by the target region setting unit 43. The binarization process may be executed a plurality of times. Details of setting of the target area by the target area setting unit 43 will be described later.

The identification unit (first closed region identification unit, second closed region identification unit) 42 is based on data (binarization data) generated by the binarization processing unit 41 by executing the binarization process. Then, at least one or more closed regions (regions surrounded by a closed curve) listed as defect candidates are identified from the background in the image based on the image data.

When the identification unit 42 functions as a first closed region identification unit, the binarization data obtained by executing the first binarization processing in the binarization processing unit 41 is surrounded by a closed curve in the image data. Identified closed region. When the identification unit 42 functions as a second closed region identification unit, the closed curve in the image data is obtained from the binarized data obtained by executing the second binarization process in the binarization processing unit 41. Identifies the closed region surrounded by.

The target area setting unit (target area setting means) 43 sets a target area including at least one closed area identified by the identification unit 42. That is, the target area setting unit 43 sets a target area including at least one of the identified closed areas in the image data.

As a general rule, it is desirable to set as many target areas as the number of closed areas so that one target area corresponds to one closed area. However, since it may be unavoidable that multiple closed areas are included in one target area, such as when the closed areas are too close together, here the target area including at least one closed area Is supposed to be set.

The threshold calculation unit 44 calculates the threshold and the threshold range for the target area determined by the target area setting unit 43 based on the input image data. Here, the calculated threshold value is a second threshold value that is different from the first threshold value.

The second threshold value is a threshold value for the binarization processing unit 41 to binarize a closed region listed as a defect candidate.

The defect candidate information creating unit 45 uses the first closed region identified by the identifying unit 42 as a defect candidate, and creates defect candidate information including information for specifying the defect candidate in the image data.

Here, the information for specifying the defect candidate in the image data included in the defect candidate information is specifically information including coordinate information indicating the coordinate position of the defect candidate in the image data. This coordinate information may be a coordinate list in which the coordinates of defect candidates are listed. Thus, in order to specify the defect candidate existing in the image data, the coordinate information of the defect candidate described above is indispensable. In addition, the defect publication information includes information on the size and shape of the defect candidate. You may go out. The size and shape information of these defect candidates is preferably a list associated with the coordinate information. The defect candidate information created in this way is temporarily stored in the data recording unit 12, read out as necessary, used for specifying defect candidates, and read out and updated as necessary. The The update of the defect candidate information will be described later.

The threshold value determination unit 46 determines a third threshold value corresponding to the region listed as the defect candidate based on the defect candidate information created by the defect candidate information creation unit 45.

The third threshold is the target area determined by the target area setting unit 43 based on the binarized data obtained by the binarization processing unit 41 performing the binarization process with the second threshold. This is a threshold for binarization processing.

The defect candidate information update unit 47 is a defect candidate information update unit that updates the defect candidate information created by the defect candidate information creation unit 45.

For example, the defect candidate information update unit 47 for each target region set by the target region setting unit 43, the number of first closed regions and the first image data in the image data after at least one second binarization process. If the number of second closed regions is larger than the number of second closed regions, the second closed region is set as a new defect candidate, and the defect candidate information is used to identify the new defect candidate. Update to defect candidate information including information for

The identifying unit 48 identifies defect candidates in the image data based on the defect candidate information updated by the defect candidate information updating unit 47. The specifying unit 48 also specifies defect candidates in the image data based on defect candidate information that has not been updated by the defect candidate information updating unit 47.

In the above example, the defect candidate information update unit 47 calculates the number of first closed regions obtained by executing the first binarization process on the same target region set in the image data, and the second The defect candidate information is updated by comparing the number of second closed regions obtained by executing the binarization process. However, the defect candidate information update process by the defect candidate information update unit 47 is not limited to the above example, and after the comparison between the number of the first closed regions and the number of the second closed regions. The second threshold value is changed stepwise, and the second binarization process using the changed second threshold value is sequentially executed to generate a plurality of binarized image data, For each target region, the number of second closed regions obtained by using the second threshold value used for updating the defect candidate information by the defect candidate information updating unit is different from the second threshold value by at least one level. The number of second closed regions obtained using the second threshold is compared, and the second closed region having a large number is used as a new defect candidate, and the defect candidate information is used to identify the new defect candidate. It is also possible to update the defect candidate information including the information for doing so.

In this way, information on the defect candidate specified by the defect candidate specifying unit 21 is sent to the subsequent defect determining unit 22.

The defect determination unit 22 determines whether or not the defect is a defect in the image data based on the information regarding the specified defect candidate.

Here, the information on the identified defect candidate is information indicating a feature amount for identifying the defect such as the defect area and size. Therefore, the defect determination unit 22 determines whether or not the specified defect candidate is a true defect based on the information regarding the specified defect candidate. For example, the defect determination unit 22 uses the defect feature amount (defects) stored in advance in the data recording unit 12 as information on the identified defect candidates (area and size of defect candidates) sent from the defect candidate identification unit 21. To determine whether or not the defect candidate is a true defect. Thereby, it is possible to determine the presence or absence of defects in the image data, that is, the presence or absence of defects in the workpiece 302 to be inspected. The defect determination unit 22 determines a defect based on the result of the target area listed as a defect candidate binarized with a third threshold described later. A specific example of this defect determination will be described later.

The determination result by the defect determination unit 22 is output to the output unit 13 to notify the user of the presence or absence of defects in the workpiece 302 to be inspected. For example, if the output destination of the output unit 13 is a monitor, the determination result output from the defect determination unit 22 is displayed in a state that can be recognized by the user on the monitor, and the presence or absence of a defect in the workpiece 302 to be inspected is notified. . If the output destination of the output unit 13 is a printer, the determination result output from the defect determination unit 22 is printed out.

What is important here is that the defect candidate information is sequentially updated for each comparison by sequentially comparing the number of closed curves between the binarized data at different thresholds by one stage, and the final defect. In the candidate information, the number, position, size, shape, etc. of defect candidates are accurately determined. Below, the flow of a defect determination process is demonstrated.

(Defect determination processing by image processing unit 100 (1))
Next, a first procedure of defect determination processing by the image processing unit 100 will be described. Here, an example in which the defect determination process is performed by performing the first binarization process once and the second binarization process at least once will be described.

FIG. 3 is a flowchart showing the flow of the defect determination process.

First, the defect candidate specifying unit 21 of the control unit 14 detects input of image data from the input unit 11 (step S11). Here, the image data is image data of the workpiece 302 to be inspected placed on the stage 301 captured by the camera 101.

Next, the binarization processing unit 41 of the defect candidate specifying unit 21 binarizes the entire image with the first threshold (Th = Th1) (step S12: first binarization processing step). That is, the binarization processing unit 41 performs a first binarization process using the first threshold value on the input image data. Specifically, the binarization processing unit 41 of the defect candidate specifying unit 21 uses the first threshold value Th <b> 1 read from the data recording unit 12 for the entire image data whose input has been detected. The process is being executed. The binarization process at this time is a first binarization process with the first threshold Th1 stored in the data recording unit 12, and screening that screens areas that are listed as defect candidates in the image Plays the role of Here, the first threshold value is a preset threshold value. The first threshold value is preferably set in advance for each type of workpiece 302 (liquid crystal display, silicon substrate, etc.) to be inspected. Alternatively, the first threshold value may be dynamically determined from the position in the work to be inspected, the luminance value in the image, or the like.

It should be noted that the entire image data to be subjected to the first binarization process using the first threshold value includes those that follow the entire image data. For example, it may be the one excluding the peripheral part of the image. For example, when the image data is large, the data divided into several according to a predetermined number or size is regarded as the entire image data and handled. Also good.

Next, in step S12, the identification unit 42 of the defect candidate specifying unit 21 uses the first binarization data obtained by executing the first binarization process, and the first surrounded by the closed curve in the image data. A closed region is identified (step S13: first closed region identifying step).

Subsequently, the target region setting unit 43 of the defect candidate specifying unit 21 sets a target region including at least one first closed region identified in step S13 (step S14: target region setting step). The target area set here may be one or plural.

Next, the binarization processing unit 41 of the defect candidate specifying unit 21 functions as a second binarization processing unit, and for each target region set in step S14, a second different from the first threshold Th1. Using the threshold value Th2, the second binarization process is executed at least once to generate image data after at least one second binarization process (step S15: second binarization process step) ).

Here, the second threshold Th2 used in the second binarization process may be set in advance or calculated from the luminance value of the input image data. For example, the maximum luminance value (MAX) of each of the target areas set in step S14 is calculated, the range of the second threshold value (Th2) (Th1 <Th2 <MAX) is determined in the corresponding area, and this You may make it set suitably within the range.

Further, the identification unit 42 of the defect candidate specifying unit 21 determines the second closed region surrounded by the closed curve in the image data from the binarized data obtained by executing the second binarization process in step S15. (Step S16: second closed region identifying step).

Subsequently, the defect candidate information creating unit 45 of the defect candidate specifying unit 21 sets the first closed region identified in step S13 as a defect candidate, and includes defect candidate information including information for specifying the defect candidate in the image data. Is created (step S17: defect candidate information creation step).

Next, the defect candidate information updating unit 47 of the defect candidate specifying unit 21 determines whether or not to update the defect candidate information created in step S17 (step S18). Here, for each target region set in step S14, the number of first closed regions identified in step S13 and the second closed region identified in step S16, that is, at least one second two regions. The number of second closed regions in the image data after the value processing is compared. As a result of the comparison, if the number of the second closed regions is larger, it is determined that the defect candidate information needs to be updated (YES), the process proceeds to step S19 (defect candidate information update step), and the number of the first closed regions is greater. If so, it is determined that the defect candidate information update is unnecessary (NO), and the process proceeds to step S20 (defect candidate specifying step).

In step S19, the defect candidate information update unit 47 updates the defect candidate information created in step S17 to new defect candidate information using the second closed region as a defect candidate.

In step S20, the defect candidate in the image data is specified by the defect candidate information created in step S17 or the defect candidate information updated in step S19. The processing so far shows the processing of the defect candidate specifying method of the present invention.

Finally, it is determined whether or not the defect candidate is a defect in the image data based on the information on the defect candidate specified in step S20 (defect candidate area and size) (step S21).

In the above defect determination process, the example in which the second binarization process is performed once in step S15 has been described. However, in order to improve the accuracy of defect determination, the second binarization process is repeated. It is preferable to carry out a plurality of times.

Here, in the binarization processing unit 41, the second binarization process is performed repeatedly by changing the second threshold Th2 stepwise in the above range (Th1 <Th2 <MAX). This means that the second binarization process is sequentially executed using the changed second threshold Th2. In the present embodiment, for each target region set by executing the first binarization process, the second threshold Th2 is sequentially decreased from MAX to Th1 by one gradation, and the binarization process is performed. Shall be performed. Further, in order to reduce the number of times of the second binarization process, the second threshold value Th2 may be decreased stepwise at intervals. In any case, it is assumed that the second binarization process is sequentially performed step by step in one direction in which the second threshold value Th decreases or increases.

When executing the second binarization process a plurality of times, if the threshold value is changed stepwise so that it gradually becomes less than the first second threshold value, the following two behaviors of the closed curve are obtained.

(1) The closed curve disappears at a certain threshold while the closed region gradually decreases. (Reduction of closed curve).

(2) The closed region gradually becomes smaller, and at the same time, the closed region is divided into a plurality at a certain threshold. (Increase in closed curve)
In step S18, the number of first closed regions and the number of second closed regions are compared to determine whether or not the defect candidate information is to be updated. In the case where the binarization process is repeated and executed a plurality of times, it may be determined whether or not the defect candidate information is updated according to the result of comparing the number of second closed regions. That is, in the case of (2), the defect candidate information update unit 47 determines whether or not the closed curve has increased (step S18), and when the closed curve has increased (YES in step S18), the defect candidate information is updated. (Step S19).

Hereinafter, a process of performing defect determination by executing the second binarization process a plurality of times will be described.

(Defect determination processing by image processing unit 100 (2))
Next, a second procedure of defect determination processing by the image processing unit 100 will be described. Here, an example will be described in which the second binarization process is repeatedly performed a plurality of times, the closed area in the target area is separated, a new target area is set, and the defect determination process is performed.

Here, the target region setting unit 43 of the defect candidate specifying unit 21 includes a plurality of at least one increased closed region for each target region every time the defect candidate information update unit 47 updates the defect candidate information. This area is reset as a new target area.

Specifically, the target region setting unit 43 of the defect candidate specifying unit 21 determines whether or not the target region (closed region) listed as the defect candidate is separated from the defect candidate information updated by the defect candidate information update unit 47. Determine whether. More specifically, the target area setting unit 43 monitors the number of closed areas in the result after the second binarization process performed repeatedly. Here, when the number of closed regions decreases from 3 to 2, and decreases from 2 to 1, a peak appears within the second threshold (Th2) range (Th1 <Th2 <MAX). It is determined that there are 3 and 2 respectively. Therefore, it can be seen that there is a threshold for separating the target areas listed as defect candidates.

Alternatively, the target area setting unit 43 monitors the barycentric position of the closed area in the result after the second binarization process performed repeatedly. In this case, the centroid position of the target area listed as the defect candidate is compared with the centroid position of each closed area. When the second threshold Th2 is a value close to the first threshold Th1, the center of gravity position of the target region listed as the defect candidate matches the center of gravity of the closed region after the second binarization process, or in the image However, when there are a plurality of peaks, the position of the center of gravity of each peak is different from the position of the center of gravity of the target region listed as a defect candidate. Therefore, by comparing the gravity center position of the closed region and the gravity center position of the target region listed as the defect candidate, it can be seen that there is a threshold for separating the target regions listed as the defect candidates.

That is, when the target region setting unit 43 determines that the closed region is to be separated, the threshold value determination unit 46 sets the found threshold value as the third threshold value (Th = Th3).

On the other hand, when the target region setting unit 43 determines not to separate the closed region, the defect candidate information before being updated in the defect candidate information update unit 47 is stored in the data recording unit 12 as a determination result.

Subsequently, the binarization processing unit 41 of the defect candidate specifying unit 21 performs the second binarization processing of the target area listed as the defect candidate with the third threshold Th3 set as described above.

The defect determination unit 22 calculates a feature amount such as the number and size of the closed regions after the binarization processing based on the result of the target region listed as the defect candidate binarized with the third threshold, A closed region having a certain feature amount or more is determined as a defective defect, and the result is stored in the data recording unit 12. For example, a defect having a diameter of 180 μm or more is determined as a defective product, a size of 1 mm or more in the X direction or the Y direction is determined as a defective product, or a plurality of feature amounts are determined in combination.

By determining the threshold value for each target region listed as the defect candidate thus obtained, the defect inspection for accurately identifying the defect candidate and determining the true defect from the identified defect candidate is performed with high accuracy. be able to.

Here, in order to accurately perform defect inspection for determining a true defect, each time the defect candidate information update unit 47 updates the defect candidate information, the area of the closed region to be compared is updated. The information on the defect candidate size in the defect candidate information may be updated as the size information of the correct defect candidate.

(Panel inspection)
Next, panel inspection using the above-described defect determination processing will be described below with reference to FIGS. In this panel inspection standard, a defect having a diameter of 180 μm or more is determined as a defective defect, and a defect having a diameter of 180 μm or less is regarded as a non-defective product regardless of the proximity distance.

4A is an input image of the workpiece 302 (panel) on the stage 301 captured by the camera 101 of the appearance inspection apparatus 1 shown in FIG. 1, and FIG. 4B is a binarization process. This is an image after the entire image is binarized by the first threshold Th1 by the unit 41. Here, binarization processing was performed with the first threshold Th1 = 100 (in the 255 gradation). As a result of the binarization process, a defect candidate bright spot of 320 μm × 180 μm was obtained in a region surrounded by a dotted line in FIG. FIG. 4C shows the result of extracting (specifying) the target area set by the target area setting unit 43 of the appearance inspection apparatus 1 from the original image as a defect candidate.

Next, the control unit 14 measures the gradation with the highest luminance in the original image of the region listed as the defect candidate. In the panel to be inspected, the maximum gradation MAX was 200 gradations (of 255 gradations). As a result, the threshold value calculation unit 44 determines the range of the second threshold value Th2 as 100 <Th2 <200.

Next, the binarization processing unit 41 repeatedly performs binarization processing of the areas listed as defect candidates within the range defined by the threshold value calculation unit 44. In the present invention, the second threshold Th2 is sequentially changed from the maximum threshold MAX to the first threshold Th1. That is, the second threshold value is changed stepwise so as to gradually decrease from the maximum threshold value MAX toward the first threshold value Th1, and the second binarization process is sequentially performed.

FIG. 5 shows the result of iteratively performing the second binarization process. The vertical axis represents the second threshold value, and the horizontal axis represents the position in the region. 5 represents the number, shape, and area of closed regions obtained from the defect candidate information updated by the defect candidate information update unit 47 after performing binarization processing with respective threshold values. Is. In the state where the second threshold Th2 is close to the maximum value MAX, the number of closed regions is one, but when the second threshold Th2 is less than 170, a new closed region is observed. When the threshold value was further lowered, when the second threshold value Th2 was less than 120, the closed region was connected to become one. FIG. 4D is a graph showing the result of repeated binarization processing. From the results of FIG. 4D and FIG. 5, the threshold value determination unit 46 determines the third threshold value Th3 as Th3 = 120. FIG. 4 (e) shows the result of binarization processing of the regions listed as defect candidates with the third threshold Th3. The defect determination unit 22 measured the defect sizes of 150 μm and 110 μm in diameter. The defect determination unit 22 compares each feature amount with the inspection standard, determines that the defect included in the defect candidate area is a non-defective defect, and the output unit outputs the panel as a non-defective product. By using this method, two defects included in a region listed as a defect candidate can be separated and detected, and the feature amount and the inspection standard can be collated. Since the performance of the inspection is improved and the panel that has been determined to be defective can be saved, as a result, the yield is improved.

In the present embodiment, as shown in FIG. 5, the transition of defects in the target area is measured by the number of closed areas, but the centroid position of each closed area and the centroid position of the areas listed as defect candidates are determined. You may compare. FIG. 6 is a schematic diagram for explaining the technique. FIG. 6 is a diagram illustrating the barycentric position of the closed region after processing with the second threshold on the vertical axis and the second threshold on the horizontal axis. The barycentric coordinates are obtained by the defect candidate information creation unit 45. When the second threshold value Th2 is close to the first threshold value Th1, the centroid position of the closed region subjected to the binarization process with the second threshold value Th2 and the centroid position of the region including the defect candidate substantially coincide. However, when a plurality of defects are included in the area including the defect candidate, the position of the center of gravity is different from the position of the center of gravity of the target area listed as the defect candidate. Therefore, by comparing the gravity center position of the closed region with the gravity center position of the target region listed as the defect candidate, it can be seen that there is a threshold value for separating the target region listed as the defect candidate.

The second binarization process is executed with the threshold value obtained in this way as the third threshold value, and the defect included in the area listed as the defect candidate by the defect determination unit 22 based on the obtained result. Judge the quality of the.

[Embodiment 2]
The following will describe another embodiment of the present invention. For convenience of explanation, members having the same functions as those described in the embodiment are given the same reference numerals, and descriptions thereof are omitted.

In the present embodiment, an example will be described in which the second threshold used in the second binarization process performed in the first embodiment is repeated in a binary search.

As shown in FIG. 7, the first threshold (Th2-1) at the second threshold Th2 is performed at the middle point (Th2-1) of the threshold upper limit (MAX) and the threshold lower limit (Th1). Next, the middle point (Th2-2-1) of the threshold upper limit (MAX) and the first threshold (Th2-1), and the middle point (Th2) of the threshold lower limit (Th1) and the first threshold (Th2-1) -2-2). A change in the number of closed regions and a change in position obtained by performing binarization processing at each threshold value are monitored.

In this way, by searching for threshold values that are limited to the range in which the number and position of closed regions change, the search for the luminance peak and valley positions that constitute the target region listed as a defect candidate is accelerated. be able to. That is, in FIG. 7, since the threshold value (Th2-2-1) in step 2 and the threshold value (Th2-3-1) in step 3 are the same in the number of closed regions and the position of the center of gravity, the second threshold value is set. Skip from Th2-2-1 to Th2-3-1.

In the threshold value (Th2-3-2) and threshold value (Th2-3-3) of step 3, the threshold values (Th2-2-1) and (Th2-2-2) one time before defining the respective threshold values are used. Since there is a difference in the number of closed regions and the position of the center of gravity from the binarization processing result, the search for the region including the threshold value is continued.

As described above, the second threshold value used in the second binarization process is repeated in a binary search, thereby searching for a threshold value that is narrowed down to a range in which the number of closed regions and position change occur. As a result, it is possible to speed up the search for the luminance peak and valley positions constituting the target region listed as the defect candidate. Therefore, it is possible to shorten the processing time from the identification of the defect candidate until the final defect determination result is obtained.

[Embodiment 3]
The following will describe another embodiment of the present invention. For convenience of explanation, members having the same functions as those described in the embodiment are given the same reference numerals, and descriptions thereof are omitted.

In the present embodiment, an example will be described in which another panel having the defect determination result shown in FIG. 8 is used instead of the panel in which the defect determination result shown in FIG. To do. In the panel, a bright spot of 430 μm × 180 μm is generated in the target region set by the target region setting unit 43, that is, the region listed as the defect candidate with the first threshold value. If the range of the second threshold value Th2 is set in the threshold value calculation unit 44, the result is as follows. Since the first threshold value Th1 is 100 and the maximum luminance MAX measured in the target area is 220, the range of the second threshold value Th2 is 100 <Th2 <220. In the binarization processing unit 41, the second binarization processing was repeatedly performed within this range. The result is the graph shown in FIG.

Further, the results of measuring the number, position, area, and the like of the closed regions when the second threshold Th2 is changed within the specified range (100 <Th2 <220) are as shown in FIG. Even in such a case, it is possible to define the thresholds (third threshold Th3) for separating the closed regions as Th3-1 and Th3-2 by the method described in the first embodiment. , It is possible to determine the defects separated by the respective threshold values.

In the present embodiment, a bright spot of 430 μm × 180 μm is generated in the region listed as the defect candidate at the first threshold Th1, but as shown in FIG. 9, this method allows 150 μm × 140 μm, 130 μm. It was found that three defects of × 110 μm and 100 μm × 90 μm are close to each other. Therefore, one defect (smallest, 100 μm × 90 μm) included in the region listed as a defect candidate was determined as a non-defective product, and the remaining two regions were determined as true defects. In this example, there are two true defects included in the defect candidates, but a plurality of true defects may exist.

As described above, according to the defect determination method according to the present embodiment, it is possible to determine a true defect by excluding a defect treated as a non-defective product from the defect candidates, thereby improving the accuracy of the defect determination. it can.

[Embodiment 4]
The following will describe another embodiment of the present invention. For convenience of explanation, members having the same functions as those described in the embodiment are given the same reference numerals, and descriptions thereof are omitted.

In the present embodiment, an example will be described in which another panel having the defect determination result shown in FIG. 10 is used instead of the panel in which the defect determination result shown in FIG. To do. In the panel, a bright spot of 450 μm × 200 μm is generated in the target region set by the target region setting unit 43, that is, the region listed as the defect candidate at the first threshold value. If the range of the second threshold value Th2 is set in the threshold value calculation unit 44, the result is as follows. Since the first threshold Th1 is 100 and the maximum luminance MAX in the target area is 220, the range of the second threshold Th2 is 100 <Th2 <220. The binarization process was repeated within. The result is the graph shown in FIG.

Also, the results of measuring the number, position, area, and the like of the closed regions when the second threshold Th2 is changed within the specified range (100 <Th2 <220) are as shown in FIG. From FIG. 11, even when the second binarization process is repeatedly performed while changing the second threshold Th2, the number of areas remains one, and the center of gravity position is also a defect candidate. There was no difference. In the case of such a defect, the third threshold value is set to be the same as the first threshold value Th1, 450 μm × 200 μm exists in the region listed as the defect candidate, and it is determined as a defective panel as compared with the determination criterion. .

As described above, according to the defect determination method according to the present embodiment, it is not necessary to perform the processing until the third threshold value is set, so that the time from the defect candidate until the true defect is determined is greatly increased. It can be shortened.

[Example of software implementation]
The control block (particularly the image processing unit 100) of the appearance inspection apparatus 1 may be realized by a logic circuit (hardware) formed in an integrated circuit (IC chip) or the like, or using a CPU (Central Processing Unit). It may be realized by software.

In the latter case, the image processing unit 100 includes a CPU that executes instructions of a program that is software that realizes each function, and a ROM (Read Only Memory) in which the program and various data are recorded so as to be readable by a computer (or CPU). Alternatively, a storage device (these are referred to as “recording media”), a RAM (Random Access Memory) for expanding the program, and the like are provided. And the objective of this invention is achieved when a computer (or CPU) reads the said program from the said recording medium and runs it. As the recording medium, a “non-temporary tangible medium” such as a tape, a disk, a card, a semiconductor memory, a programmable logic circuit, or the like can be used. The program may be supplied to the computer via an arbitrary transmission medium (such as a communication network or a broadcast wave) that can transmit the program. The present invention can also be realized in the form of a data signal embedded in a carrier wave in which the program is embodied by electronic transmission.

[Summary]
The defect candidate specifying device according to the first aspect of the present invention provides a first binarization processing unit (binarization processing unit) that executes a first binarization process using a first threshold on image data. 41) and the image data after the binarization process generated by the execution of the first binarization process, the first closed area that is the area surrounded by the closed curve in the image data is identified. First closed region identification means (identification unit 42), and defect candidate information creation that uses the first closed region as a defect candidate and creates defect candidate information including information for specifying the defect candidate in image data Means (defect candidate information creating unit 45), defect candidate specifying means (defect candidate specifying unit 48) for specifying a defect candidate in image data based on the defect candidate information, and at least one first closed region in the image data. Target area And a second binarization process based on a second threshold value that is different from the first threshold value for each target region with respect to the image data. A second binarization processing unit (binarization processing unit 41) for generating at least one second binarized image data executed at least once, and the second binarization processing Based on the image data after the second binarization process generated by the execution of the second closed region identifying means for identifying the second closed region that is the region surrounded by the closed curve in the image data ( The identification unit 42) compares the number of first closed regions with the number of second closed regions in the image data after at least one second binarization process for each target region, If the number of second closed areas is larger, the second closed area Defect candidate information updating means (defect candidate information updating unit 47) for updating the defect candidate information to defect candidate information including information for specifying the new defect candidate as a defect candidate, The specifying means (defect candidate information updating unit 47) is characterized by specifying defect candidates in the image data from the defect candidate information updated by the defect candidate information updating means.

According to said structure, the 1st enclosed by the closed curve identified from the binarization data obtained by performing the 1st binarization process using a 1st threshold value with respect to image data. Using the closed region as a defect candidate, defect candidate information including information for specifying the defect candidate in the image data is created. In addition, an area including at least one of the first closed areas in the image data is set as a target area, and the second threshold different from the first threshold is set for each target area for the image data. The second binarization process is executed at least once to generate image data after at least one second binarization process. Then, for each target region, the number of first closed regions is compared with the number of second closed regions in the image data after at least one second binarization process. When the number is larger than the number of the first closed regions, the defect candidate information is updated. And the defect candidate in image data is specified from the updated defect candidate information.

By performing the binarization process in at least two stages in this way, the defect candidates are roughly identified in the first binarization process (first binarization process), and the next binarization process (second binarization process) Since the defect candidates can be specified in detail in the binarization process), in order to realize the same accuracy of defect candidate identification, at least as compared with the case where the binarization process is executed with one threshold value. The processing load can be reduced when the binarization processing is executed with two threshold values.

Therefore, it is possible to reduce the processing load from the defect candidate identification to the final defect determination, and to perform the defect determination with high accuracy.

In the defect candidate specifying device according to aspect 2 of the present invention, in the aspect 1, the second binarization processing unit (binarization processing unit 41) changes the second threshold value step by step, and For each target region set by the target region setting means (target region setting unit 43), the second binarization process is sequentially executed using the changed second threshold value, and a plurality of binarization processes are performed. Image data is generated, and the defect candidate information updating unit (defect candidate information updating unit 47) uses, for each target area, the second threshold value used for updating the defect candidate information by the defect candidate information updating unit. The number of second closed regions obtained is compared with the number of second closed regions obtained using a second threshold that is at least one step different from the second threshold. The closed region is set as a new defect candidate, and the defect candidate information is specified as the new defect candidate. It may be updated to include it defect candidate information the information to.

According to the above configuration, since the second binarization process is repeatedly performed by changing the second threshold value stepwise, defect candidate information is created for each changed second threshold value. The Then, with respect to the number of closed regions in the image data after the second binarization process used for the creation of the latest defect candidate information, the second two performed next with a threshold different from that of the image data. The number of closed regions in the image data after the binarization processing is compared as a comparison target, and when the number of closed regions is larger in the comparison target, the portion of the comparison target closed region is determined as a new defect candidate, The defect candidate information is updated. Based on the updated defect candidate information, a defect candidate in the image data is specified.

In this way, the defect candidate information is sequentially updated for each comparison, and the defect candidates in the image data are identified in the final defect candidate information, thereby accurately determining the number, position, size, shape, etc. of the defect candidates. It becomes possible to do.

In the defect candidate specifying device according to aspect 3 of the present invention, in the aspect 2, the target area setting unit (target area setting unit 43) is the defect candidate information updating unit (defect candidate information updating unit 47). Each time the information is updated, a plurality of target areas including at least one increased closed area may be reset as new target areas for each target area.

According to the above configuration, each time the defect candidate information update unit updates the defect candidate information, a plurality of target areas including at least one increased closed area are re-created as new target areas for each target area. By setting, even if the number of closed areas in the target area increases, it is possible to include one closed area in the target area as much as possible.

In the defect candidate specifying device according to aspect 4 of the present invention, in any one of the aspects 1 to 3, the defect candidate information includes defect candidate size information indicating a size of the defect candidate, and the defect candidate information Each time the update means (defect candidate information update unit 47) updates the defect candidate information, the update means updates the defect candidate size information in the defect candidate information with the area of the closed region to be compared as the new defect candidate size. May be.

According to the above configuration, the defect candidate information includes defect candidate size information indicating the size of the defect candidate, and the defect candidate information update unit performs the closed region to be compared each time the defect candidate information is updated. By updating the defect candidate size information in the defect candidate information with the area of the defect size as a new defect candidate size, it becomes possible to perform defect determination based on the defect size.

A defect determination apparatus according to aspect 5 of the present invention includes the defect candidate specifying apparatus (defect candidate specifying unit 21) described in any one of aspects 1 to 4, and the defect candidate specified by the defect candidate specifying apparatus. It is characterized in that it is determined whether or not the defect candidate is a defect in the image data based on the information relating to the above.

According to the above configuration, the processing load required for defect determination can be reduced, and a true defect can be appropriately determined from the defect candidates specified in the image data.

The defect inspection apparatus according to the aspect 6 of the present invention determines the defect from the imaging unit (camera 101) that generates image data obtained by imaging the inspection target (work 302) and the image data generated by the imaging unit. A defect determination device (image processing unit 100), and the defect determination device (image processing unit 100) may be the defect determination device according to any one of the first to fourth aspects.

According to the above configuration, it is possible to realize a defect inspection apparatus capable of reducing the processing load in defect determination and performing defect determination with high accuracy.

A defect candidate specifying method according to aspect 7 of the present invention is a defect candidate specifying method for specifying a defect candidate from image data, and executes a first binarization process using a first threshold value on the image data. Based on the first binarization processing step (step S12) and the image data after binarization processing generated by the execution of the first binarization processing, an area surrounded by a closed curve in the image data A first closed region identifying step (step S13) for identifying the first closed region, and a defect including information for specifying the defect candidate in the image data with the first closed region as a defect candidate A defect candidate information creating step (step S17) for creating candidate information, a defect candidate identifying step (step S20) for identifying a defect candidate in the image data based on the defect candidate information, and the image data A target region setting step (step S14) for setting a region including at least one first closed region as a target region, and a second different from the first threshold for each target region with respect to the image data A second binarization process step (step S15) for executing at least one second binarization process based on the threshold value to generate image data after at least one second binarization process; Based on the image data after the second binarization process generated by the execution of the second binarization process, a second closed area that is an area surrounded by a closed curve in the image data is identified. Second closed region identification step (step S16), and for each target region, the number of first closed regions and the second closed region in the image data after the second binarization process. Compare the number to the second closed region When the number is larger, the second closed region is set as a new defect candidate, and the defect candidate information is updated to update the defect candidate information to defect candidate information including information for specifying the new defect candidate. Including the steps (steps S18 and S19), and the defect candidate specifying step (step S20) specifies defect candidates in the image data based on the defect candidate information updated by the defect candidate information update step. It is characterized by.

According to said structure, there exists an effect similar to the effect in the defect candidate specific device concerning the aspect 1 of this invention.

The defect candidate specifying device (defect candidate specifying unit 21) according to each aspect of the present invention may be realized by a computer. In this case, the image is operated by operating the computer as each unit included in the defect determining device. A defect candidate identification program of a defect candidate identification device that realizes the processing unit by a computer and a computer-readable recording medium that records the defect candidate identification program also fall within the scope of the present invention.

The present invention is not limited to the above-described embodiments, and various modifications are possible within the scope shown in the claims, and embodiments obtained by appropriately combining technical means disclosed in different embodiments. Is also included in the technical scope of the present invention. Furthermore, a new technical feature can be formed by combining the technical means disclosed in each embodiment.

The present invention can be used for an image processing apparatus, for example, an inspection apparatus for inspecting the appearance.

DESCRIPTION OF SYMBOLS 1 Appearance inspection apparatus 11 Input part 12 Data recording part 13 Output part 14 Control part 21 Defect candidate specific part (defect candidate specific apparatus)
22 Defect determination unit (defect determination means)
41 Binarization processing unit (first binarization processing means, second binarization processing means)
42 identification unit (first closed area recognition means, second closed area recognition means)
43 Target area setting unit (target area setting means)
44 threshold calculation unit 45 defect candidate information creation unit (defect candidate information creation means)
46 threshold determination unit 47 defect candidate information update unit (defect candidate information update means)
48 identification part (defect candidate identification means)
100 Image processing unit (defect determination device)
101 Camera (imaging unit)
200 Controller 201 Illuminator 300 Device Control Unit 301 Stage 302 Workpiece (Inspection Object)
Th1 First threshold Th2 Second threshold Th3 Third threshold

Claims (9)

A first binarization processing means for executing a first binarization process using a first threshold on the image data;
Based on the image data after binarization processing generated by executing the first binarization processing, a first closed region for identifying a first closed region that is a region surrounded by a closed curve in the image data. Region identification means;
Defect candidate information creating means for creating defect candidate information including information for specifying the defect candidate in the image data with the first closed region as a defect candidate;
Defect candidate specifying means for specifying defect candidates in image data by the defect candidate information;
Target region setting means for setting a region including at least one of the first closed regions in the image data as a target region;
At least one second binarization is performed on the image data by executing at least one second binarization process based on a second threshold different from the first threshold for each target region. Second binarization processing means for generating image data after processing;
Based on the image data after the second binarization process generated by the execution of the second binarization process, a second closed area that is an area surrounded by a closed curve in the image data is identified. A second closed region identification means;
For each target area, the number of first closed areas is compared with the number of second closed areas in the image data after at least one second binarization process. If there are more defect candidates, the second closed region is set as a new defect candidate, and the defect candidate information updating unit updates the defect candidate information to defect candidate information including information for specifying the new defect candidate. And
The defect candidate specifying device, wherein the defect candidate specifying means specifies defect candidates in image data from the defect candidate information updated by the defect candidate information updating means. The second binarization processing means is:
A second threshold value is changed in a stepwise manner, and a second binarization process is sequentially executed using the changed second threshold value for each target region set by the target region setting means. Generate binarized image data,
The defect candidate information update means includes
For each target region, the number of second closed regions obtained by using the second threshold value used for updating the defect candidate information by the defect candidate information updating unit is different from the second threshold value by at least one level. The number of the respective second closed regions obtained by using the second threshold is compared, and the second closed region having a large number is set as a new defect candidate, and the defect candidate information is used as the new defect. The defect candidate specifying device according to claim 1, wherein the defect candidate information is updated to defect candidate information including information for specifying a candidate. The target area setting means includes:
Each time the defect candidate information update unit updates the defect candidate information, a plurality of target areas including at least one increased closed area are reset as new target areas for each target area. The defect candidate specifying device according to claim 2. The defect candidate information includes size information indicating the size of the defect candidate,
The defect candidate information update means includes
4. The size information in the defect candidate information is updated each time the defect candidate information is updated, with the area of the closed region to be compared as a new defect candidate size. The defect candidate identification device described in 1. A defect candidate specifying device according to any one of claims 1 to 4,
A defect determination device that determines whether or not the defect candidate is a defect in image data based on information on the defect candidate specified by the defect candidate specification device. An imaging unit that generates image data obtained by imaging an inspection object;
A defect determination device that determines a defect from the image data generated by the imaging unit,
The defect inspection apparatus according to claim 5, wherein the defect determination apparatus is the defect determination apparatus according to claim 5. In a defect candidate identification method for identifying defect candidates from image data,
A first binarization process for executing a first binarization process using a first threshold on the image data;
Based on the image data after binarization processing generated by executing the first binarization processing, a first closed region for identifying a first closed region that is a region surrounded by a closed curve in the image data. Region identification process;
A defect candidate information creating step for creating defect candidate information including information for specifying the defect candidate in the image data, with the first closed region as a defect candidate,
A defect candidate specifying step for specifying a defect candidate in the image data by the defect candidate information,
A target region setting step for setting a region including at least one of the first closed regions in the image data as a target region;
At least one second binarization is performed on the image data by executing at least one second binarization process based on a second threshold different from the first threshold for each target region. A second binarization processing step for generating processed image data;
Based on the image data after the second binarization process generated by the execution of the second binarization process, a second closed area that is an area surrounded by a closed curve in the image data is identified. A second closed region identification step;
For each target area, the number of first closed areas is compared with the number of second closed areas in the image data after at least one second binarization process. If there are more defect candidates, the second closed region is set as a new defect candidate, and the defect candidate information update step is performed to update the defect candidate information to defect candidate information including information for specifying the new defect candidate. Including
The defect candidate specifying step is:
A defect candidate specifying method, wherein a defect candidate in the image data is specified based on the defect candidate information updated by the defect candidate information update step. A defect candidate specifying program for causing a computer to function as the defect candidate specifying device according to any one of claims 1 to 4, wherein the computer functions as each of the above means. A computer-readable recording medium on which the defect candidate specifying program according to claim 8 is recorded.

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