KR102613682B1 - Moire detecting method for inspecting defect - Google Patents

Abstract

A moiré detection method and device for foreign matter inspection are disclosed. According to one aspect of the present invention, there is a moire detection device for inspecting foreign matter in a camera module, comprising: an image acquisition unit that acquires an input image captured using the camera module; a candidate group highlighting unit for highlighting a moiré candidate group including an actual foreign material area and a moiré area through primary foreign matter inspection for the input image; and a moiré detection unit for selecting the highlighted moiré candidate group as a region of interest and determining a moiré region exhibiting moiré characteristics through color sensitivity analysis for the region of interest. do.

Description

Moire detection method and device for inspecting foreign substances {Moire detecting method for inspecting defect}

The present invention relates to a moiré detection method and device for inspecting foreign substances during the manufacturing process of displays, camera modules, etc.

Recently, as the demand for electronic devices has rapidly increased, the supply of equipment such as cameras and displays is increasing.

In order to provide reliable products, we use protective tapes to absorb foreign substances and protect the lenses during the manufacturing process. Although protective tape is made of a transparent material, it can cause moiré phenomenon depending on the unit price of the tape, the company's situation, etc.

In this case, there is a problem in that reliable inspection data cannot be obtained as defects similar to foreign substances are output during the product inspection process.

If normal detection of foreign substances is not possible in a situation where protective tape must be used, the shipment volume of the manufacturer is affected, and this causes quality to deteriorate when standards are changed.

Korean Patent Publication No. 10-2005-0117424 (published on December 14, 2005) - Apparatus and method for detecting foreign substances in a digital camera module

The present invention distinguishes between foreign matter and moiré phenomenon, detects and excludes the moiré phenomenon caused by protective tape, and enables normal detection of foreign matter in manufactured products such as cameras, thereby securing the reliability of the inspection. The purpose is to provide a moiré detection method and device.

Other objects of the present invention may be easily understood through the following description.

According to one aspect of the present invention, there is a moire detection device for inspecting foreign matter in a camera module, comprising: an image acquisition unit that acquires an input image captured using the camera module; a candidate group highlighting unit for highlighting a moiré candidate group including an actual foreign material area and a moiré area through primary foreign matter inspection for the input image; and a moiré detection unit for selecting the highlighted moiré candidate group as a region of interest and determining a moiré region exhibiting moiré characteristics through color sensitivity analysis for the region of interest. do.

The candidate group highlighting unit includes an image preprocessing unit that performs predetermined preprocessing on the input image and converts it into a first image having a luminance value; a contrast image generator that generates a contrast image by performing contrast stretching and blur processing on the first image; It may include an image processing unit that performs binarization processing on the contrast image according to an adaptive threshold setting to emphasize the moiré candidate group.

The moiré determination unit includes a region of interest selection unit that selects a binarized data-dense section of the image in which the moiré candidate group is emphasized as a region of interest regarding the location of the moiré candidate group; a color sensitivity calculation unit that analyzes color sensitivity of the image within the region of interest; a filtering unit that applies a rounding function to the color sensitivity analysis result; It may include a determination unit that determines whether the moiré candidate group is a moiré area based on a change trend of the color sensitivity in one or more of the horizontal and vertical directions.

The region of interest selection unit sets a binarized data search block of a predetermined size, moves the binarized data search block in one or more of the horizontal and vertical directions, and searches for the presence or absence of binarized data, and binarized data within the binarized data search block. When included, a region twice the size of the area corresponding to the binarized data search block can be selected as the region of interest based on the starting point of the binarized data.

The color sensitivity calculator may calculate the color sensitivity by dividing the region of interest into a predetermined number of ROI divisions and calculating an average value of R/G or B/G for each ROI division as color ratio data. .

The color sensitivity calculation unit may take a complement to the color sensitivity.

The filtering unit may round the color sensitivity data to the fourth decimal place through the rounding function and perform binarization if a difference occurs.

The determination unit counts the amount of change in binarized data within the ROI division area, and if more than a predetermined amount is counted, counts the corresponding ROI division area. If the total count amount of the ROI division unit is more than a predetermined standard, the region of interest is a moiré area. It can be determined that it is.

Meanwhile, according to another aspect of the present invention, there is a moiré detection method performed in a moire detection device for inspecting foreign matter in a camera module, comprising: acquiring an input image captured using the camera module in an image acquisition unit; performing a primary foreign matter inspection on the input image in a candidate group highlighting unit to highlight a moiré candidate group including an actual foreign material area and a moiré area; and selecting the highlighted moiré candidate group as a region of interest in a moiré determination unit; performing color sensitivity analysis on the region of interest; and determining a moiré area showing moiré characteristics through the color sensitivity analysis results. A moiré detection method for foreign matter inspection is provided.

Other aspects, features and advantages in addition to those described above will become apparent from the following drawings, claims and detailed description of the invention.

According to an embodiment of the present invention, by distinguishing between foreign matter and moire phenomenon and detecting and excluding the moiré phenomenon caused by the protective tape, normal detection of foreign matter in products such as cameras is possible, thereby ensuring the reliability of inspection. There is an effect.

1 is a configuration diagram of a moiré detection device for foreign matter inspection according to an embodiment of the present invention;
2 is a flowchart of a moiré detection method for foreign matter inspection according to an embodiment of the present invention;
Figure 3 is a diagram showing images (input image, average image, contrast image, output image) according to the first foreign matter detection process;
Figure 4 is a diagram showing image segmentation and histogram for the binarization process;
Figure 5 is a diagram for explaining the binarization process in the first histogram;
Figure 6 is an example of a dense section of binarized data for moiré detection;
Figure 7 is a screen showing the color sensitivity analysis results for moiré candidates;
8 shows the contour line removal screen,
9 is a moiré characteristic analysis screen;
Figure 10 is a diagram showing the process of specifying a moiré area.

Since the present invention can make various changes and have various embodiments, specific embodiments will be illustrated in the drawings and described in detail. However, this is not intended to limit the present invention to specific embodiments, and should be understood to include all changes, equivalents, and substitutes included in the spirit and technical scope of the present invention.

When a component is said to be "connected" or "connected" to another component, it is understood that it may be directly connected to or connected to the other component, but that other components may exist in between. It should be. On the other hand, when it is mentioned that a component is “directly connected” or “directly connected” to another component, it should be understood that there are no other components in between.

Terms such as first, second, etc. may be used to describe various components, but the components should not be limited by the terms. The above terms are used only for the purpose of distinguishing one component from another.

The terms used herein are only used to describe specific embodiments and are not intended to limit the invention. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, terms such as “comprise” or “have” are intended to indicate the presence of features, numbers, steps, operations, components, parts, or combinations thereof described in the specification, but are not intended to indicate the presence of one or more other features. It should be understood that this does not exclude in advance the possibility of the existence or addition of elements, numbers, steps, operations, components, parts, or combinations thereof.

In addition, the components of the embodiments described with reference to each drawing are not limited to the corresponding embodiments, and may be implemented to be included in other embodiments within the scope of maintaining the technical spirit of the present invention, and may also be included in separate embodiments. Even if the description is omitted, it is natural that a plurality of embodiments may be re-implemented as a single integrated embodiment.

In addition, when describing with reference to the accompanying drawings, identical or related reference numbers will be assigned to identical or related elements regardless of the drawing symbols, and overlapping descriptions thereof will be omitted. In describing the present invention, if it is determined that a detailed description of related known technologies may unnecessarily obscure the gist of the present invention, the detailed description will be omitted. In the drawings attached to this specification, colors are assigned to help distinguish components. However, even for the same component, its color may vary in perspective and cross-sectional views. And even different components may be given the same or similar colors.

In addition, terms such as “… unit,” “… unit,” “… module,” and “… unit” used in the specification refer to a unit that processes at least one function or operation, which refers to hardware or software or hardware and software. It can be implemented by combining .

1 is a configuration diagram of a moiré detection device for foreign matter inspection according to an embodiment of the present invention, FIG. 2 is a flowchart of a moiré detection method for foreign matter inspection according to an embodiment of the present invention, and FIG. 3 is a first It is a diagram showing images (input image, average image, contrast image, output image) according to the foreign matter detection process, Figure 4 is a diagram showing image segmentation and histogram for the binarization process, and Figure 5 is a diagram showing the image segmentation and histogram for the binarization process. This is a diagram for explaining the binarization process, Figure 6 is an example of a dense section of binarization data for moiré detection, Figure 7 is a color sensitivity analysis result screen for the moiré candidate group, Figure 8 is a contour line removal screen, and Figure 9 is a moiré characteristic analysis screen, and Figure 10 is a diagram showing the process of specifying a moiré area.

The moiré detection device 10 for foreign matter inspection according to an embodiment of the present invention is a product to which a protective tape for protecting components is attached during the manufacturing process (for example, a camera module for lens protection) due to the protective tape. It is characterized by detecting and excluding the resulting moiré phenomenon so that it can be distinguished from foreign matter, allowing the foreign matter inspection for the product to proceed normally.

The moiré detection device 10 for foreign matter inspection according to this embodiment is one of the camera module inspection equipment 1, and the foreign matter inspection module 20 is added at the rear end to be implemented as one of the configurations of equipment for inspecting foreign matter. .

Referring to FIG. 1 , the moiré detection device 10 for foreign matter inspection may include an image acquisition unit 110, a candidate group highlighting unit 120, and a moiré determination unit 130.

The image acquisition unit 110 acquires an input image captured using a product to be inspected (eg, a camera module).

In this embodiment, we aim to provide reliable evaluation standards for camera modules for foreign matter inspection. In order to provide reliable evaluation standards, the same environment is required, and an image of a white sheet or light source can be obtained as an input image using a camera module. By keeping the environmental conditions for foreign matter inspection constant, it is possible to set the condition that the environmental deviation of the input image is minimized.

Additionally, the image acquisition unit 110 may generate an average image by averaging a preset number of image frames included in the acquired input image. When shooting a specific point in time with a camera module, multiple image frames before and after can be acquired simultaneously, and the input image may consist of a predetermined number of image frames that are continuous or have a certain interval. The quantity of image frames for generating an average image can be variably set as needed.

The average image may be generated by averaging pixel values (eg, RGB values) at each coordinate for a plurality of image frames. That is, the average pixel value is obtained by averaging the pixel value p_1(x1,y1) of the (x1, y1) coordinate of the first image frame to the pixel value p_n(x1,y1) of the (x1, y1) coordinate of the nth image frame. It can be set to the pixel value (p_avg(x1,y1)) of the (x1, y1) coordinate of the average image.

When an image is acquired, the candidate highlighting unit 120 primarily performs a foreign matter inspection on the input image (or average image, hereinafter referred to as 'input image'). In the first foreign matter inspection, the moiré candidate group, which includes actual foreign matter and moiré, is emphasized through foreign matter inspection using an adaptive threshold, enabling easy distinction at the rear end. This takes advantage of the phenomenon that the moiré area appears more prominent when adjusting the contrast.

The candidate group highlighting unit 120 may include an image pre-processing unit 121, a contrast image generating unit 123, and an image processing unit 125.

The image pre-processing unit 121 performs lens shading compensation (LSC) to correct the lens shading phenomenon caused by the lens on the input image. Lens shading compensation can be performed by multiplying a pre-calculated LSC ratio corresponding to the lens mounted on the camera module.

Lens shading compensated images can be processed with auto white balance (AWB) to equalize the brightness values for each color channel (R, G, and B channels).

Afterwards, the luminance (Y) value (average of R, G, and B pixel values) for each pixel of the image is obtained through gray scale. The contrast image generator 123 generates a contrast image by performing a contrast stretching operation on the image that has been preprocessed as described above, that is, the image for which the luminance (Y) value has been obtained.

Contrast stretching changes the brightness value to the maximum range between the absolute minimum value (e.g., 0) and the absolute maximum value (e.g., 255) that the image can express, which is the valid range between the minimum and maximum brightness values of the input image. It is a tuning process that expands . For this purpose, the slope of the deviation between the maximum and minimum values can be used.

Assuming that the minimum value of the brightness values of all pixels belonging to the input image is Min and the maximum value is Max, the slope a and weight W for the amount of deviation between the maximum and minimum values are obtained as shown in Equation 1 and Equation 2 below.

The changed brightness value P of each pixel of the contrast image can be calculated as shown in Equations 3 and 4 below.

P is the tuned pixel brightness value. If the calculated P is greater than 255, it can be changed to 255, and if it is less than 0, it can be changed to 0.

The brightness value of each pixel is tuned by the contrast image generator 123, so that a contrast image with greater contrast than the input image can be generated.

The image processing unit 125 may apply a predetermined image processing algorithm to the contrast image so that the moiré candidate is more clearly highlighted.

The image processing unit 125 may include a binarization processing module.

The binarization processing module can divide the contrast image into a preset number of fragment images. Referring to FIG. 4, an example in which the contrast image 300 is divided into six fragment images (ROI_1 to ROI_6) is shown. Each piece image (ROI_n) may have the same size and shape.

The binarization processing module may analyze the characteristics of each segmented image (ROI_n), that is, the range form of the brightness values of pixels, and perform binarization processing by setting an individualized binarization standard for each segment image (ROI_n).

For example, the binarization processing module performs histogram conditional calculation on each segmented image (ROI-n).

For each fragment image (ROI_n), a histogram (H_1 to H_6) of the brightness value of the pixel can be obtained. In each histogram (H_n), the X-axis is the brightness value of the pixel, and the Y-axis represents the frequency of that brightness value.

The threshold for the corresponding fragment image is set based on the minimum value among the points where the histogram frequency rises and falls. This threshold becomes the standard for binarization of lower data in the contrast image.

In Figure 5, a histogram for the first fragment image (ROI_1) is shown.

In the histogram, when the value with the most frequency is M_1 and the most frequent value is M_2, the value with the lowest frequency among the points where the rise and fall changes between M_1 and M_2 can be set as Mid. It can be M_1 <= Mid && Mid <= M_2 or M_2 <= Mid && Mid <= M_1.

Mid is set as the threshold, and frequencies with lower values than Mid in the corresponding fragment image can be excluded (deleted). In Figure 5, pixel values (pixel values less than M_3 or greater than M_4) with a frequency less than the frequency of Mid (F_Mid) can be judged as noise and the data can be ignored.

And for the pixels on the left side of Mid in the histogram, the brightness value can be changed to 0 (zero).

Mid may be set differently depending on the brightness distribution of the fragment image (ROI_n), and more sophisticated binarization processing may be possible for each fragment image.

Additionally, the image processing unit 125 may further include a blur processing module.

The blur processing module can remove noise from the binarized contrast image through morphology calculation. For example, erosion of a 3 x 3 array can be applied as a morphology operation.

In a contrast image where binarization processing (or even blur processing) has been completed, only the portion corresponding to the moiré candidate can be highlighted (emphasized).

The candidate group highlighting unit 120 may detect the moiré candidate group through the image processing described above. An area where binarized data (e.g., '1') is dense can be viewed as an area where foreign matter or moiré exists, and this can be selected as a moiré candidate group.

Since the moiré candidate group selected by the candidate group highlighting unit 120 includes actual foreign matter in addition to the moiré area, it is necessary to distinguish them.

The moiré determination unit 130 performs predetermined image processing on the moiré candidate group highlighted in the candidate group highlighting unit 120 to determine moiré and distinguish it from an actual foreign matter, and excludes the foreign matter candidate determined as moiré from the moiré candidate group, so that the rear stage The foreign matter inspection module 20 can produce more accurate and highly reliable foreign matter inspection results.

The moire determination unit 130 may include a region of interest selection unit 131, a color sensitivity calculation unit 133, a filtering unit 135, and a determination unit 137.

The region of interest selection unit 131 selects a region of interest (ROI) related to the location of the moiré candidate group for the image in which the moiré candidate group is emphasized. The binarized data-dense section can be selected as the first moiré candidate section (see Figure 6).

For this purpose, the image can be divided into predetermined M x N regions. For example, M and N may be 15.

For each partition, a binarized data search block of a predetermined size (e.g., 10 pixels in width and height (10 * 10)) is set, and the presence or absence of binarized data is checked by moving the binarized data search block in the horizontal and/or vertical direction. Search.

During movement, when a situation occurs in which the entire binarized data search block (i.e., 100 pixels) consists of binarized data called '1', the corresponding segmented area can be selected as a region of interest (ROI).

Alternatively, based on the starting point of the binarized data, twice the size of the continuously expanded area in the binarized data search block can be selected as the region of interest.

In condition 1) of Equation 5, Rn refers to the block selected as the region of interest in the 10 * 10 block, and if the data value is 0 when inspecting the image in the 10 * 10 block, it is not included in R.

In condition 2, if there is data exceeding '0', that is, '1', for the surrounding blocks of the 10 * 10 block, it is included in the range of one R.

In Condition 3, for the R range, (X1, Y1) is used as the starting point and (X2, Y2) is used as the end point, and a size multiple of 2 is selected as the final region of interest.

Once the area of interest is selected, the color sensitivity calculation unit 133 calculates the color sensitivity within the area of interest.

In the case of general foreign substances, the change in sensitivity is monotonous, but in the case of moiré, it shows a unique change in sensitivity due to its wave-like characteristics. Therefore, in this embodiment, we calculate the sensitivity and analyze the sensitivity change pattern to distinguish whether the moiré candidate group is a general foreign matter or moiré range.

The color sensitivity calculation unit 133 divides the selected region of interest into a predetermined number of ROI division regions. For example, it can be divided into 32 x 32 ROI partition areas.

Then, the pixel value for each ROI division area is averaged to calculate the representative value of the ROI division area. The standard for calculation is color ratio data of R, G, B (e.g., 24 bit standard) values as R/G (R pixel value/G pixel value) and/or B/G (B pixel value/G pixel value). Calculate color sensitivity.

Color sensitivity can also be maximized by taking conservatives to give weight.

In Equation 6, M is the 32 x 32 division value of the region of interest, and the average value per block is stored. And, the result of averaging 3 * 3 can be squared by 2.

Referring to FIG. 7, a color sensitivity test is performed based on an RGB image, and the results expressed as result values for R/G and B/G values are shown. In addition, normal results as shown in FIG. 8 can be obtained by arbitrarily inserting a rise rate in preparation for the amount of image data rise due to the occurrence of image breaking due to contour lines generated as the angle of view of the lens increases, thereby removing the breaking phenomenon.

The filtering unit 135 applies a filter such as a rounding function (Round function) as shown in Equation 7 to utilize the moire characteristic.

To apply the filter, 32 x 32 data averaging is additionally performed, which has the effect of clearly dividing the value deviation by reducing the difference in values. If a difference in surrounding values occurs, the data can be binarized to explicitly divide the interval in which the difference value occurs.

When data averaging is performed, the data differences are generally evenly distributed in the normal region. However, in the moiré area, the difference points are uneven and do not appear evenly. Therefore, when calculating this difference, the difference can be obtained from the 4th decimal place. The difference portion can be binarized to distinguish the amount of change.

Equation 7 is a Round function, which rounds to the 4th decimal place. In cases where there is a difference in values up to the 4th decimal place, binarization is performed.

In terms of color sensitivity, moiré is a repeated increase or decrease in color ratio data, and the amount of binarization change (0 -> 1 and 1 -> 0) is repeatedly confirmed (see Figure 9 (a)). In contrast, in the case of non-moire, it shows a steadily rising or steadily decreasing trend as shown in (b) of FIG. 9.

For the output result, the determination unit 137 collects data in the horizontal or vertical direction, counts the amount of change in the binarized data within the ROI segment, and if it is more than a predetermined quantity (for example, 10), the corresponding ROI segment is divided. is counted, and if the total count amount of the ROI segment unit is more than a predetermined standard (for example, 13), it is determined that the region of interest is a moiré region.

According to Equation 8, if the data value in the horizontal or vertical direction is 10 or more, Count is increased by 1, and if the Count value is 13 or more during 32 blocks, it can be recognized as moiré.

Referring to FIG. 10, the specification of the moiré section is shown. (a) is the R/G array result of the moire test position, (b) is the binarization sequential change when the data size from left to right is different, and (c) is the result of rounding to 4 decimal places after averaging. This is the binarization result screen if the left -> right data is different for the results extracted in (b).

When a binarization filter is applied to an area where a difference in value compared to the surrounding area occurs, it is possible to display sections in block units, and the resultant value repeatedly rises and falls for the displayed section, making moiré judgment possible.

It is possible to specify only the section where the data deviation value occurred, and the progress line can be extracted, making it possible to avoid the moiré section by excluding the area containing the line.

According to the moiré detection method according to this embodiment, when an image taken by a camera module to be inspected is acquired (S200), a foreign matter inspection is first performed to highlight the moiré candidate group (S205). A region of interest is selected from the image in which the moiré candidate group is emphasized (S210), but the size of the region of interest is twice that of the moiré candidate group, and pixel data of 3 RGB channels is acquired for the image within the region of interest to determine color sensitivity (R/G, B/G) is obtained (S215), and the values are averaged for each block divided into a predetermined number (e.g. 32 x 32) ROI partitions. To maximize the value, a conservative is taken, secondary averaging is performed, and if a difference value occurs compared to the surroundings, the data is expressed by binarization (S220). Afterwards, moiré can be determined based on the number of binarized data in the horizontal or vertical direction (S225, S230).

The area of interest determined to be moiré is excluded from the inspection area when inspecting the camera module for foreign matter (S235), thereby preventing a situation where it is misclassified as a foreign material and preventing a normal camera module from being judged as defective due to a foreign material due to the protective tape. You can.

According to this embodiment, it is possible to create a specific standard that can resolve the ambiguity of foreign matter detection due to the occurrence of defective foreign matter overkill during the manufacturing process of a camera module due to the moiré phenomenon.

In addition, inspection can be performed with just a light source without any additional equipment, which has the effect of reducing costs.

In addition, it is possible to establish detection standards for foreign matter expansion and moiré expansion regardless of the increase in resolution of the camera module and the size of the lens angle of view.

The moiré detection method described above can also be implemented in the form of a recording medium containing instructions executable by a computer, such as an application or program module executed by a computer. Computer-readable media can be any available media that can be accessed by a computer and includes both volatile and non-volatile media, removable and non-removable media. Additionally, computer-readable media may include computer storage media. Computer storage media includes both volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, or other data.

The moiré detection method described above can be executed by an application installed by default on the terminal (this may include programs included in the platform or operating system, etc. installed by default on the terminal), and the user can use the application store server, application, or corresponding service. It may also be executed by an application (i.e. program) installed directly on the master terminal through an application providing server such as a web server related to the. In this sense, the moiré detection method described above can be implemented as an application (i.e., program) installed by default in the terminal or directly installed by the user and recorded on a computer-readable recording medium such as the terminal.

Although the present invention has been described above with reference to embodiments, those skilled in the art can make various modifications and changes to the present invention without departing from the spirit and scope of the present invention as set forth in the claims below. You will understand that you can do it.

1: Camera module inspection equipment 10: Moiré detection device
20: Foreign matter inspection module 110: Image acquisition unit
120: Candidate highlighting unit 121: Image preprocessing unit
123: Contrast image generation unit 125: Image processing unit
130: Moiré determination unit 131: Area of interest selection unit
133: Color sensitivity calculation unit 135: Filtering unit
137: Judgment panel

Claims (9)

A moire detection device for inspecting foreign substances in a camera module,
an image acquisition unit that acquires an input image captured using the camera module;
a candidate group highlighting unit for highlighting a moiré candidate group including an actual foreign material area and a moiré area through primary foreign matter inspection for the input image; and
A moiré determination unit that selects the highlighted moiré candidate group as a region of interest and determines a moiré region exhibiting moiré characteristics through color sensitivity analysis of the region of interest,
The candidate group emphasis section is,
an image pre-processing unit that performs pre-processing on the input image and converts it into a first image with a luminance value;
a contrast image generator that generates a contrast image by performing contrast stretching and blur processing on the first image;
A moiré detection device for foreign matter inspection, comprising an image processing unit that performs binarization processing on the contrast image according to an adaptive threshold setting to emphasize the moiré candidate group.
delete A moire detection device for inspecting foreign substances in a camera module,
an image acquisition unit that acquires an input image captured using the camera module;
a candidate group highlighting unit for highlighting a moiré candidate group including an actual foreign material area and a moiré area through primary foreign matter inspection for the input image; and
A moiré determination unit that selects the highlighted moiré candidate group as a region of interest and determines a moiré region exhibiting moiré characteristics through color sensitivity analysis of the region of interest,
The moiré determination unit,
a region of interest selection unit that selects a binarized data-dense section of the image in which the moiré candidate group is emphasized as a region of interest regarding the location of the moiré candidate group;
a color sensitivity calculation unit that analyzes color sensitivity of the image within the region of interest;
a filtering unit that applies a rounding function to the color sensitivity analysis result;
A moiré detection device for foreign matter inspection, comprising a determination unit that determines whether the moiré candidate group is a moiré area based on a change trend of the color sensitivity in one or more of the horizontal and vertical directions.
According to paragraph 3,
The region of interest selection unit sets a binarized data search block of a predetermined size, moves the binarized data search block in one or more of the horizontal and vertical directions, and searches for the presence or absence of binarized data, and binarized data within the binarized data search block. When included, a moiré detection device for foreign matter inspection, characterized in that twice the size of the area corresponding to the binarized data search block is selected as the region of interest based on the starting point of the binarized data.
According to paragraph 4,
The color sensitivity calculation unit divides the region of interest into a predetermined number of ROI divisions and calculates the color sensitivity by calculating an average value of R/G or B/G for each ROI division as color ratio data. Moiré detection device for foreign matter inspection.
According to clause 5,
A moiré detection device for foreign matter inspection, wherein the color sensitivity calculation unit takes a complement to the color sensitivity.
According to clause 5,
The filtering unit rounds the color sensitivity data to 4 decimal places through the rounding function, and performs binarization when a difference occurs.
In clause 7,
The determination unit counts the amount of change in binarized data within the ROI division area, and if more than a predetermined amount is counted, counts the corresponding ROI division area. If the total count amount of the ROI division unit is more than a predetermined standard, the region of interest is a moiré area. A moiré detection device for foreign matter inspection, characterized in that it determines that it is.
A moire detection method performed in a moire detection device for inspecting foreign substances in a camera module, comprising:
Acquiring an input image captured using the camera module in an image acquisition unit;
performing a primary foreign matter inspection on the input image in a candidate group highlighting unit to highlight a moiré candidate group including an actual foreign material area and a moiré area; and
selecting the highlighted moiré candidate group as a region of interest in a moiré determination unit;
performing color sensitivity analysis on the region of interest; and
Including the step of determining a moiré area showing moiré characteristics through the color sensitivity analysis results,
In the step of selecting the region of interest, the binarized data dense section of the image in which the moiré candidate group is emphasized is selected as the region of interest regarding the location of the moiré candidate group,
In the step of performing the color sensitivity analysis, the color sensitivity of the image within the region of interest is analyzed,
In the step of determining the moiré area, a rounding function is applied to the color sensitivity analysis result, and whether the moiré candidate group is a moiré area is determined from the change trend of the color sensitivity in one or more of the horizontal and vertical directions. Moiré detection method for foreign matter inspection, characterized in that determination.