CN115225885A - Electronic device and method for inspecting defect of display area of display - Google Patents
Electronic device and method for inspecting defect of display area of display Download PDFInfo
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- CN115225885A CN115225885A CN202110410296.4A CN202110410296A CN115225885A CN 115225885 A CN115225885 A CN 115225885A CN 202110410296 A CN202110410296 A CN 202110410296A CN 115225885 A CN115225885 A CN 115225885A
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Abstract
The invention relates to an electronic device and a method for inspecting defects of a display area of a display. The method comprises the following steps: acquiring an image corresponding to a display, wherein the image comprises a display area and a non-display area, and the edge of the display area is adjacent to the non-display area; capturing an edge image corresponding to the edge of a display area of the display according to the image, wherein the edge image comprises a part of the display area and a part of a non-display area; obtaining a pixel array in the edge image and generating a regression model of the pixel array; judging whether pixels in the pixel array are abnormal or not according to the pixel array and the regression model to generate a judgment result; and outputting the judgment result. The invention can be used for detecting flaws on the edge of the display area of the display without being influenced by high-brightness contrast at the boundary of the display area and the non-display area.
Description
Technical Field
The invention relates to an electronic device and a method for detecting defects of a display area of a display.
Background
After the assembly of the lcd panel is completed, the lcd panel needs to be lighted for image detection. The LCD is, for example, a thin film transistor liquid crystal display (TFT-LCD), and the image detection can be used to detect whether the image of the LCD contains defects such as foreign objects, bright spots or dark spots. In a display region of a display, a region defect in which brightness or color is low in contrast is called Mura (roman spelling of japanese kanji "spot" [ 12416\12425. In the past, the display inspection method was performed by a person in charge of inspection by visually observing the display. However, defects in the display, especially low-contrast defects such as Mura, which are observed for a long time, easily cause eye fatigue of the quality control staff, and the standard is easily inconsistent when the quality control staff performs detection. Therefore, a detection method of performing Automatic Optical Inspection (AOI) by an optical instrument is becoming a mainstream in the industry.
Most of the existing AOI detection methods are based on background estimation (background estimation). However, since a portion of the display area and a portion of the non-display area included in the edge area of the display show a significant luminance difference, the background estimation method is difficult to be applied to detect the edge area of the display. For example, the low contrast defect in the display area is very insignificant compared to the brightness difference at the boundary between the display area and the non-display area, so the low contrast defect is easily ignored. If the position of the display area of the display can be accurately located, the optical instrument can eliminate the interference caused by the brightness difference. However, it is difficult to completely fix the position of the display or the camera.
The background section is only provided to assist in understanding the present disclosure, and therefore, the disclosure in the background section may include some prior art that does not constitute a part of the common general knowledge of a person skilled in the art. The statements in the "background" section do not represent that matter or the problems which may be solved by one or more embodiments of the present invention, but are known or appreciated by those skilled in the art before filing the present application.
Disclosure of Invention
The invention provides an electronic device and a method for detecting defects of a display area of a display, which can be applied to an edge area of the display.
The invention provides an electronic device for detecting defects of a display area of a display, which comprises a transceiver, a storage medium and a processor. The storage medium stores a plurality of modules. The processor is coupled to the storage medium and the transceiver, and accesses and executes a plurality of modules, wherein the plurality of modules include a data collection module and an operation module. The data collection module obtains an image corresponding to the display through the transceiver, wherein the image comprises a display area and a non-display area, and the edge of the display area is adjacent to the non-display area. The operation module is configured to perform: capturing an edge image corresponding to the edge of a display area of the display according to the image, wherein the edge image comprises a part of the display area and a part of a non-display area; obtaining a pixel array in the edge image and generating a regression model of the pixel array; judging whether pixels in the pixel array are abnormal or not according to the pixel array and the regression model to generate a judgment result; and outputting the judgment result through the transceiver.
The invention provides a method for inspecting defects of a display area of a display, which comprises the following steps: acquiring an image corresponding to a display, wherein the image comprises a display area and a non-display area, and the edge of the display area is adjacent to the non-display area; capturing an edge image corresponding to the edge of a display area of the display according to the image, wherein the edge image comprises a part of the display area and a part of a non-display area; obtaining a pixel array in the edge image and generating a regression model of the pixel array; judging whether the pixels in the pixel array are abnormal or not according to the pixel array and the regression model to generate a judgment result; and outputting the judgment result.
Based on the above, the present invention can be used to detect the defect of the edge of the display area of the display without being affected by the high brightness contrast at the boundary of the display area and the non-display area.
In order to make the aforementioned and other features and advantages of the invention more comprehensible, embodiments accompanied with figures are described in detail below.
Drawings
Fig. 1 is a schematic diagram illustrating an electronic device according to an embodiment of the invention.
FIG. 2 is a schematic diagram illustrating a plurality of images of a display according to an embodiment of the invention.
FIG. 3 is a schematic diagram illustrating capturing an edge image of a display area of a display according to an embodiment of the invention.
FIG. 4 is a flow chart illustrating a method for inspecting a display area of a display for defects according to an embodiment of the present invention.
Reference numerals are as follows:
100: electronic device
110: processor with a memory having a plurality of memory cells
120: storage medium
121: data collection module
122: operation module
130: transceiver
200: image forming method
30: display device
300: processed image
310: non-display area
320: display area
321. 322, 323, 324: edge image
400: masking image
50: flaw or flaw
S401, S402, S403, S404, S405: step (ii) of
Detailed Description
In order that the contents of the invention may be more readily understood, the following specific examples are given as illustrative of the manner in which the invention may be practiced. Further, wherever possible, the same reference numbers will be used throughout the drawings and the description to refer to the same or like parts.
Fig. 1 is a schematic diagram illustrating an electronic device 100 according to an embodiment of the invention. The electronic device 100 may be used to inspect the display area of the display for defects (e.g., mura defects). The electronic device 100 may include a processor 110, a storage medium 120, and a transceiver 130.
The processor 110 is, for example, a Central Processing Unit (CPU), or other programmable general or special Micro Control Unit (MCU), a microprocessor (microprocessor), a Digital Signal Processor (DSP), a programmable controller, an Application Specific Integrated Circuit (ASIC), a Graphics Processor (GPU), a video signal processor (ISP), an Image Processing Unit (IPU), an Arithmetic Logic Unit (ALU), a Complex Programmable Logic Device (CPLD), a field programmable logic gate array (FPGA), or other similar components or combinations thereof. The processor 110 may be coupled to the storage medium 120 and the transceiver 130, and access and execute a plurality of modules and various applications stored in the storage medium 120.
The storage medium 120 is, for example, any type of fixed or removable Random Access Memory (RAM), read-only memory (ROM), flash memory (flash memory), hard disk (HDD), solid State Drive (SSD), or the like, or any combination thereof, and is used for storing a plurality of modules or various applications executable by the processor 110. In the embodiment, the storage medium 120 may store a plurality of modules including a data collection module 121 and an operation module 122, the functions of which will be described later.
The transceiver 130 transmits and receives signals in a wireless or wired manner. The transceiver 130 may also perform operations such as low noise amplification, impedance matching, frequency mixing, up or down frequency conversion, filtering, amplification, and the like.
FIG. 2 is a schematic diagram illustrating a plurality of images of a display according to an embodiment of the invention. The data collection module 121 may obtain the image 200 corresponding to the display through the transceiver 130. The image 200 may correspond to a closed display or a display displaying a black screen.
The image 200 may be a grayscale image. If the image 200 is not a grayscale image, the operation module 122 can convert the image 200 into a grayscale image. Although the image 200 in fig. 2 only includes a partial image of the upper left corner of the display, the invention is not limited thereto. For example, the image 200 may include a complete display.
The image 200 may include a display area and a non-display area of a display (e.g., the display area 320 and the non-display area 310 of the display 30 shown in FIG. 3). The edge of the display area 320 may be adjacent to the non-display area 310. In other words, in the image 200, the boundary between the display area and the non-display area of the display may be a high brightness contrast image or a high color contrast image. The existing automatic optical detection technology is easily affected by the image with high brightness contrast or high color contrast to generate wrong judgment results. For example, the image 200 includes the defect 50. However, the defect 50 is very inconspicuous compared to the high contrast image at the interface of the display area and the non-display area.
To check the defect of the edge of the display area of the display, the operation module 122 can obtain an edge image of the edge of the display area 320 of the display 30. FIG. 3 is a schematic diagram illustrating capturing an edge image of a display area of a display according to an embodiment of the invention.
In order to make the defects of the display area of the display more obvious and filter out the lattice texture (lattice) of the display, after the data collection module 121 obtains the image 200 corresponding to the display, the operation module 122 may first perform image pre-processing on the image 200 to generate a processed image 300, as shown in fig. 2. The image pre-processing may include gamma correction or low pass filtering, but the invention is not limited thereto.
The operation module 122 can capture an edge image corresponding to an edge of the display area 320 of the display 30 from the processed image 300. In the embodiment, the operation module 122 can obtain the edge image 321 corresponding to the left edge of the display area 320 of the display 30, the edge image 322 corresponding to the right edge of the display area 320 of the display 30, the edge image 323 corresponding to the upper edge of the display area 320 of the display 30, and the edge image 324 corresponding to the lower edge of the display area 320 of the display 30 from the processed image 300. The edge images 321, 322, 323, and 324 include an edge portion of the display area 310 and a non-display area of the display 30, respectively. That is, the computing module 122 can capture two edge images (i.e. the edge image 321 and the edge image 322) from two short sides corresponding to the display area 320, and capture two edge images (i.e. the edge image 323 and the edge image 324) from two long sides corresponding to the display area 320.
The edge image 321 may include a portion of the display area 320 and a portion of the non-display area 310. After obtaining the edge image 321, the operation module 122 obtains the pixel array from the edge image 321. In one embodiment, the edge image 321 may be a rectangle, and the extending direction of the pixel array is parallel to the long side of the rectangle. For example, the long side of the edge image 321 (a short side of the display area 320) is parallel to the y-axis. Therefore, the operation module 122 can obtain an image of the pixel array parallel to the y-axis from the edge image 321.
After obtaining the pixel array parallel to the y-axis, the operation module 122 may generate a regression model of the pixel array. The regression model is, for example, a polynomial regression (multinomial regression) model I', as shown in formula (1), where d is a polynomial order and a m Is a polynomial parameter of order m. The arithmetic module 122 can determine the respective polynomial parameter a, for example, according to the Least Squares (LS) m 。
I′(y)＝∑ 0≤m≤d a m y m …(1)
After obtaining the regression model, the operation module 122 may determine whether the pixels in the pixel array are abnormal according to the pixel array and the regression model to generate a determination result, and may output the determination result through the transceiver 130 for the product management staff to refer to. Specifically, the operation module can calculate a norm r (y) of a difference value between the pixel array and the regression model according to formula (2), where I (y) is a value of a pixel in the pixel array.
r(y)＝||I(y)-I′(y)||…(2)
After obtaining the norm r (y) of the difference between the pixel array and the regression model, the operation module 122 can determine whether the pixel corresponding to the norm r (y) is an abnormal pixel according to the norm r (y). Specifically, the operation module 122 may determine that the pixel corresponding to the norm r (y) is abnormal in response to the norm r (y) being greater than the threshold. For example, the operation module 122 may determine that the pixel with the y coordinate value equal to y1 in the pixel array is an abnormal pixel in response to the norm r (y 1) being greater than the threshold. The operation module 122 can generate a determination result according to the determination result.
In one embodiment, the calculation module 122 may dynamically adjust the threshold. For example, the operation module 122 may dynamically adjust the threshold, e.g., based on a Niblack algorithm.
In addition to outputting the determination result, the operation module 122 may further generate a mask image corresponding to the edge image according to the determination result, wherein the mask image may include a mark corresponding to the abnormal pixel. The operation module 122 can output the mask image through the transceiver 130 for the personnel of the product management to refer. Specifically, the operation module 122 generates a mask image of the edge image according to formula (3), where m (y) is a value of a pixel in the mask image corresponding to the pixel array, and th is a threshold.
For example, m (y 1) equal to 1 represents that the pixel of the mask image with y1 coordinate value can be marked as black, and m (y 1) equal to 0 represents that the pixel of the mask image with y1 coordinate value can be marked as white. Such as mask image 400 shown in fig. 2. The operation module 122 can generate a mask image 400 corresponding to the processed image 300 according to formula (3). In the mask image 400, the operation module 122 may mark the pixels corresponding to the defect 50 as black (abnormal pixels) and mark the remaining pixels as white. Thus, the person in charge of quality can easily identify the location of the defect 50 on the display from the mask image 400.
According to the above method, the calculating module 122 can generate a mask image corresponding to the edge image 321, a mask image corresponding to the edge image 322, a mask image corresponding to the edge image 323, and a mask image corresponding to the edge image 324, respectively. The operation module 122 can fuse the four mask images into a defect mask image, and output the defect mask image through the transceiver 130 for the product management staff to refer.
FIG. 4 is a flowchart illustrating a method for inspecting a display area of a display for defects according to an embodiment of the invention, wherein the method can be implemented by the electronic device 100 shown in FIG. 1. In step S401, an image corresponding to a display is obtained, wherein the image includes a display area and a non-display area of the display, and an edge of the display area is adjacent to the non-display area. In step S402, an edge image corresponding to an edge of a display area of the display is captured according to the image, wherein the edge image includes a portion of the display area and a portion of the non-display area. In step S403, a pixel array in the edge image is obtained, and a regression model of the pixel array is generated. In step S404, it is determined whether the pixels in the pixel array are abnormal according to the pixel array and the regression model to generate a determination result. In step S405, a determination result is output.
In summary, the present invention can be used to detect the edge area of the display without being affected by the high brightness contrast at the boundary between the display area and the non-display area. A regression model generated from the pixel array may be used to examine each pixel in the pixel array to determine if there is an abnormal pixel. The invention can also carry out image preprocessing or graying on the image with the display so as to make the defects more obvious. After the abnormal pixels are judged, the invention can generate the mask image related to the judgment result. The quality control personnel can easily judge the specific position of the flaw on the display according to the mask image.
While the invention has been described with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Furthermore, not all objects or advantages or features disclosed herein are necessarily achieved in accordance with any one embodiment or claim set forth herein. In addition, the abstract and the title are provided to assist the patent document searching and are not intended to limit the scope of the invention. Furthermore, the terms "first", "second", and the like in the description or the claims are used only for naming elements (elements) or distinguishing different embodiments or ranges, and are not used for limiting the upper limit or the lower limit on the number of elements.
Claims (20)
1. An electronic device for inspecting a display area of a display for defects, the electronic device comprising: a transceiver, a storage medium, and a processor; wherein,
the storage medium stores a plurality of modules; and
the processor couples the storage medium and the transceiver, and accesses and executes the plurality of modules, wherein the plurality of modules comprises:
a data collection module that obtains an image corresponding to the display through the transceiver, wherein the image includes the display area and a non-display area, wherein an edge of the display area is adjacent to the non-display area; and
an operation module configured to perform:
capturing an edge image corresponding to the edge of the display area of the display according to the image, wherein the edge image comprises a part of the display area and a part of the non-display area;
obtaining a pixel array in the edge image and generating a regression model of the pixel array;
judging whether pixels in the pixel array are abnormal or not according to the pixel array and the regression model to generate a judgment result; and
and outputting the judgment result through the transceiver.
2. The electronic device of claim 1, wherein the arithmetic module calculates a norm of a difference between the pixel array and the regression model, and determines the pixel corresponding to the norm to be abnormal in response to the norm being greater than a threshold.
3. The electronic device of claim 1, wherein the edge image is a rectangle, and wherein the extending direction of the pixel array is parallel to a long side of the rectangle.
4. The electronic device of claim 1, wherein the image is a grayscale image.
5. The electronic device of claim 1, wherein the computing module performs image preprocessing on the image to generate a processed image, and captures the edge image from the processed image, wherein the computing module performs the image preprocessing according to at least one of:
gamma correction and low pass filtering.
6. The electronic device of claim 1, wherein the regression model is a polynomial regression model.
7. The electronic device of claim 1, wherein the calculation module generates the regression model according to a least squares method.
8. The electronic device of claim 1, wherein the computing module generates a mask image corresponding to the edge image according to the determination result, wherein the mask image includes a mark corresponding to the pixel.
9. The electronic device of claim 8, wherein the display area is rectangular, wherein the computing module captures two of the edge images from two long sides corresponding to the display area, and captures two of the edge images from two short sides corresponding to the display area, respectively, wherein the computing module generates the mask image according to each of the edge images, and fuses the four mask images into a defect mask image.
10. The electronic device of claim 1, wherein the image is mapped to one of: the closed display and the display displaying a black screen.
11. A method of inspecting a display area of a display for defects, comprising:
obtaining an image corresponding to the display, wherein the image comprises the display area and a non-display area, wherein the edge of the display area is adjacent to the non-display area;
capturing an edge image corresponding to the edge of the display area of the display according to the image, wherein the edge image comprises a part of the display area and a part of the non-display area;
obtaining a pixel array in the edge image and generating a regression model of the pixel array;
judging whether the pixels in the pixel array are abnormal or not according to the pixel array and the regression model to generate a judgment result; and
and outputting the judgment result.
12. The method of claim 11, wherein determining whether the pixel in the pixel array is abnormal according to the pixel array and the regression model to generate the determination result comprises:
a norm of a difference of the pixel array and the regression model is calculated, and the pixel corresponding to the norm is judged to be abnormal in response to the norm being greater than a threshold.
13. The method of claim 11, wherein the edge image is a rectangle, and wherein the pixel array extends parallel to a long side of the rectangle.
14. The method of claim 11, wherein the image is a grayscale image.
15. The method of claim 11, wherein the step of capturing the edge image corresponding to the edge according to the image comprises:
performing image preprocessing on the image to generate a processed image, and capturing the edge image from the processed image, wherein the image preprocessing on the image to generate the processed image comprises:
and performing the image preprocessing according to at least one of gamma correction and low-pass filtering.
16. The method of claim 11, wherein the regression model is a polynomial regression model.
17. The method of claim 11, further comprising:
the regression model is generated according to the least squares method.
18. The method of claim 11, further comprising:
and generating a mask image corresponding to the edge image according to the judgment result, wherein the mask image comprises a mark corresponding to the pixel.
19. The method of claim 18, wherein the display area is rectangular, wherein the method further comprises: respectively capturing two edge images from two long edges corresponding to the display area; respectively capturing two edge images from two short edges corresponding to the display area; generating the mask image according to each edge image; and fusing the four mask images into a defect mask image.
20. The method of claim 11, wherein the image is mapped to one of: the closed display and the display displaying a black screen.
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| TW200842339A (en) * | 2007-04-19 | 2008-11-01 | Au Optronics Corp | Mura detection method and system |
| CN106200047A (en) * | 2016-08-29 | 2016-12-07 | 武汉精测电子技术股份有限公司 | A kind of method of TFT LCD Mura defects detection based on GPU |
| CN108877631A (en) * | 2018-07-25 | 2018-11-23 | 昆山国显光电有限公司 | Mura compensation method, device, computer equipment and the storage medium of display screen |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| TW200842339A (en) * | 2007-04-19 | 2008-11-01 | Au Optronics Corp | Mura detection method and system |
| CN106200047A (en) * | 2016-08-29 | 2016-12-07 | 武汉精测电子技术股份有限公司 | A kind of method of TFT LCD Mura defects detection based on GPU |
| US20190197678A1 (en) * | 2016-08-29 | 2019-06-27 | Wuhan Jingce Electronic Group Co., Ltd. | Gpu-based tft-lcd mura defect detection method |
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