KR20050052064A - Method for detecting regular mura in a light-related plate element for a flat panel - Google Patents
Method for detecting regular mura in a light-related plate element for a flat panel Download PDFInfo
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Abstract
본 발명은 플랫패널용 광관련판요소로부터 영상을 획득하여 영상처리를 통해 불량을 검출함에 있어, 정형성 얼룩을 자동으로 검출할 수 있는 방법을 제공한다.The present invention provides a method capable of automatically detecting stereotypic smudges in acquiring an image from a light-related plate element for a flat panel and detecting defects through image processing.
그 정형성 얼룩 검출방법은, 입력된 영상 내부의 전체적으로 나타나는 비선형적 휘도변화에 따른 오검출을 보정하기 위해 선형성이 보장되는 크기의 다수의 매크로 블록으로 분할하고, 분할된 각 매크로 블록 내부의 화소값들이 가지는 x축, y축 방향의 선형적 휘도변화를 제거하는 이미지 평활화 단계; 및 평활화된 영상을 기설정된 상하의 임계값과 비교하여 그 범위밖인 때에 비정상 화소로 판별하고, 그 비정상화소가 적어도 2개이상 군집되어 있는 비정상 화소들은 얼룩으로 판별하는 얼룩판별단계를 포함하여 구성되는 것을 특징으로 한다.The method for detecting stereotyped spots is divided into a plurality of macroblocks having a guaranteed linearity in order to correct erroneous detections caused by nonlinear luminance changes appearing in the input image as a whole, and the pixel values within each divided macroblock. An image smoothing step of removing a linear luminance change in the x-axis and y-axis directions; And a spot discrimination step of comparing the smoothed image with a predetermined upper and lower threshold value to determine abnormal pixels when the pixel is out of the range, and identifying abnormal pixels in which at least two abnormal pixels are clustered as spots. It is characterized by.
Description
본 발명은, 플랫패널용 광관련판요소의 정형성 얼룩 검출방법에 관한 것으로, 더 상세하게는 플랫패널용 광관련판요소로부터 영상을 획득하여 영상처리를 통해 불량을 검출함에 있어, 정형성 얼룩을 자동으로 검출할 수 있는 방법에 관한 것이다.The present invention relates to a method for detecting irregularities of light related plate elements for flat panels, and more particularly, to detecting defects through image processing by acquiring images from light related plate elements for flat panels. The present invention relates to a method for automatically detecting.
현재, LCD, PDP 등의 플랫패널은 유리기판이나 광학문양판 등의 광관련판요소를 적층/포함하여 구성되며, 그 광관련판요소 각각 또는 적층된 상태에 따라 제조 과정중에서 여러 종류의 불량들이 나타난다. 즉, 화면의 색깔이 고르지 않거나 먼지부착, 크랙, 스크래치 등의 결함(흠)에 따라 점결함, 선결함, 블록형태 등의 특정형태의 정형성 얼룩, 테두리가 불분명한 부정형성 얼룩 등의 다양한 불량이 나타나게 된다.Currently, flat panels, such as LCDs and PDPs, are constructed by stacking / incorporating light-related plate elements such as glass substrates or optical pattern plates. appear. In other words, various defects such as irregularities such as irregularities such as irregular shapes with irregular shapes or irregular shapes such as point defects, predecessors, and block shapes due to uneven color or defects such as dust adhesion, cracks, and scratches, etc. Will appear.
이러한 광관련판요소의 불량을 판별하는 데에는 통상 육안에 의해 검사하여 판별하고 있어, 검사의 생산성과 정확성이 저하될 뿐만 아니라, 많은 비용이 소비되며, 불량 검출시 재현성이 부족하다. 따라서 객관적이면서도 비용절감 효과를 얻기 위해서는 자동 검사시스템의 도입이 반드시 필요하다.In order to discriminate the defects of the optically related plate element, inspection and discrimination are usually carried out by visual inspection, which not only lowers the productivity and accuracy of the inspection, but also consumes a lot of cost and lacks reproducibility in detecting the defects. Therefore, in order to obtain objective and cost-effective effects, it is necessary to introduce an automatic inspection system.
이와 같이 자동검사시스템은, 고도의 컴퓨터비전 기술과 고속 영상처리 알고리즘 이용하여 실시간으로 재현성 있게 처리함으로써 생산성의 향상뿐만 아니라 효율적인 품질관리가 가능하게 된다. 그러나, 고해상도로 영상을 획득하여 영상처리를 이용함에 있어서, 몇 가지 어려움이 따르는데, 첫째로 패널 자체의 휘도 레벨이 전면에 걸쳐 비선형적으로 나타나며, 패널에 신호가 인가된 후에도 활성화 시간에 따라 휘도 분포가 변하게 된다는 것이다. 둘째, 배경조명(back light)의 위치와 확산 시트 등의 내부 구조에 따라 모델별로 각각 다른 영상 특성을 가지게 된다. 이러한 어려움으로 인해서 일반적인 에지 연산자 등의 영상 분할 기법을 적용하기가 힘들다.In this way, the automatic inspection system is capable of reproducible processing in real time using advanced computer vision technology and high-speed image processing algorithms, thereby improving productivity as well as efficient quality control. However, there are some difficulties in using image processing by acquiring an image at a high resolution. First, the luminance level of the panel itself appears nonlinearly over the entire surface, and the luminance according to the activation time even after a signal is applied to the panel. The distribution will change. Second, each model has different image characteristics according to the position of the back light and the internal structure of the diffusion sheet. Due to these difficulties, it is difficult to apply image segmentation techniques such as general edge operators.
따라서, 본 발명은 이러한 문제를 해결하기 위한 것으로, 평판 디스플레이(FPD: Flat Panel Display)용 반제품 혹은 완제품의 자동검사를 위한 영상처리 알고리즘에 관한 것으로, 기존의 육안 검사에 의존하는 검사공정을 고도의 컴퓨터비전 기술과 고속 영상처리 알고리즘을 이용하여 실시간으로 재현성 있게 처리함으로써 생산성의 향상뿐만 아니라 효율적인 품질관리를 가능하게 하는 데에 그 주된 목적이 있다. Accordingly, the present invention is to solve this problem, and relates to an image processing algorithm for automatic inspection of semi-finished or finished products for flat panel displays (FPD), a high level of inspection process that relies on the existing visual inspection Its main purpose is to enable efficient quality control as well as productivity by reproducible processing in real time using computer vision technology and high-speed image processing algorithms.
이를 위해 본 발명은, 플랫패널용 광관련판요소로부터 영상을 획득하여 영상처리를 통해 불량을 검출함에 있어, 정형성 얼룩을 자동으로 검출할 수 있는 방법을 제공한다.To this end, the present invention provides a method for automatically detecting stereotyped irregularities in detecting defects through image processing by acquiring an image from a light related plate element for a flat panel.
이러한 목적을 달성하기 위해 본 발명의 일실시예에 따른 플랫패널용 광관련판요소의 정형성 얼룩 검출방법은, 입력된 영상 내부의 전체적인 휘도변화에 따른 오검출을 보정하기 위해 다수의 매크로 블록으로 분할하고, 분할된 각 매크로 블록 내부의 화소값들이 가지는 x축, y축 방향의 선형적 휘도변화를 제거하는 이미지 평활화 단계; 및 이미지가 평활화된 영상을 기설정된 상하의 임계값과 비교하여 그 범위밖인 때에 비정상 화소로 판별하고, 그 비정상화소가 적어도 2개이상 군집되어 있는 비정상 화소들은 얼룩으로 판별하는 얼룩판별단계를 포함하여 구성되는 것을 특징으로 한다.In order to achieve the above object, the method for detecting irregularities of a flat panel optically related plate element according to an embodiment of the present invention includes a plurality of macro blocks to correct misdetection according to the change in luminance of an entire image. An image smoothing step of dividing and removing linear luminance changes in x- and y-axis directions of pixel values in each of the divided macroblocks; And a spot discriminating step of comparing the image smoothed image with a predetermined upper and lower threshold value to determine an abnormal pixel when it is out of the range, and identifying abnormal pixels in which at least two abnormal pixels are clustered as spots. It is characterized in that the configuration.
검사가 가능한 플랫패널용 광관련판요소는, TFT-LCD 완제품 및 BLU, 확산판 등의 LCD 반제품, 유기 EL, PDP 등이며, 그 외에도 ITO Glass, 광학 Film 계열에도 적용이 가능하다. 플랫패널용 제품군에 관련된 영상은 획득이 어려울 뿐 아니라 획득 영상이 고해상도이며 영상 내부에서 비선형적인 휘도 변화를 가지므로 본 발명에서는 영상개선을 위한 전처리와 후처리 기술, 실시간 검사를 위한 고속 검사 알고리즘의 사용을 특징으로 한다.Flat panel light-related plate elements that can be inspected include TFT-LCD finished products, LCD semi-finished products such as BLU and diffuser plates, organic EL, and PDP, and can also be applied to ITO glass and optical film series. The image related to the flat panel product is not only difficult to acquire, but the acquired image is high resolution and has non-linear luminance change in the image. Therefore, in the present invention, the use of pre-processing and post-processing techniques for image improvement, and high-speed inspection algorithm for real-time inspection It is characterized by.
이하, 첨부된 도면을 참조하여 본 발명의 바람직한 실시예를 상세히 설명하면 다음과 같다.Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings.
도 1에는 본 발명의 일실시예에 따른 플랫패널용 광관련판요소의 정형성 얼룩 검출방법이 흐름도로 도시되며, 도 2에는 플랫패널용 광관련판요소의 결함검출방법을 적용하기 위해 획득된 영상을 도시한 화면도로서, 수직방향 패턴(11) 및 수평방향 패턴(12)이 화면(10)에 포함된다.1 is a flowchart illustrating a method for detecting irregularities of a flat panel light related plate element according to an embodiment of the present invention, and FIG. 2 is obtained to apply a defect detection method of a flat panel light related plate element. As a screen diagram showing an image, a vertical pattern 11 and a horizontal pattern 12 are included in the screen 10.
도 1에서 본 발명의 플랫패널용 광관련판요소의 정형성 얼룩 검출방법은, 기본적으로는 이미지 평활화 단계(단계S3)와 얼룩판별단계(단계S4)를 포함하여 구성되며, 바람직하게는 모델별 정규화(단계S2), 영역확장단계(단계S5), 오영역 제거단계(단계S6)를 더욱 포함하여 구성된다.In FIG. 1, the method for detecting irregularities of the flat panel optically related plate element of the present invention basically includes an image smoothing step (step S3) and a spot discriminating step (step S4). It further comprises a normalization (step S2), an area expansion step (step S5), and a wrong area removal step (step S6).
그 이미지 평활화(image flattening) 단계(단계S1)는, 입력된 영상 내부의 전체적인 휘도변화에 따른 오검출을 보정하기 위한 단계로, 이 단계에서는 입력 영상을 매크로 블록 단위로 분할하고 매크로 블록 내부의 화소값들이 가지는 x축, y축 방향의 선형적 휘도변화를 제거한다. 즉, 입력영상의 좌표 (i,j)의 화소값을 f(i,j)라고 할 때 평활화된 입력 영상의 화소값 f'(i,j)는, [K+{f(i,j)-M}]에 의해 산정된다. 여기서, M은 해당라인의 화소값들의 평균이며, K는 이미지 평활화한 후의 원하는 평균값이며, 2차원 연산을 단순하게 하기 위해 가로방향과 세로방향으로 분리되어 수행된다.The image flattening step (step S1) is a step for correcting erroneous detection according to the change of overall luminance inside the input image. In this step, the input image is divided into macroblock units and the pixels inside the macroblock. Eliminates linear luminance change in the x- and y-axis directions of the values. That is, when the pixel value of the coordinate (i, j) of the input image is f (i, j), the pixel value f '(i, j) of the smoothed input image is [K + {f (i, j)- M}]. Here, M is an average of pixel values of the corresponding line, K is a desired average value after image smoothing, and is performed separately in the horizontal and vertical directions to simplify the two-dimensional operation.
이와 같이, 이미지가 평활화된 영상은, 얼룩판별단계에서 기설정된 상하의 임계값과 비교하여 그 범위밖인 때에 비정상 화소로 판별되고, 그 비정상화소가 적어도 2개이상 군집되어 있는 비정상 화소들은 얼룩으로 판별된다. 이때, 그 비정상화소의 판별은, 블록단위로 수행되는 것이 바람직하다. 즉, 특정한 크기의 블록으로 영상을 분할하는데 이때, 블록의 크기는 N을 가변 가능한 양의 정수라고 할 때 평활화시에 사용한 매크로 블록의 1/N로 결정된다. 블록 내부의 통계값을 바탕으로 기설정된 검출강도에 따라 자동으로 블록별 임계값을 설정하여 블록별로 불량을 판별하는 것이다. 이와 같이 블록 내부의 통계값을 바탕으로 자동으로 임계값을 설정해 주는 어댑티브 멀티레벨 쓰레쏠딩(Adaptive multi-level thresholding) 알고리즘을 사용하는 것이 바람직하다. 그 어댑티브 멀티레벨 쓰레쏠딩 알고리즘은, 강한 결함의 영향으로 국부적인 창(local window) 내부의 약한 결함이 검출되지 않는 현상을 보완하기 위해 2중으로 수행하게 되는데, 블록 내부에서 히스토그램 분포에서 평균으로부터 통계적 수치를 고려하여 두 개의 임계값을 자동으로 설정한다. 즉, 임계값(VTH)은 식[me±kσ]에 의해 결정되며, 여기서 me는 예측된 블록의 평균 밝기값이고, σ는 현재 블록의 표준편차를 나타낸다. 그리고 k 값은 검출 강도를 조정하기 위한 사용자 파라메터이다.In this way, the image smoothed image is determined as an abnormal pixel when it is out of the range compared to the upper and lower threshold values preset in the spot discriminating step, and abnormal pixels in which at least two abnormal pixels are clustered are identified as spots. do. At this time, it is preferable that the abnormal pixel is determined in units of blocks. That is, the image is divided into blocks of a specific size, where the size of the block is determined as 1 / N of the macro block used for smoothing when N is a variable positive integer. Based on the statistical value inside the block, it automatically sets the threshold value for each block according to the preset detection intensity to determine the failure for each block. As such, it is desirable to use an adaptive multi-level thresholding algorithm that automatically sets a threshold based on the statistics within the block. The adaptive multilevel threading algorithm performs double to compensate for the fact that weak defects in the local window are not detected due to the influence of strong defects, which are statistically derived from the mean in the histogram distribution inside the block. Consider two thresholds automatically. That is, the threshold value V TH is determined by the formula [m e ± kσ], where m e is Is the average brightness value of the predicted block, and σ represents the standard deviation of the current block. And k is a user parameter for adjusting detection intensity.
한편, 상기 이미지 평활화 단계이전에 모델별로 입력영상이 일정한 평균값과 분산값을 가지기 위해서는 단계S1의 입력영상을 정규화하는 모델별 정규화단계(단계S2)가 필요하다. 그 모델별 정규화단계는, [md+(I-m)σd/σ]에 의해 정규화된 화소값(IN)을 구함으로써 이루어지며, 여기서, I, m, σ는 원영상의 화소값, 평균값 및 표준편차를 나타내고, md 와 σd는 원하는 평균값과 표준편차를 각각 나타낸다.On the other hand, before the image smoothing step, in order for the input image to have a constant average value and variance value for each model, a model normalization step (step S2) of normalizing the input image of step S1 is required. The normalization step for each model is performed by obtaining a pixel value I N normalized by [m d + (Im) σ d / σ], where I, m, and σ are pixel values and average values of the original image. And standard deviation, and m d and sigma d represent desired average values and standard deviations, respectively.
또한, 상기 얼룩판별단계이후에 얻어진 얼룩으로 얼룩의 전체영역이 모두 잡히도록 검출된 얼룩을 이용하여 얼룩의 영역을 확장하는 단계(단계S5)가 수행된다. 그 영역확장단계(단계S5)는, 도 2에 도시된 바와 같이 검출된 얼룩을 시드영역으로 추출하는 단계; 그 시드영역의 최소 외접 사각형(MBR)(4변의 직선방정식)을 구하는 단계; 그 최소 외접 사각형(MBR)을 각 변 방향별로 1라인씩 확장하는 단계; 그 확장부분의 화소값을 독취하는 단계; 독취한 화소값들을 시드영역과 대비하여 유사시에는 시드영역으로 편입시켜 그 시드영역을 확장시키는 단계; 그리고, 위의 단계를 반복하여 더 이상 시드영역이 확장되지 아니하는 때에 상기 최소 외접 사각형(MBR)의 정보를 확장된 시드영역의 최소 외접 사각형(MBR)으로 갱신하는 단계를 포함하여 구성된다.In addition, a step of expanding the area of the spot (step S5) using the spot detected so that the entire area of the spot is captured with the spot obtained after the spot discriminating step is performed. The area expanding step (step S5) may include extracting the detected spot into a seed area as shown in FIG. 2; Obtaining a minimum circumscribed square MBR (four-sided linear equation) of the seed region; Expanding the minimum circumscribed rectangle MBR by one line for each side direction; Reading pixel values of the extended portion; Expanding the seed region by incorporating the read pixel values into the seed region in case of a similarity with the seed region; And repeating the above steps, when the seed region is no longer extended, updating the information of the minimum circumference rectangle MBR to the minimum circumference rectangle MBR of the expanded seed region.
상기 영역확장단계이후에, 얼룩의 최소 외접 사각형(MBR)의 2배에 해당하는 사각형(배경)의 평균값과 최소 외접 사각형(MBR)의 평균값을 대비하여 유사한 경우에는 오영역으로 판단하여 얼룩으로부터 제거하는 오영역제거단계(단계S6)가 실시됨으로써 오영역이 제거된 실제 정형성 얼룩정보만이 추출되게 되고, 단계S7 및 단계S8에서 실제 정형성 얼룩정보들이 분석된 후, 그 결과들은 출력저장된다.After the area expansion step, if the average value of the rectangle (background) corresponding to twice the minimum circumference square (MBR) of the stain is compared with the average value of the minimum circumference square (MBR), it is judged as a wrong area and removed from the stain. By performing the error region removing step (step S6), only the actual shaping stain information from which the mistaken region is removed is extracted, and after the actual shaping stain information is analyzed in steps S7 and S8, the results are stored and output. .
이와 같이 하여 자동으로 정형성 얼룩들을 검출함으로써 보다 효율적이고도 정확하게 정형성 얼룩들이 검출될 수 있게 된다.In this way, by automatically detecting the shaped stains, the shaped stains can be detected more efficiently and accurately.
한편, 본 발명의 방법은, 본 출원인이 개발하여 2002.03.04일자출원번호 제2002-11358호로 출원한 감응식 플랫패널용 광관련판요소의 검사장치와 같이 구성되는 시스템들에 의해 수행될 수 있다.On the other hand, the method of the present invention can be carried out by systems that are configured as the inspection apparatus of the light-related plate element for the sensitive flat panel developed by the applicant and filed in application number 2002-11358 dated March 4, 2002. .
이상에서 설명한 본 발명의 실시예에 따른 플랫패널용 광관련판요소의 정형성 얼룩 검출방법의 구성과 작용에 의하면, 플랫패널용 광관련판요소로부터 영상을 획득하여 영상처리를 통해 불량을 검출함에 있어, 이미지 플랫트닝(image flattening), 옵티멀 스레숄딩(optimal thresholding) 등을 이용하여 플랫패널용 광관련판요소의 정형성 얼룩을 효과적이고도 자동으로 검출할 수 있는 등의 효과가 있다.According to the configuration and operation of the method for detecting irregularities of the flat panel light related plate element according to the embodiment of the present invention described above, an image is obtained from the flat panel light related plate element to detect defects through image processing. Thus, image flattening, optimal thresholding, and the like can effectively and automatically detect the irregularities of the flat panel light related plate elements.
도 1은 본 발명의 일실시예에 따른 플랫패널용 광관련판요소의 정형성 얼룩 검출방법을 나타내는 흐름도,1 is a flow chart showing a method for detecting irregularities of the light-related plate element for a flat panel according to an embodiment of the present invention;
도 2는 도 1의 정형성 얼룩검출법에 사용되는 영역확장법의 구체적 흐름도,FIG. 2 is a detailed flowchart of the area expansion method used in the stereotyping spot detection method of FIG. 1;
도 3은 정형성 얼룩을 포함한 획득영상을 나타내는 화면도.3 is a screen diagram showing an acquired image including stereotypic staining.
<도면의 주요 부분에 대한 부호 설명><Description of the symbols for the main parts of the drawings>
S1: 영상입력단계 S2: 모델별 정규화단계S1: Image input step S2: Normalization step by model
S3: 블록별 평활화단계 S4: 얼룩판별단계S3: smoothing step by block S4: staining step
S5: 영역확장단계 S6: 오영역 제거단계S5: Area Expansion Step S6: Error Area Removal Step
S7: 결함분석단계 S8: 결과출력저장단계S7: Defect analysis step S8: Result output saving step
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| US9159258B2 (en) | 2012-12-28 | 2015-10-13 | Samsung Display Co., Ltd. | Display device, and optical compensation system and optical compensation method thereof |
| CN116758011A (en) * | 2023-05-27 | 2023-09-15 | 福建蔚视科技有限公司 | Detection method and system for laser line sensing blanking port status based on area division |
| CN118735812A (en) * | 2024-08-30 | 2024-10-01 | 泉州职业技术大学 | Texture perception smoothing method and device based on local extreme difference |
| CN119006417A (en) * | 2024-08-12 | 2024-11-22 | 中国长江电力股份有限公司 | Equipment panel signal detection method based on image recognition algorithm |
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| KR0160655B1 (en) * | 1994-03-24 | 1999-05-01 | 김광호 | Method and apparatus for inspecting panel |
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| US9159258B2 (en) | 2012-12-28 | 2015-10-13 | Samsung Display Co., Ltd. | Display device, and optical compensation system and optical compensation method thereof |
| CN116758011A (en) * | 2023-05-27 | 2023-09-15 | 福建蔚视科技有限公司 | Detection method and system for laser line sensing blanking port status based on area division |
| CN119006417A (en) * | 2024-08-12 | 2024-11-22 | 中国长江电力股份有限公司 | Equipment panel signal detection method based on image recognition algorithm |
| CN118735812A (en) * | 2024-08-30 | 2024-10-01 | 泉州职业技术大学 | Texture perception smoothing method and device based on local extreme difference |
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