A Region-Scalable Fitting Model Algorithm Combining Gray Level Difference of Sub-image for AMOLED Defect Detection

In this paper, we improve the Region-Scalable Fitting (RSF) model by the gray level difference of sub-image for RSF model's sensitivity to initial contour and slow speed in active matrix organic light emitting diode (AMOLED) defect detection. The gray level difference of sub-image algorithm can only locate the approximate defects area. The Region-Scalable Fitting (RSF) model overcomes the detection difficulty caused by the intensity inhomogeneity, but the local characteristic makes it extremely sensitive to the position of the initial contour curve. To solve this problem, we combine the gray level difference of sub-image algorithm with the RSF model. Firstly, the approximate position of the defects area is found by the gray level difference of sub-image algorithm, and the outline of this approximate defects area is taken as the initial contour curve of the RSF model. Then we implement the RSF model to segment the defects accurately. The experimental results show that the use of gray level difference of sub-image algorithm to locate the initial contour overcomes the disadvantage that the RSF model is sensitive to the initial contour, and improves the detection speed.

[1]  Tony F. Chan,et al.  A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model , 2002, International Journal of Computer Vision.

[2]  Xiaojun Wu,et al.  A defect detection method based on sub-image statistical feature for texture surface , 2016, International Conference on Digital Image Processing.

[3]  Ding Han.  Machine Vision Inspection Method of Mura Defect for TFT-LCD , 2010 .

[4]  郭太良 Guo Tai-liang,et al.  LCD Mura defectimage segmentation based on improved Chan-Vese model , 2016 .

[5]  Kazutaka Taniguchi,et al.  A mura detection method , 2006, Pattern Recognit..

[6]  李辉,et al.  Algorithm for fast TFT-LCD Mura defect image segmentation based on Chan-Vese model , 2014 .

[7]  Li Jun,et al.  A Fast Level Set Approach to Image Segmentation Based on Mumford-Shah Model , 2002 .

[8]  Chunming Li,et al.  Minimization of Region-Scalable Fitting Energy for Image Segmentation , 2008, IEEE Transactions on Image Processing.

[9]  Zhenxiang Zeng,et al.  Latest development of display technologies , 2016 .

[10]  卢小鹏 Lu Xiao-Peng,et al.  Algorithm for fast TFT-LCD Mura defect image segmentation based on Chan-Vese model , 2014 .

[11]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[12]  Jinxiao Pan,et al.  Active Contours Driven by Local and Global Image Fitting Energy , 2011 .

[13]  Pan Zhengyong.  Active contour segmentation model of combining global and dual-core local fitting energy , 2013 .

[14]  Anthony J. Yezzi,et al.  Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification , 2001, IEEE Trans. Image Process..