Aiming at the effect of pixel defects on the display of electrowetting electronic paper, an automatic threshold detection method based on Otsu is proposed to detect defects. Otsu is a commonly used automatic threshold method that gives satisfactory results when the image histogram is bimodal. However, the electrowetting defect image histogram is usually a single peak, and Otsu method fails. Electrowetting differs from the background contrast due to the filling inks of different colors, making segmentation more difficult. In this paper, the weighting coefficient is introduced before the target variance, and the weight decreases as the cumulative probability of defects increases. The weight keeps a large value before the threshold crosses the peak, and the weight decreases after the peak, ensuring that the threshold is always to the left of the peak in the case of a single peak. The experimental results show that the proposed method can effectively segment the electrowetting defect region, especially in the electrowetting defect image with lower contrast ratio. The method is closer to 0 compared to the ME value of Otsu, VE, WOV and entropy weighting methods. The proposed method has a better segmentation effect.
Electrowetting defect image segmentation based on improved Otsu method
First published at:Jun 15, 2020
 Hayes R A, Feenstra B J. Video-speed electronic paper based on electrowetting[J]. Nature, 2003, 425(6956): 383–385.
 Overton G. ELECTRONIC PAPER DISPLAYS: Kindles and cuttlefish: Biomimetics informs e-paper displays[J]. Laser Focus World, 2012, 48(12): 16.
 Hayes R A, Feenstra B J, Camps I G J, et al. 52.1: a high brightness colour 160 PPI reflective display technology based on electrowetting[J]. SID Symposium Digest of Technical Papers, 2004, 35(1): 1412–1415.
 Cheng W Y, Lo K L, Chang Y P, et al. 37.1: novel development of large-sized electrowetting display[J]. SID Symposium Digest of Technical Papers, 2008, 39(1): 526–529.
 Kuo S W, Chang Y P, Cheng W Y, et al. 34.3: novel development of multi-color electrowetting display[J]. SID Symposium Digest of Technical Papers, 2009, 40(1): 483–486.
 Schultz A, Heikenfeld J, Kang H S, et al. 1000:1 contrast ratio transmissive electrowetting displays[J]. Journal of Display Technology, 2011, 7(11): 583–585.
 Chang R L J, Liu P W, Wu C Y, et al. 54.2: reliable and high performance transparent electrowetting displays[J]. SID Symposium Digest of Technical Papers, 2014, 45(1): 785–788.
 Zhang X M, Bai P F, Hayes R A, et al. Novel driving methods for
manipulating oil motion in electrofluidic display pixels[J]. Journal of Display Technology, 2016, 12(2): 200–205.
 Zhao R, Tian Z Q, Liu Q C, et al. Electrowetting-based liquid prism[J]. Acta Optica Sinica, 2014, 34(12): 1223003.
赵瑞, 田志强, 刘启超, 等. 介电润湿液体光学棱镜[J]. 光学学报, 2014, 34(12): 1223003.
 Qian M Y, Lin S L, Zeng S Y, et al. Real-time dynamic driving system implementation of electrowetting display[J]. Opto-Electronic Engineering, 2019, 46(6): 180623.
钱明勇, 林珊玲, 曾素云, 等. 电润湿电子纸的实时动态显示驱动系统实现[J]. 光电工程, 2019, 46(6): 180623.
 Jin S Q, Ji C, Yan C C, et al. TFT-LCD mura defect detection using DCT and the dual-γ piecewise exponential transform[J]. Precision Engineering, 2018, 54: 371–378.
 He J J, Xiao K, Liu C, et al. TFT-LCD circuit defects detection based on faster R-CNN[J]. Computer and Modernization, 2018(7): 33–38.
何俊杰, 肖可, 刘畅, 等. 基于区域神经网络的TFT-LCD电路缺陷检测方法[J]. 计算机与现代化, 2018(7): 33–38.
 Otsu N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1): 62–66.
 Ng H F. Automatic thresholding for defect detection[J]. Pattern Recognition Letters, 2006, 27(14): 1644–1649.
 Fan J L, Lei B. A modified valley-emphasis method for automatic thresholding[J]. Pattern Recognition Letters, 2012, 33(6): 703–708.
 Zhang B, Ni K Z, Wang L J, et al. New algorithm of detecting optical surface imperfection based on background correction and image segmentation[J]. Acta Optica Sinica, 2016, 36(9): 0911004.
张博, 倪开灶, 王林军, 等. 基于背景校正和图像分割定量分析光学元件表面疵病的新算法[J]. 光学学报, 2016, 36(9): 0911004.
 Yuan X C, Lu W S, Peng Q J. An improved Otsu method using the weighted object variance for defect detection[J]. Applied Surface Science, 2015, 349: 472–484.
 Truong M T N, Kim S. Automatic image thresholding using Otsu’s method and entropy weighting scheme for surface defect detection[J]. Soft Computing, 2018, 22(13): 4197–4203.
 Liao P S, Chen T S, Chung P C. A fast algorithm for multilevel thresholding[J]. Journal of Information Science and Engineering, 2001, 17(5): 713–727.
 Yasnoff W A, Mui J K, Bacus J W. Error measures for scene segmentation[J]. Pattern Recognition, 1977, 9(4): 217–231.
National Key Research and Development Program of China (2016YFB0401503), Science and Technology Major Program of Fujian Province (2014HZ0003-1), Science and Technology Major Program of Guangdong Province (2016B090906001) and the Guangdong Provincial Key Laboratory of Optical Information Materials and Technology (2017B030301007)
Get Citation: Liao Qinkai, Lin Shanling, Lin Zhixian, et al. Electrowetting defect image segmentation based on improved Otsu method[J]. Opto-Electronic Engineering, 2020, 47(6): 190388.
Next: Broadband cross-slots fractal nano-antenna and its extraordinary optical transmission characteristics