Abstract The obvious wrong segmentation is pointed out in the existing two-dimensional histogram vertical segmentation method. A two-dimensional histogram oblique segmentation method is proposed. Then the formula and its fast recursive algorithm of the maximum Shannon entropy thresholding are deduced based on the two-dimensional histogram oblique segmentation. Finally, the threshold images and the processing time are given in the experimental results and analysis. The results are compared with those of the original maximum Shannon entropy algorithm and its fast algorithms based on the two-dimensional histogram vertical segmentation. The experimental results show that the proposed method makes the inner part uniform and the edge accurate in the threshold image, and it has a better anti-noise property. The processing time of the fast recursive algorithm of the proposed method is about 2% of that of the original two-dimensional maximum Shannon entropy algorithm, and it is less than one third of that of two fast recursive algorithms of the maximum Shannon entropy thresholding based on the two-dimensional histogram vertical segmentation.
[1] Wu Yiquan, Zhu Zhaoda. 30 Years(1962-1992) of the Developments in Threshold Selection Methods in Image Processing (1).Journal of Data Acquisition & Processing, 1993, 8(3): 193-201 (in Chinese) (吴一全,朱兆达.图像处理中阈值选取方法30年(1962-1992)的进展(一).数据采集与处理, 1993, 8(3): 193-201) [2] Wu Yiquan,Zhu Zhaoda. 30 Years(1962-1992) of the Developments in Threshold Selection Methods in Image Processing (2). Journal of Data Acquisition & Processing,1993,8(4):268-282 (in Chinese) (吴一全,朱兆达.图像处理中阈值选取方法30年(1962-1992)的进展(二).数据采集与处理, 1993, 8(4): 268-282) [3] Kapur J N, Sahoo P K, Wong A K C. A New Method for Grey-Level Picture Thresholding Using the Entropy of the Histogram. Computer Vision, Graphics and Image Processing, 1985, 29(3): 273-285 [4] Abutaleb A S. Automatic Thresholding of Gray-Level Picture Using Two-Dimensional Entropies. Pattern Recognition, 1989, 47(1): 22-32 [5] Brink A D. Thresholding of Digital Image Using Two-Dimensional Entropies. Pattern Recognition, 1992, 25(8): 803-808 [6] Liu Jianzhuang. A Fast Algorithm for Thresholding of Gray-Level Picture Using 2D Entropy. Pattern Recognition and Artificial Intelligence, 1991, 4(3): 46-53 (in Chinese) (刘健庄.基于二维熵的图像阈值选择快速算法.模式识别与人工智能, 1991, 4(3): 46-53) [7] Chen W T, Wen C H, Yang C W. A Fast Two-Dimensional Entropic Thresholding Algorithm. Pattern Recognition, 1994, 27(7): 885-893 [8] Gong Jian, Li Liyuan, Chen Weinan. A Fast Two-Dimensional Entropic Thresholding Method. Journal of Southeast University, 1996, 26(4): 31-36 (in Chinese) (龚 坚,李立源,陈维南.二维熵阈值分割的快速算法.东南大学学报, 1996, 26(4): 31-36) [9] Zhang Yijun, Wu Xuejing, Xia Liangzheng. A Fast Recurring Algorithm for Two-Dimensional Entropic Thresholding for Image Segmentation. Pattern Recognition and Artificial Intelligence, 1997, 10(3): 259-264 (in Chinese) (张毅军,吴雪菁,夏良正.二维图像阈值分割的快速递推算法.模式识别与人工智能, 1997, 10(3): 259-264) [10] Liu Jingnan, Chen Congyan, Yu Lingling, et al. A Fast Two-Dimensional Entropy Threshold Method. Application Research of Computers, 2002, 19(1): 67-68,70 (in Chinese) (刘京南,陈从颜,余玲玲,等.一种快速二维熵阈值分割算法.计算机应用研究, 2002, 19(1): 67-68,70)
2009, Vol. 22 
