Abstract:In view of the inaccurate segmentation and the high computational complexity of the traditional two-dimensional (2-D) Renyi entropy (RE) thresholding method, a fast and precise 2-D RE image thresholding method is presented. Firstly, the 2-D histogram is divided into inner, edge and noise areas by four oblique lines in parallel with the main diagonal line, and the noise points of the noise areas in the 2-D histogram are eliminated to obtain better segmentation performance. Then, the values of inner and edge areas in the 2-D RE formula are calculated precisely to get a more accurate threshold. Finally, a recursive algorithm of the precise 2-D RE image thresholding method is proposed, and an approach based on the recursive algorithm is inferred with the computational features and two formulas of 2-D RE to reduce the computational complexity. The experimental results show that the proposed method achieves more accurate segmentation results and more robust anti-noise capability compared with other contrast methods, and its running time is much less, almost the same as that of the current RE recursive algorithm based on 2-D histogram oblique segmentation.
[1] Nakib A,Oulhadj H,Siarry P.Non-Supervised Image Segmentation Based on Multiobjective Optimization.Pattern Recognition,2008,29(2): 161-172 [2] Zhang Xinming,Zheng Yanbin,Zhang Huiyun.Image Segmentation Based on Combining Chaos and Multiobjective Programming Theory.Journal of Chinese Computer Systems,2010,31(7): 1416-1420 (in Chinese) (张新明,郑延斌,张慧云.应用混沌多目标规划理论融合的图像分割.小型微型计算机系统,2010,31(7): 1416-1420) [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] Sahoo P,Wilkins C,Yeager J.Threshold Selection Using Renyis Entropy.Pattern Recognition,1997,30(1): 71-84 [5] Portes de Albuquerque M,Esquef I A,Gesualdi Mello A R.Image Thresholding Using Tsallis Entropy.Pattern Recognition Letters,2004,25(9): 1059-1065 [6] Abutaleb A S.Automatic Thresholding of Gray-Level Pictures Using Two-Dimensional Entropies.Pattern Recognition,1989,47(1): 22-32 [7] Brink A D.Thresholding of Digital Images Using Two-Dimensional Entropies.Pattern Recognition,1992,25(8): 803-808 [8] Sahoo P K,Arora G.A Thresholding Method Based on Two-Dimensional Renyis Entropy.Pattern Recognition,2004,37(6): 1149-1161 [9] Liu Suolan,Yang Jingyu.Segmentation Approach Based on Fuzzy Theory and Renyi Entropy of 2D Membership Partition.Journal of Image and Graphics,2009,14(2): 323-327 (in Chinese) (刘锁兰,杨静宇.基于模糊理论的2维隶属度划分Renyi熵分割算法.中国图象图形学报,2009,14(2): 323-327) [10] Gong J,Li L Y,Chen W N.Fast Recursive Algorithm for Two-Dimensional Thresholding.Pattern Recognition,1998,31(3): 295-300 [11] Wu Yiquan,Pan Zhe.Fast Recurring Two-Dimensional Tsallis-Havrda-Charvát Entropic Thresholding Algorithms.Signal Processing,2009,25(4): 665-668 (in Chinese) (吴一全,潘 喆.二维Tsallis-Havrda-Charvát熵阈值分割的快速递推算法.信号处理,2009,25(4): 665-668) [12] Wu Yiquan,Pan Zhe,Wu Wenyi.Maximum Entropy Image Thresholding Based on Two-Dimensional Histogram Oblique Segmentation.Pattern Recognition and Artificial Intelligence,2009,22(1): 162-168 (in Chinese) (吴一全,潘 喆,吴文怡.二维直方图区域斜分的最大熵阈值分割算法.模式识别与人工智能,2009,22(1): 162-168) [13] Wu Yiquan,Zhang Jinkuang.Image Thresholding Based on θ-division of 2-D Histogram and Maximum Shannon Entropy.Acta Physica Sinica,2010,59(8): 5487-5495 (in Chinese) (吴一全,张金矿.二维直方图θ-划分最大Shannon熵图像阈值分割.物理学报,2010,59(8): 5487-5495) [14] Zhang Xinming,Zheng Yanbin.Precise 2-D Tsallis Entropy Image Threshold Segmentation and Its Fast Recursive Realization.Chinese Journal of Scientific Instruments,2011,32(8): 1796-1802(in Chinese) (张新明,郑延斌.二维直方图准分的Tsallis熵阈值分割及快其速实现.仪器仪表学报,2011,32(8): 1796-1802) [15] Wu Chengmao,Tian Xiaopin,Tan Tieniu.Modification of Two-Dimensional Entropic Thresholding Method and Its Fast Iterative Algorithm.Pattern Recognition and Artificial Intelligence,2010,23(1): 127-136 (in Chinese) (吴成茂,田小平,谭铁牛.二维熵阈值法的修改及其快速迭代算法.模式识别与人工智能,2010,23(1): 127-136) [16] Zhang Xinming,Dang Liuqun,Zheng Yanbin,et al.Improved Image Segmentation Based on 2-D Minimum Cross Entropy.Opto-Electronic Engineering,2010,37(11): 103-109 (in Chinese) (张新明,党留群,郑延斌,等.一种改进的二维最小交叉熵图像分割方法.光电工程,2010,37(11): 103-109)