模式识别与人工智能
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模式识别与人工智能  2012, Vol. 25 Issue (3): 411-418    DOI:
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二维直方图准分的Renyi熵快速图像阈值分割
张新明,薛占熬,郑延斌
河南师范大学计算机与信息技术学院新乡453007
Fast and Precise Two-Dimensional Renyi Entropy Image Thresholding
ZHANG Xin-Ming, XUE Zhan-Ao, ZHENG Yan-Bin
College of Computer and Information Technology,Henan Normal University,Xinxiang 453007

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摘要 针对传统二维Renyi熵(RE)分割法分割结果不够准确和计算复杂度高的问题,提出一种快速的二维RE准分法。首先,用与主对角线平行的四条斜线将直方图分成内点区、边界点区和噪声点区,并对噪声点区进行去噪处理以便获得更好的分割性能。然后,对内点区与边界点区在RE公式中的对应量准确取值使阈值选取更准确。最后,提出二维RE准分法的一般递推算法,并在此算法的基础上利用RE在二维直方图上的计算特性和两个公式导出快速的二维RE阈值选取算法来降低计算复杂度。实验结果表明,与对比方法相比,文中方法不仅分割更准确和抗噪性更强,而且其运行时间少,与二维RE斜分法运行时间相近。
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张新明
薛占熬
郑延斌
关键词 图像分割阈值法二维Renyi熵递推算法准分法    
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.
Key wordsImage Segmentation    Thresholding Method    Two-Dimensional Renyi Entropy    Recursive Algorithm    Precise Segmentation Method   
收稿日期: 2011-05-26     
ZTFLH: TP391.41  
基金资助:国家自然科学基金项目(No.60873104)、河南省重点科技攻关项目(No.092102210017,102102210554)资助
作者简介: 张新明,男,1963年生,副教授,主要研究方向为数字图像处理、智能优化算法、模式识别等。E-mail:xinmingzhang@126。com。薛占熬,男,1963年生,博士,教授,主要研究方向为人工智能、非经典逻辑推理等。郑延斌,男,1964年生,博士,教授,主要研究方向为图形图像技术、虚拟现实、人工智能等。
引用本文:   
张新明,薛占熬,郑延斌. 二维直方图准分的Renyi熵快速图像阈值分割[J]. 模式识别与人工智能, 2012, 25(3): 411-418. ZHANG Xin-Ming, XUE Zhan-Ao, ZHENG Yan-Bin. Fast and Precise Two-Dimensional Renyi Entropy Image Thresholding. , 2012, 25(3): 411-418.
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