一种超模糊熵ULPCNN图像自动分割新方法

doi:10.3969/j.issn.1001-2400.2010.05.008

J4 ›› 2010, Vol. 37 ›› Issue (5): 817-824.doi: 10.3969/j.issn.1001-2400.2010.05.008

一种超模糊熵ULPCNN图像自动分割新方法

刘勍^1,2;许录平¹;马义德³;苏哲¹;王勇¹

(1. 西安电子科技大学电子工程学院，陕西西安 710071；
2. 天水师范学院物理与信息科学学院，甘肃天水 741001；
3. 兰州大学信息科学与工程学院，甘肃兰州 730000)

收稿日期:2009-06-29 出版日期:2010-10-20 发布日期:2010-10-11
通讯作者: 刘勍
作者简介:刘勍(1970-)，男，副教授，西安电子科技大学博士研究生，E-mail: lqlzu@126.com．
基金资助:
国家高技术研究发展计划863基金资助项目(2007AA12Z323);国家自然科学基金资助项目(60772139;60872109);高等学校博士学科点专项科研基金资助项目(200807011007);甘肃省自然科学基金计划资助项目(1010RJZE028);天水师范学院“青蓝”人才工程基金资助项目

Automated image segmentation using the ULPCNN model with ultra-fuzzy entropy

LIU Qing^1,2;XU Lu-ping¹;MA Yi-de³;SU Zhe¹;WANG Yong¹

(1. School of Electronic Engineering, Xidian Univ., Xi'an 710071， China;
2. School of Physics ＆ Information Science, Tianshui Normal Univ., Tianshui 741001， China;
3. School of Information Sci. ＆ Eng., Lanzhou Univ., Lanzhou 730000， China)

Received:2009-06-29 Online:2010-10-20 Published:2010-10-11
Contact: LIU Qing

摘要/Abstract

摘要：

为了自动地对图像进行二值分割，提出了一种新的自适应迭代全局阈值图像分割算法．首先对二维超模糊集隶属函数进行了自适应修正，并将其引入到图像超模糊熵概念中; 然后从适应图像分割角度，将传统脉冲耦合神经网络模型改进为具有单调指数上升阈值函数的ULPCNN抑制捕获模型; 最后把ULPCNN与最大超模糊熵判据相结合对图像进行自动分割，并与基于最大香农熵、最小交叉熵及最小模糊熵准则的ULPCNN分割方法作了比较．理论分析和实验结果表明，该方法能自动确定迭代次数和选取最佳阈值，对图像目标划分清晰，细节保持较好，改善了图像的分割性能．

关键词: 图像分割, 最大超模糊熵, ULPCNN, 阈值函数, 抑制捕获

Abstract:

In order to process the binary segmentation of an image automatically, a new adaptive iterative image segmentation algorithm with the property of global threshold is proposed. Firstly, the two-dimensional ultra-fuzzy sets membership function, which is adaptively modified, is introduced into the concept of image ultra-fuzzy entropy. Secondly, the traditional pulse coupled neural networks (PCNN) model is improved to obtain the restraining capture Unit-Linking PCNN model with the monotony exponential raised threshold function from the point of view of image segmentation. Finally, the improved ULPCNN is combined with the criterion of maximum ultra-fuzzy entropy to process image segmentation automatically. A comparison is made between this algorithm and ULPCNN segmentation methods based on the criteria of maximum Shannon entropy, minimum cross entropy and minimum fuzzy entropy. Theoretical analysis and experimental simulations show that the proposed algorithm automatically determines the number of iterative times, chooses the best threshold, separates the objects in the image clearly, preserves most of the details, and enhances the performance of image segmentation.

Key words: image segmentation, maximum ultra-fuzzy entropy, ULPCNN, threshold functions, restrain and capture

刘勍;许录平;马义德;苏哲;王勇. 一种超模糊熵ULPCNN图像自动分割新方法[J]. J4, 2010, 37(5): 817-824.

LIU Qing;XU Lu-ping;MA Yi-de;SU Zhe;WANG Yong. Automated image segmentation using the ULPCNN model with ultra-fuzzy entropy[J]. J4, 2010, 37(5): 817-824.

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一种超模糊熵ULPCNN图像自动分割新方法

Automated image segmentation using the ULPCNN model with ultra-fuzzy entropy

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