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

被引：1
|
作者：
Liu Q. [1 ,2 ]
Xu L.-P. [1 ]
Ma Y.-D. [3 ]
Su Z. [1 ]
Wang Y. [1 ]
机构：
[1] School of Electronic Engineering, Xidian Univ.
[2] School of Physics and Information Science, Tianshui Normal Univ.
[3] School of Information Sci. and Eng., Lanzhou Univ.
来源：
Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University | 2010年 / 37卷 / 05期
关键词：
Image segmentation; Maximum ultra-fuzzy entropy; Restrain and capture; Threshold functions; ULPCNN;
D O I：
10.3969/j.issn.1001-2400.2010.05.008
中图分类号：
学科分类号：
摘要：
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.
引用
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页码：817 / 824
页数：7
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