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

Abstract

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

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

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