二维Arimoto 灰度熵阈值分割

doi:10.3969/j.issn.0255-8297.2014.04.001

Abstract

Abstract: A recently proposed 2D Arimoto entropy thresholding method only depends on frequency information of gray scale in an image, without considering uniformity of within-class gray scales. To solve this problem, a 2D Arimoto gray entropy thresholding method based on gray scale-gradient histogram is proposed.Uniformity of within-class gray scale is considered based on Arimoto entropy and a formula for 1D Arimoto gray entropy threshold selection constructed. Using regional division of object and background in a gray scale-gradient 2D histogram, a formula for 2D Arimoto gray entropy threshold selection is derived. Recursion formulae of intermediate variables in the threshold selection criterion function are used to eliminate redundant computation. The local period of an artificial bee colony algorithm is improved using a chaotic sequence based on tent mapping. The improved bee colony optimization algorithm can accelerate search speed of the optimal threshold for image segmentation to significantly reduce execution time. Experimental results based on a large number of typical images show that the proposed method can segment image quickly and accurately, with the overall performance better than 2D Shannon entropy thresholding, Tsallis gray entropy thresholding, and Arimoto entropy thresholding.

Key words: image segmentation, threshold selection, Arimoto gray entropy, artificial bee colony algorithm, Tent mapping, chaos

CLC Number:

TN911.73

WU Yi-quan1,2, CAO Peng-xiang1,3, WANG Kai1, YIN Jun1. Two-Dimensional Arimoto Gray Entropy Thresholding[J]. Journal of Applied Sciences, 2014, 32(4): 331-340.

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URL: https://www.jas.shu.edu.cn/EN/10.3969/j.issn.0255-8297.2014.04.001

https://www.jas.shu.edu.cn/EN/Y2014/V32/I4/331

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Two-Dimensional Arimoto Gray Entropy Thresholding

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