Fuzzy region competition images segmentation driven by local entropy

doi:10.11834/jig.20110612

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Fuzzy region competition images segmentation driven by local entropy
Vol. 16, Issue 6, Pages: 953-959(2011)
Online First：15 June 2011，

Published：2011
DOI： 10.11834/jig.20110612
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DOI：

Fuzzy region competition images segmentation driven by local entropy[J]. Journal of Image and Graphics, 2011, 16(6): 953-959. DOI： 10.11834/jig.20110612.

摘要

针对现有图像分割模型对光照敏感，提出一种新的基于区域的主动轮廓线模型。该模型能量泛函包含一个惩罚区域弧长的几何正则项和一个区域数据拟合项，特别的是数据拟合项采用局部熵来区分不同的区域。首先，根据图像像素空间排列之间的相关性，采用一个滑动窗函数提取图像局部熵特征

将图像从灰度空间转化到相应局部熵特征空间；然后，在局部熵空间计算最大后验分割概率得出两相区域竞争模型，为了能够快速求解该模型，采用隶属度函数替换特征函数得到了凸的模糊区域竞争模型。最后，采用快速的Chambolle对偶方法得到全局最小解。实验结果表明，该算法可以得到令人满意的分割效果且收敛速度快和对光照稳定。

Abstract

This paper proposes a new region-based active contour for existing images segmentation model to light-sensitive. The energy functional consists of a geometric regularization term that penalizes the length of region boundaries and a data fitting term. Particularly

the local entropy is used as the data fitting term to distinguish different region. First

this paper uses a sliding window function to extract the local entropy according to the relationship of spatial arrangements of image pixel

which can map intensity space of image to local entropy space. Then

we can get the region competition model by maximum a posteriori segmentation probability in local entropy space. Next

it has a fuzzy region competition model by the membership function to replace the characteristic function to solving this model. Finally one can solve this model using fast Chambolle’s dual method. The experimental results for some images show desirable performances of this model

which has the fast convergence speed and light stability.