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19 August 1998 Threshold selection using cross-entropy and fuzzy divergence
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Image segmentation consists of dividing an image into non- intersecting and dissimilar but meaningful regions (object and background). Thresholding is a commonly employed technique for segmenting image. Many methods for automatic selection of thresholds use optimization process in which some specific criterion functions are defined. Recently, several thresholding methods based on minimizing the cross- entropy function of images have been proposed. Cross-entropy measures the information discrepancy between two probability distributions. Derived from cross-entropy, fuzzy divergence measures the dissimilarity between two fuzzy sets. In this paper, we present four new algorithms for optimal threshold selection based on different criteria integrating cross entropy and fuzzy divergence. The first one is a minimum cross entropy algorithm based on the hypothesis of uniform probability distribution. The second one is a maximum between-class cross entropy algorithm using a posterior probability. The third one is a modified version of existing method based on maximum between-class fuzzy divergence. The last one is a minimum fuzzy divergence algorithm. According to the requirement of image thresholding, we construct a new fuzzy membership function to take into account the gray level probability distribution of object pixels and background pixels about their mean values for the last two algorithms. The effectiveness and generality of these proposed algorithms have been compared with some recent techniques based on related principles, and evaluated by using uniformity measure and shape measure with real images. Results showing the superiority of the proposed algorithms are presented.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jinghao Xue, Yujin Zhang, and Xinggang Lin "Threshold selection using cross-entropy and fuzzy divergence", Proc. SPIE 3561, Electronic Imaging and Multimedia Systems II, (19 August 1998);


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