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Image segmentation through maximizing fuzzy partition entropy of 2-D histogram |
Jin Lizuo; Yuan Xiaohui; Zhao Yifan; Xia Liangzheng |
Dept. of Automatic Control Engineering Southeast University Nanjing 210096 China |
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Abstract In this paper a novel method is presented to segment gray level image through maximizing the fuzzy partition entropy of two-dimensional histogram. After the concept of fuzzy partition is briefly introduced first, a new definition of fuzzy partition entropy based on condition probability and condition entropy is presented. Then, the multi-dimensional triangular-norm is applied to construct generalized Cartesian product of non-interactive fuzzy sets, and also an approach for generating multi-dimensional fuzzy partition is presented. Finally, a new method for segmenting gray level image through maximizing the fuzzy partition entropy of two-dimensional histogram is put forward. Experiments are conducted on real object pictures, and the results show that the approach presented herein performs better than some classical threshold selection methods do.
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Received: 11 February 2001
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