Image segmentation through maximizing fuzzy partition entropy of 2-D histogram

Jin Lizuo; Yuan Xiaohui; Zhao Yifan; Xia Liangzheng

Volume 24 Issue 8

Aug. 2002

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Article Navigation > Journal of Electronics & Information Technology > 2002 > 24(8): 1040-1048

Jin Lizuo, Yuan Xiaohui, Zhao Yifan, Xia Liangzheng. Image segmentation through maximizing fuzzy partition entropy of 2-D histogram[J]. Journal of Electronics & Information Technology, 2002, 24(8): 1040-1048.

Citation:

Jin Lizuo, Yuan Xiaohui, Zhao Yifan, Xia Liangzheng. Image segmentation through maximizing fuzzy partition entropy of 2-D histogram[J]. Journal of Electronics & Information Technology, 2002, 24(8): 1040-1048.

Citation:

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Image segmentation through maximizing fuzzy partition entropy of 2-D histogram

Received Date: 2001-02-11
Rev Recd Date: 2001-08-16
Publish Date: 2002-08-19

Abstract

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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References

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