DocumentCode :
2726829
Title :
Image segmentation based on two -dimension Fuzzy Tsallis-Entropy
Author :
Yang, Shu-Hong ; Xia, Dong-Xue ; Li, Chun-Gui ; Zeng-Fang Zhang
Author_Institution :
Dept. of Comput. Eng., Guangxi Univ. of Technol., Liuzhou, China
Volume :
4
fYear :
2009
fDate :
20-22 Nov. 2009
Firstpage :
342
Lastpage :
345
Abstract :
Image processing bears some fuzziness in nature, as a effective mathematical tool for handling the ambiguity, Fuzzy set theory is introduced in the paper to define a new kind of fuzzy entropy, namely two-dimension fuzzy Tsallis entropy (TFTE) and applied in image segmentation following the maximum entropy principle. To overcome the huge calculational burden when generalizing one-dimension entropy to two-dimension, the particle swarm optimization (PSO) algorithm was employed to accelerate the search of the optimal threshold. The validity and effectiveness of the presented method is illustrated by experiments and the application of Tsallis Entropy is generalized to fuzzy fields.
Keywords :
fuzzy set theory; image segmentation; particle swarm optimisation; fuzzy set theory; image segmentation; maximum entropy principle; particle swarm optimization algorithm; two-dimension fuzzy Tsallis-entropy; Acceleration; Entropy; Fuzzy set theory; Histograms; Image edge detection; Image processing; Image segmentation; Paper technology; Particle swarm optimization; Pixel; Image segmentation; Maximum entropy; Particle swarm optimization (PSO); Two-dimension Fuzzy Tsallis Entropy(TFTE);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-4754-1
Electronic_ISBN :
978-1-4244-4738-1
Type :
conf
DOI :
10.1109/ICICISYS.2009.5357666
Filename :
5357666
Link To Document :
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