DocumentCode :
437083
Title :
Infrared image segmentation via intelligent genetic algorithm based on maximum entropy
Author :
Jin, Wu ; Ya, Qiu ; Jian, Liu ; Jinwen, Tian
Author_Institution :
State Key Lab. for Image Process. & Intelligence Control, Huazhong Univ. of Sci. & Technol., Wuhan, China
Volume :
1
fYear :
2004
fDate :
31 Aug.-4 Sept. 2004
Firstpage :
789
Abstract :
This paper presents a fast and effective segmentation method for infrared image, based on fuzzy filtering, the criteria of maximum entropy and intelligent genetic algorithm. A fuzzy filter is applied to depress the Gaussian(-like) noise. Then we use the theory of maximum entropy to select the optimum threshold. A new intelligent genetic algorithm (IGA). which applies an intelligent crossover (IC) based on orthogonal arrays (OAS). is proposed to solve this optimal problem. Experiment results show that the proposed method can depress the Gaussian(-like) noise effectively, segment the infrared image properly, and is faster than the conventional genetic algorithm and exhaustive search, also is easier to implement on hardware.
Keywords :
Gaussian noise; fuzzy set theory; genetic algorithms; image segmentation; infrared imaging; maximum entropy methods; optical engineering computing; Gaussian noise; exhaustive search; fuzzy filtering; infrared image segmentation; intelligent crossover; intelligent genetic algorithm; maximum entropy; optimal problem; optimum threshold; orthogonal arrays; Entropy; Filtering; Filters; Gaussian noise; Genetic algorithms; Hardware; Image segmentation; Infrared imaging; Integrated circuit noise; White noise;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing, 2004. Proceedings. ICSP '04. 2004 7th International Conference on
Print_ISBN :
0-7803-8406-7
Type :
conf
DOI :
10.1109/ICOSP.2004.1452781
Filename :
1452781
Link To Document :
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