DocumentCode :
2620048
Title :
Closed-loop adaptive image segmentation
Author :
Bhanu, Bir ; Ming, John ; Lee, Sungkee
Author_Institution :
Dept. of Electr. Eng., California Univ., Riverside, CA, USA
fYear :
1991
fDate :
3-6 Jun 1991
Firstpage :
734
Lastpage :
735
Abstract :
A closed-loop image segmentation system that incorporates a genetic algorithm to adapt the segmentation process to changes in image characteristics caused by variable environmental conditions is presented. The genetic algorithm efficiently searches the hyperspace of segmentation parameter combinations to determine the parameter set which maximizes the segmentation quality criteria. A summary of the experimental results that demonstrates the ability to perform adaptive image segmentation and to learn from experience using a collection of outdoor color imagery is given
Keywords :
genetic algorithms; pattern recognition; picture processing; self-adjusting systems; adaptive image segmentation; closed-loop image segmentation system; computer vision; genetic algorithm; image characteristics; outdoor color imagery; quality criteria; Adaptive systems; Application software; Color; Computer science; Computer vision; Current measurement; Genetic algorithms; Humans; Image segmentation; Learning systems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 1991. Proceedings CVPR '91., IEEE Computer Society Conference on
Conference_Location :
Maui, HI
ISSN :
1063-6919
Print_ISBN :
0-8186-2148-6
Type :
conf
DOI :
10.1109/CVPR.1991.139805
Filename :
139805
Link To Document :
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