Presentation 2001/3/16
Optimization of Image Processing by Genetic and Evolutionary Computation : How to Realize Still Better Performance
Hisashi Shimodaira,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) In this paper, we examine the results of major previous attempts to apply genetic and evolutionary computation (GEC) to image processing. In many problems, the accuracy (quality) of solutions obtained by GEC-based methods is better than that obtained by other methods such as conventional methods, neural networks and simulated annealing. However, the computation time required is satisfactory in some problems, whereas it is unsatisfactory in other problems. We consider the current problems of GEC-based methods and present the following measures to achieve still better performance: (1) utilizing competent GECs, (2) incorporating other search algorithms such as local hill climbing algorithms, (3) hybridizing with conventional image processing algorithms, (4) modeling the given problem with as smaller parameters as possible, and (5) using parallel processors to evaluate the fitness function.
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Keyword(in English) Genetic Algorithm / Evolutionary Computation / Image Processing / Optimization / Performance
Paper # PRMU2000-245
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Conference Information
Committee PRMU
Conference Date 2001/3/16(1days)
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Registration To Pattern Recognition and Media Understanding (PRMU)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Optimization of Image Processing by Genetic and Evolutionary Computation : How to Realize Still Better Performance
Sub Title (in English)
Keyword(1) Genetic Algorithm
Keyword(2) Evolutionary Computation
Keyword(3) Image Processing
Keyword(4) Optimization
Keyword(5) Performance
1st Author's Name Hisashi Shimodaira
1st Author's Affiliation Faculty of Information and Communications, Bunkyo University()
Date 2001/3/16
Paper # PRMU2000-245
Volume (vol) vol.100
Number (no) 702
Page pp.pp.-
#Pages 8
Date of Issue