Genetic particle swarm optimization for polygonal approximation of digital curves |
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Authors: | P Y Yin |
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Affiliation: | (1) Department of Information Management, National Chi Nan University, Nantou, 545, Taiwan |
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Abstract: | Polygonal approximation is an important technique in image representation which directly impacts on the accuracy and efficacy
of the subsequent image analysis tasks. This paper presents a new polygonal approximation approach based on particle swarm
optimization (PSO). The original PSO is customized to continuous function value optimization. To facilitate the applicability
of PSO to combinatorial optimization involving the problem in question, genetic reproduction mechanisms, namely crossover
and mutation, are incorporated into PSO. We also propose a hybrid strategy embedding a segment-adjusting-and-merging optimizer
into the genetic PSO evolutionary iterations to enhance its performance. The experimental results show that the proposed genetic
PSO significantly improves the search efficacy of PSO for the polygonal approximation problem, and the hybrid strategy can
accelerate the convergence speed but still with good-quality results. The performance of the proposed method is compared to
existing approaches on both synthesized and real image curves. It is shown that the proposed hybrid genetic PSO outperforms
the polygonal approximation approaches based on genetic algorithms and ant colony algorithms.
The text was submitted by the author in English.
Peng-Yeng Yin was born in 1966 and received his B.S., M.S. and Ph.D. degrees in Computer Science from National Chiao Tung University, Hsinchu,
Taiwan, in 1989, 1991 and 1994, respectively. From 1993 to 1994, he was a visiting scholar at the Department of Electrical
Engineering, University of Maryland, and the Department of Radiology, Georgetown University. In 2000, he was a visiting Associate
Professor in the Visualization and Intelligent Systems Lab (VISLab) at the Department of Electrical Engineering, University
of California, Riverside (UCR). He is currently a Professor at the Department of Information Management, National Chi Nan
University, Nantou, Taiwan. His current research interests include image processing, pattern recognition, machine learning,
computational biology, and evolutionary computation. He has published more than 70 articles in refereed journals and conferences.
Dr. Yin received the Overseas Research Fellowship from the Ministry of Education in 1993 and Overseas Research Fellowship
from the National Science Council in 2000. He is a member of the Phi Tau Phi Scholastic Honor Society and listed in Who’s Who in the World. |
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Keywords: | polygonal approximation particle swarm optimization genetic algorithm ant colony optimization local optimal solution global optimal solution |
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