NDLI: Differential evolution bare bones particle swarm optimization and its application to image segmentation

Content Provider	IEEE Xplore Digital Library
Author	Hou Chang-hong
Copyright Year	2011
Description	Author affiliation: Department of Information Science, Zhengzhou Institute of Aeronautical Industry Managment, Zhengzhou 450015, China (Hou Chang-hong)
Abstract	Basic bare bones particle swarm optimization (BBPSO) can not get good optimization performance because it easy to get stuck into local optima. Basing on basic BBPSO, using the idear of mutation in differential evolution, a new algorithm named differential evolution bare bones particle swarm optimization (DEBBPSO) is proposed. Combining with image fuzzy entropy, applies DEBBPSO to image segmentation. Uses DEBBPSO to explore fuzzy parameters of maximum fuzzy entropy, and gets the optimum fuzzy parameter combination, then obtains the segmentation threshold. According to experiment results of the new algorithm compare with other two algorithms, the proposed algorithm performs good segmentation performance and very low time cost. It can be use to real time and precision measure coal dust image.
Starting Page	1680
Ending Page	1683
File Size	303175
Page Count	4
File Format	PDF
ISBN	9781424487370
e-ISBN	9781424487387
DOI	10.1109/CCDC.2011.5968465
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	differential evolution Image segmentation Heuristic algorithms Power system dynamics image segmentation Coal Bones fuzzy entropy Entropy bare bones particle swarm optimization Particle swarm optimization
Content Type	Text
Resource Type	Article

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