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A PSO-Based Method for Min-ε Approximation of Closed Contour Curves

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Advances in Neural Networks - ISNN 2008 (ISNN 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5263))

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Abstract

Finding a polygon to approximate the contour curve with the minimal approximation error ε under the pre-specified number of vertices, is termed min-ε problem. It is an important issue in image analysis and pattern recognition. A discrete version of particle swarm optimization (PSO) algorithm is proposed to solve this problem. In this method, the position of each particle is represented as a binary string which corresponds to an approximating polygon. Many particles form a swarm to fly through the solution space to seek the best one. For those particles which fly out of the feasible region, the traditional split and merge techniques are applied to adjust their position which can not only move the particles from the infeasible solution space to the feasible region, but also relocate it in a better site. The experimental results show that the proposed PSO-based method has the higher performance over the GA-based methods.

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Author information

Authors and Affiliations

  1. Key Laboratory of Electronic Business, Nanjing University of Finance and Economics, Nanjing, 210003, P.R. China

    Bin Wang

  2. Merchant Marine College, Shanghai Maritime University, Shanghai, 200135, P.R. China

    Chaojian Shi

  3. Department of Computer Science and Engineering, Fudan University, Shanghai, 200433, P.R. China

    Chaojian Shi

  4. Alcatel Shanghai Bell Company Limited, Shanghai, 201206, P.R. China

    Jing Li

Authors
  1. Bin Wang
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  2. Chaojian Shi
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  3. Jing Li
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Tsinghu University, 100084, Beijing, China

    Fuchun Sun

  2. Institute TAMS (Technical Aspects of Multimodal Systems), department of Informatics, University of Hamburg, Vogt-Koelln-Straße 30, 22527, Hamburg, Germany

    Jianwei Zhang

  3. Intel China Research Center, 8/F, Peking University, Department of Machine Intelligence, 100871, Beijing, China

    Ying Tan

  4. Department of Mathematics, Southeast University, 210096, Nanjing, China

    Jinde Cao

  5. Departamento de Control Automático, CINVESTAV-IPN, A.P. 14-740, Av.IPN 2508, 07360, México D.F., México

    Wen Yu

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© 2008 Springer-Verlag Berlin Heidelberg

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Wang, B., Shi, C., Li, J. (2008). A PSO-Based Method for Min-ε Approximation of Closed Contour Curves. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87732-5_98

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  • DOI: https://doi.org/10.1007/978-3-540-87732-5_98

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87731-8

  • Online ISBN: 978-3-540-87732-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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