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Evolutionary Algorithm-Based Local Structure Modeling for Improved Active Shape Model

  • Conference paper
Book cover Applications of Evolutionary Computing (EvoWorkshops 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3005))

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Abstract

An evolutionary algorithm-based robust local structure modeling technique is proposed to improve the performance of the active shape model (ASM). The proposed algorithm can extract boundary of an object under adverse condition, such as noisy corruption, occlusions, and shadow effect. The principle idea of the evolutionary algorithm is to find the global minimum of an objective function by evolving from a large set of populations rather than a single solution which may cause a local minimum. The proposed algorithm has been tested for various images including a sequence of human motion to demonstrate the improved performance of object tracking based on the evolutionary ASM.

This work was supported in part Korean Ministry of Science and Technology under the National Research Lab. Project and in part by Korean Ministry of Education under Brain Korea 21 Project.

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References

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Author information

Authors and Affiliations

  1. Image Processing and Intelligent Systems Lab. Department of Image Engineering, Graduate School of Advanced Imaging Science, Multimedia, and Film, Chung-Ang University, 221 Huksuk-Dong, Tongjak-Ku, Seoul, 156-756, Korea

    Jeongho Shin, Hyunjong Ki, Vivek Maik, Jinyoung Kang, Junghoon Jung & Joonki Paik

Authors
  1. Jeongho Shin
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  2. Hyunjong Ki
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  3. Vivek Maik
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  4. Jinyoung Kang
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  5. Junghoon Jung
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  6. Joonki Paik
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Editor information

Editors and Affiliations

  1. Institute of Computer Graphics and Algorithms, Vienna University of Technology, Favoritenstraße 9–11/1861, 1040, Vienna, Austria

    Günther R. Raidl

  2. Dipartimento di Ingegneria dell’Informazione, Università di Parma,  

    Stefano Cagnoni

  3. Institute AIFB, University of Karlsruhe, 76128, Karlsruhe, Germany

    Jürgen Branke

  4. School of Mathematical and Computer Science, Heriot-Watt University, EH14 8AS, Edinburgh, UK

    David Wolfe Corne

  5. Institute of Computer Science, University of Bremen, 28359, Bremen, Germany

    Rolf Drechsler

  6. Honda Research Institute Europe GmbH, Offenbach/Main, Germany

    Yaochu Jin

  7. Computing Laboratory, University of Kent, Canterbury, UK

    Colin G. Johnson

  8. CISUC, Department of Informatics Engineering, University of Coimbra, Polo II of the University of Coimbra, 3030, Coimbra, Portugal

    Penousal Machado

  9. ICIS, Radboud University Nijmegen, The Netherlands

    Elena Marchiori

  10. Johannes Gutenberg University, Mainz, Germany

    Franz Rothlauf

  11. School of Computing Sciences, UEA Norwich, University of East Anglia, NR4 7TJ, Norwich, UK

    George D. Smith

  12. Dipartimento di Automatica e Informatica, Politecnico di Torino, Italy

    Giovanni Squillero

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© 2004 Springer-Verlag Berlin Heidelberg

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Shin, J., Ki, H., Maik, V., Kang, J., Jung, J., Paik, J. (2004). Evolutionary Algorithm-Based Local Structure Modeling for Improved Active Shape Model. In: Raidl, G.R., et al. Applications of Evolutionary Computing. EvoWorkshops 2004. Lecture Notes in Computer Science, vol 3005. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24653-4_37

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  • DOI: https://doi.org/10.1007/978-3-540-24653-4_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-21378-9

  • Online ISBN: 978-3-540-24653-4

  • eBook Packages: Springer Book Archive

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