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Genetic Fourier Descriptor for the Detection of Rotational Symmetry

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Fuzzy Logic and Applications (WILF 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2955))

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Abstract

In this paper, a Genetic Fourier Descriptors is proposed to detect rotational symmetry. Rotational symmetry is one of the important features for image decoding and object recognition in computer vision systems. In the Genetic Fourier algorithm, the Fourier descriptors are chromosomes and fitting function of the GA. The Genetic Fourier method has the following advantages. (1) It can handle partially occurred contour and opened contour, (2) It can handle complex point pattern, (3) It can obtain multiple perceptions and (4) It is highly parallel and its efficient can be greatly improved if parallel processors are used. Experimental results show that it can handle complex symmetry figures, these symmetry figures may be formed by separated curves, points or partially occurred or partially missed (open contour).

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Author information

Authors and Affiliations

  1. Department of Information and Applied Technology, Hong Kong Institute of Education, 10 Lo Ping Road, Tai Po, New Territories, Hong Kong

    Raymond K. K. Yip

Authors
  1. Raymond K. K. Yip
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Editor information

Editors and Affiliations

  1. Stanford University, Department of Statistics, Stanford, CA, USA

    Vito Di Gesú

  2. Department of Computer and Information Sciences, University of Genova, and CNISM, Via Dodecaneso 35, I-16146, Genova, Italy

    Francesco Masulli

  3. Centro Direzionale, DSA, University of Naples Parthenope, Isola C/4, 80143, Naples, Italy

    Alfredo Petrosino

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© 2006 Springer-Verlag Berlin Heidelberg

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Yip, R.K.K. (2006). Genetic Fourier Descriptor for the Detection of Rotational Symmetry. In: Di Gesú, V., Masulli, F., Petrosino, A. (eds) Fuzzy Logic and Applications. WILF 2003. Lecture Notes in Computer Science(), vol 2955. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10983652_29

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  • DOI: https://doi.org/10.1007/10983652_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-31019-8

  • Online ISBN: 978-3-540-32683-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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