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Figure-ground separation: A case study in energy minimization via evolutionary computing

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Book cover Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1223))

Abstract

It is known that the problem of figure-ground separation can be modeled as one of energy minimization using the Ising system model from quantum physics. The Ising system model for the figure-ground separation problem makes explicit the definition of shape in terms of attributes such as cocircularity, smoothness, proximity and contrast and is based on the formulation of an energy function that incorporates pairwise interactions between local image features in the form of edgels. This paper explores a class of stochastic optimization techniques based on evolutionary algorithms for the problem of figure-ground separation using the Ising system model. Experimental results on synthetic edgel maps and edgel maps derived from gray scale images are presented. The advantages and shortcomings of evolutionary algorithms in the context of figure-ground separation are discussed.

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Authors and Affiliations

  1. Department of Computer Science, The University of Georgia, 30602-7404, Athens, Georgia, USA

    Suchendra M. Bhandarkar & Xia Zeng

Authors
  1. Suchendra M. Bhandarkar
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  2. Xia Zeng
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Marcello Pelillo Edwin R. Hancock

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© 1997 Springer-Verlag Berlin Heidelberg

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Bhandarkar, S.M., Zeng, X. (1997). Figure-ground separation: A case study in energy minimization via evolutionary computing. In: Pelillo, M., Hancock, E.R. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 1997. Lecture Notes in Computer Science, vol 1223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-62909-2_92

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  • DOI: https://doi.org/10.1007/3-540-62909-2_92

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-62909-2

  • Online ISBN: 978-3-540-69042-9

  • eBook Packages: Springer Book Archive

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