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An Integrated Bayesian Approach to Shape Representation and Perceptual Organization

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Shape Perception in Human and Computer Vision

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

We present a unified Bayesian approach to shape representation and related problems in perceptual organization, including part decomposition, shape similarity, figure/ground estimation, and 3D shape. The approach is based on the idea of estimating the skeletal structure most likely to have generated the observed shape via a process of stochastic “growth.” We survey the approach briefly and show how it can be extended in a principled way to solve a wide array of related problems.

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Author information

Authors and Affiliations

  1. Department of Psychology, Center for Cognitive Science, Rutgers University, New Brunswick, USA

    Jacob Feldman

  2. Department of Psychology, Rutgers University, New Brunswick, USA

    Manish Singh

  3. Aerospace, Transportation and Advanced Systems Laboratory, Georgia Tech Research Institute, Atlanta, GA, USA

    Erica Briscoe, Vicky Froyen, Seha Kim & John Wilder

Authors
  1. Jacob Feldman
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  2. Manish Singh
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  3. Erica Briscoe
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  4. Vicky Froyen
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  5. Seha Kim
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  6. John Wilder
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Corresponding author

Correspondence to Jacob Feldman .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Toronto, King's College Road 6, Toronto, M5S 3G4, Ontario, Canada

    Sven J. Dickinson

  2. Department of Psychological Sciences, Purdue University, Third Street 703, West Lafayette, 47907, Indiana, USA

    Zygmunt Pizlo

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© 2013 Springer-Verlag London

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Feldman, J., Singh, M., Briscoe, E., Froyen, V., Kim, S., Wilder, J. (2013). An Integrated Bayesian Approach to Shape Representation and Perceptual Organization. In: Dickinson, S., Pizlo, Z. (eds) Shape Perception in Human and Computer Vision. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-5195-1_4

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  • DOI: https://doi.org/10.1007/978-1-4471-5195-1_4

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-5194-4

  • Online ISBN: 978-1-4471-5195-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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