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Shape Priors for Image Segmentation

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Book cover Shape Perception in Human and Computer Vision

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

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Abstract

The goal of image segmentation is to partition the image plane into a set of meaningful regions. While generic low-level segmentation algorithms often impose a prior which favors shorter boundaries, for segmenting familiar structures in images it may be advantageous to impose a more object-specific shape prior. Over the years, researchers have proposed different algorithms to impose prior shape knowledge based on either explicit or implicit representations of shape. In the following, I will provide a brief review of several approaches.

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Acknowledgements

The work described here was done in collaboration with numerous researchers. The author would like to thank T. Schoenemann, F.R. Schmidt, C. Schnoerr, S. Soatto, N. Sochen, T. Kohlberger, M. Rousson and S.J. Osher for their support.

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Authors and Affiliations

  1. Departments of Computer Science & Mathematics, TU Munich, Garching, Germany

    Daniel Cremers

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  1. Daniel Cremers
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Correspondence to Daniel Cremers .

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  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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Cremers, D. (2013). Shape Priors for Image Segmentation. 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_7

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  • DOI: https://doi.org/10.1007/978-1-4471-5195-1_7

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