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Boundaries as Contours of Optimal Appearance and Area of Support

  • Conference paper
Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5681))

Abstract

Bayesian boundary models often assume that the evidence for each contour is derived from the entire image. Consequently, the normalization term in the Bayes rule is the same for every contour and becomes irrelevant when seeking the optimal. However, in practice these models only use the vicinity of a contour, making the normalization term contour-specific. We propose a formulation that acknowledges the normalization term and includes it in the optimization. We show that it can be interpreted as a confidence measure promoting contours which are far better than other nearby candidate contours. We validate our approach in an interactive boundary delineation setting and demonstrate that complex boundaries can be extracted with significantly smaller amount of user input than when traditional Bayesian models are employed.

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Author information

Authors and Affiliations

  1. Computer Science Department, Boston College, Chestnut Hill, MA 02467, USA

    Christina Pavlopoulou & Stella X. Yu

Authors
  1. Christina Pavlopoulou
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  2. Stella X. Yu
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Editor information

Editors and Affiliations

  1. Institut für Informatik III, Rö, Universität Bonn, merstraße 164, 53117, Bonn, Germany

    Daniel Cremers

  2. Computer Science Department, University of Western Ontario, N6A 5A5, London, ON, Canada

    Yuri Boykov

  3. Microsoft Research, J.J. Thomson Avenue, CB3 OFB, Cambridge, United Kingdom

    Andrew Blake

  4. Institut für Informatik III, Universität Bonn, Römerstraße 164, 53117, Bonn, Germany

    Frank R. Schmidt

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© 2009 Springer-Verlag Berlin Heidelberg

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Pavlopoulou, C., Yu, S.X. (2009). Boundaries as Contours of Optimal Appearance and Area of Support. In: Cremers, D., Boykov, Y., Blake, A., Schmidt, F.R. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2009. Lecture Notes in Computer Science, vol 5681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03641-5_36

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  • DOI: https://doi.org/10.1007/978-3-642-03641-5_36

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03640-8

  • Online ISBN: 978-3-642-03641-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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