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Detachable Object Detection with Efficient Model Selection

  • Conference paper
Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6819))

Abstract

We describe a computationally efficient scheme to perform model selection while simultaneously segmenting a short video stream into an unknown number of detachable objects. Detachable objects are regions of space bounded by surfaces that are surrounded by the medium other than for their region of support, and the region of support changes over time. These include humans walking, vehicles moving, etc. We exploit recent work on occlusion detection to bootstrap an energy minimization approach that is solved with linear programming. The energy integrates both appearance and motion statistics, and can be used to seed layer segmentation approaches that integrate temporal information on long timescales.

Research supported by ARO 56765, ONR N000140810414, AFOSR FA95500910427.

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Author information

Authors and Affiliations

  1. Computer Science Department, University of California, Los Angeles, USA

    Alper Ayvaci & Stefano Soatto

Authors
  1. Alper Ayvaci
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  2. Stefano Soatto
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Editor information

Editors and Affiliations

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

    Yuri Boykov  & Frank R. Schmidt  & 

  2. Centre for Mathematical Sciences, Lund University, 22100, Lund, Sweden

    Fredrik Kahl

  3. Department of Science, University of Oxford, OX1 3PJ, Oxford, UK

    Victor Lempitsky

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© 2011 Springer-Verlag Berlin Heidelberg

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Ayvaci, A., Soatto, S. (2011). Detachable Object Detection with Efficient Model Selection. In: Boykov, Y., Kahl, F., Lempitsky, V., Schmidt, F.R. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2011. Lecture Notes in Computer Science, vol 6819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23094-3_14

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  • DOI: https://doi.org/10.1007/978-3-642-23094-3_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23093-6

  • Online ISBN: 978-3-642-23094-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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