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Binocular Stereo by Maximizing the Likelihood Ratio Relative to a Random Terrain

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Robot Vision (RobVis 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1998))

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Abstract

A novel approach to computational binocular stereo based on the Neyman-Pearson criterion for discriminating between statistical hypotheses is proposed. An epipolar terrain profile is reconstructed by maximizing its likelihood ratio with respect to a purely random profile. A simple generative Markov-chain model of an image-driven profile that extends the model of a random profile is introduced. The extended model relates transition probabilities for binocularly and monocularly visible points along the profile to grey level differences between corresponding pixels in mutually adapted stereo images. This allows for regularizing the ill-posed stereo problem with respect to partial occlusions.

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Author information

Authors and Affiliations

  1. CITR, Department of Computer Science, Tamaki Campus, University of Auckland, Private Bag, 92019, Auckland, New Zealand

    Georgy Gimel’farb

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  1. Georgy Gimel’farb
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Editor information

Editors and Affiliations

  1. Center for Image Technology and Robotics (CITR Tamaki), The University of Auckland, Tamaki Campus, Building 731, 1005, Auckland, New Zealand

    Reinhard Klette

  2. Department of Computer Science, The Hebrew University of Jerusalem, Givat Ram, Ross Building, 91904, Jerusalem, Israel

    Shmuel Peleg

  3. Institut für Informatik, Universitäet Kiel, Preusserstr.1-9, 24105, Kiel, Germany

    Gerald Sommer

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© 2001 Springer-Verlag Berlin Heidelberg

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Gimel’farb, G. (2001). Binocular Stereo by Maximizing the Likelihood Ratio Relative to a Random Terrain. In: Klette, R., Peleg, S., Sommer, G. (eds) Robot Vision. RobVis 2001. Lecture Notes in Computer Science, vol 1998. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44690-7_25

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  • DOI: https://doi.org/10.1007/3-540-44690-7_25

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41694-4

  • Online ISBN: 978-3-540-44690-3

  • eBook Packages: Springer Book Archive

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