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Proceedings Paper

Bayesian stereo: 3D vision designed for sensor fusion
Author(s): John Larson; Robert B. Pless
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Paper Abstract

Classical stereo algorithms attempt to reconstruct 3D models of a scene by matching points between two images. Finding points that match is an important part of this process, and point matches are most commonly chosen as the minimum of an error function based on color or local texture. Here we motivate a probabilistic approach to this point matching problem, and provide an experimental design for the empirical measurement of the color matching error for corresponding points. We use this prior in a Bayesian scene reconstruction example, and show that we get better 3D reconstruction by not committing to a specific pixel match early in the visual processing. This allows a calibrated stereo camera to be considered as a probabilistic volume sensor -- which allows it to be more easily integrated with scene structure measurements from other kinds of sensors.

Paper Details

Date Published: 25 October 2004
PDF: 9 pages
Proc. SPIE 5608, Intelligent Robots and Computer Vision XXII: Algorithms, Techniques, and Active Vision, (25 October 2004); doi: 10.1117/12.571537
Show Author Affiliations
John Larson, Washington Univ./St Louis (United States)
Robert B. Pless, Washington Univ./St Louis (United States)

Published in SPIE Proceedings Vol. 5608:
Intelligent Robots and Computer Vision XXII: Algorithms, Techniques, and Active Vision
David P. Casasent; Ernest L. Hall; Juha Roning, Editor(s)

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