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Proceedings Paper

Statistical framework for stereo
Author(s): Wolfgang Poelzleitner; Gerhard Jakob; Gerhard Paar
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Paper Abstract

This paper deals with stereo matching, which is reformulated as a statistical pattern recognition problem. In stereo, the computation of correspondences of image points in the right and left image is viewed as a two-class pattern recognition problem. The two matching left-right points are said to constitute class 1 (matching) and the points in the neighborhood of these points form class 2 (non-matching). We have argued before that matching can be drastically improved by using several features rather than just graylevels (usually called area- based matching) or edges (usually called edge-based matching). Based on this formulation of matching as a pattern recognition problem well-known theories to optimize feature extraction and feature selection should be applied to stereo as well. In the paper we show the results of experiments to support the statistical framework for stereo and how the performance of a stereo system can be improved by taking into account the findings of statistical pattern recognition.

Paper Details

Date Published: 3 October 1995
PDF: 14 pages
Proc. SPIE 2588, Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling, (3 October 1995); doi: 10.1117/12.222705
Show Author Affiliations
Wolfgang Poelzleitner, Joanneum Research (Austria)
Gerhard Jakob, Joanneum Research (Austria)
Gerhard Paar, Joanneum Research (Austria)


Published in SPIE Proceedings Vol. 2588:
Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling
David P. Casasent, Editor(s)

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