<title>Statistical framework for stereo</title>

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.