Stereo grouping for model-based recognition
Aleš Ude, T.E. Ekre · 1996
A strategy for the fusion of information from a stereo image pair for model-based object recognition is discussed. Our scheme combines a new method for feature grouping with a region-based stereo matching and a hypothesize-and-verify paradigm. The grouping method developed is based on a graph theoretical algorithm. It exploits prior knowledge to find the groups of image features which are likely to come from a sought model(s). The Bayesian classification is used to deal with the resulting hypotheses. A mechanism for a dynamic threshold modification is incorporated into the system to enable the grouping at different resolutions. Unlike classical techniques for object recognition from stereo, our strategy does not depend on a data driven computation of a depth map. We argue that a propulsive reconstruction of 3D information can be more efficient and robust.