We present a hierarchical approach for feature description and stereo matching in computer vision. Hierarchy is first used to make a high-level description of the scene. Edge pixels and lines are extracted and grouped into higher level symbolic descriptions: L-junctions, facets, surfaces. Because they are fewer in number and more significant, high-level features are easier to match, and help reduce matching ambiguity. Thus, the matching process is hierarchical: it starts with surfaces at the highest level, and is propagated down to lines at the lowest level. To determine the best order of matching, a control strategy is defined by classifying high-level features into areas of attention according to certainty and confidence criteria. Thus better formed features are matched first.
Hierarchical feature grouping for stereo matching
01.01.1996
797951 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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