Constraint and consistency in stereo matching

The major difficulty in stereo vision is the correspondence problem that requires matching features in two stereo images. In this paper, we describe a constraint-based stereo matching technique using local geometric constraints among edge segments to limit the search space and to resolve matching ambiguity. Edge segment is used as the primitive image feature for stereo matching. Standard epipolar constraint and individual edge properties are used to determine possible initial matches between edge segments in the left and right images. Local edge geometric relations such as continuity, junction structure, and edge neighborhood relations are then used as constraints to guide the stereo matching process. The result is a locally consistent set of edge segment correspondences between stereo images. These locally consistent matches then generate higher-level hypotheses on extended edge segments and junctions. These hypotheses form more global contexts and allow local supports to correct incorrect local matches to achieve global consistency.