A stereo vision technique using curve-segments and relaxation matching

A multichannel feature-based stereo vision technique is described in which curve segments are used as the feature primitives in the matching process. The left and right images are first filtered by using several Laplacian of Gaussian operators of different widths (channel). Curve segments are extracted by a tracking algorithm, and their centroids are obtained. At each channel, the generalized Hough transform of each curve-segment in images is evaluated. The R-table is used as a local feature vector in representing the distinctive characteristics of a segment. The epipolar constraint on the centroids of the curve segment and the channel size is used to limit the search space in the right image. To resolve the ambiguity of the false targets (multiple matches), a relaxation technique is used in which the initial scores of the node assignments are updated by the compatibility measures between the centroids of the curve segments. The node assignments with the highest score are chosen as the matching segments