A stereo vision technique using curve-segments and relaxation matching

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Abstract
A multichannel feature-based stereo vision technique where curve segments are used as feature primitives in the matching process is described. The left image and the right image are filtered by using several Laplacian-of-Gaussian operators of different widths (channels). 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 the left and the right image is evaluated. The epipolar constraint on the centroids of the curve segment and the channel size is used to limit the searching space in the right image. To resolve the ambiguity of the false targets (multiple matches), a relaxation technique is used where 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 curve segments.

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