Stereo vision correspondence using a multichannel graph matching technique

A multichannel feature-based stereo vision technique is described wherein curve segments are used as the feature primitives in the matching process. Curve segments are extracted by tracking the zero-crossings of the left and right images. The generalized Hough transform of each curve and the length of the segment in the left image are used as a local feature vector to represent the distinctive characteristics of the segment. The feature vector of each segment is used as a constraint to find an instance of the same segment in the right image. The epipolar constraint on the centroids of the curve segment is used to limit the searching space in the right image. A relational graph is formed from the left image by treating the centroids as the nodes of the graph. The local features of the segments are used to represent the local properties of the nodes, and the relationship between the nodes represents the structural properties of the object in the scene. A similar graph is formed from the right image curve segments. A graph isomorphism is then formed between the two graphs