Segment-based stereo matching
Images are 2-dimensional projections of 3-dimensional scenes, therefore depth recovery is a crucial problem in image understanding, with applications in passive navigation, cartography, surveillance, and industrial robotics. Stereo analysis provides a more direct quantitative depth evaluation than techniques such as shape from shading, and its being passive makes it more applicable than active range finding imagery by laser or radar. This paper addresses the subproblem of identifying corresponding points in the two images. The primitives we use are groups of collinear connected edge points called segments, and we base the correspondence on the “minimum differential disparity” criterion. The result of this processing is a sparse array disparity map of the analyzed scene.