Abstract
A computational framework is introduced for matching a pair of
stereo images which, in contrast to existing algorithms, features a
self-contained local matching module cascaded with a global matching
module. Local matching outputs a 3-D grey-scale image in which each and
every point has an intensity measuring the goodness of a possible match.
Global matching reduces to surface detection in this image. To detect
the surface, it is first enhanced, employing a hyperpyramid data
structure. Unlike traditional multiresolution approaches, which are
based on the coarse-to-fine continuation method, the authors' multilevel
method emphasizes a fine-to-coarse process in which local support is
accumulated. The algorithm is concise, efficient and above all, gives
good results for complex scenes
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