The common approach to image matching is to detect spatial features present in both images and create a mapping that relates both images. The main drawback of this method takes place when more than one matching is likely. A first simplification to this ambiguity is to represent with a parametric model the point locus where the matching is highly likely, and then use a POCS (projection onto convex sets) procedure combined with Tikhonov regularization that results in the mapping vectors. However, if there is more than one model per pixel, the regularization and constraint-forcing process faces a multiple-choice dilemma that has no easy solution. This work proposes a framework to overcome this drawback: the combined projection over multiple models based on the L/sub k/, norm of the projection-point distance. This approach is tested on a stereo-pair that presents multiple choices of similar likelihood.
['Juan Morales', 'R. Verdu', 'J.L. Sancho', 'Luis Weruaga']
Multiple feature models for image matching