Multiple feature models for image matching

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 constraintforcing 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 Lk norm of the projection?point distance. This approach is tested on a stereo-pair that presents multiple choices of similar likelihood.

• Type: Conference
• Year: 2005
• Where: ICIP
• Authors: Juan Morales-Sánchez, Rafael Verdú, José-Luis Sancho-Gómez, Luis Weruaga
• Keywords: ICIP 2005, Image Processing, Multiple Feature Models