Multiple feature models for image matching
Morales, J.
Verdu, R.
Sancho, J.L.
Weruaga, L.
Inf. & Commun. Technol., Univ. Politecnica de Cartagena, Spain;
This paper appears in: Image Processing, 2005. ICIP 2005. IEEE International Conference on
Publication Date: 11-14 Sept. 2005
Volume: 3,
On page(s): III- 1076-9
ISBN: 0-7803-9134-9
INSPEC Accession Number: 8845752
Digital Object Identifier: 10.1109/ICIP.2005.1530582
Current Version Published: 2006-03-27
Abstract
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 Lk, norm of the projection-point distance. This approach is tested on a stereo-pair that presents multiple choices of similar likelihood.
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