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.
Multiple feature models for image matching
IEEE International Conference on Image Processing 2005 ; 3 ; III-1076
2005-01-01
280936 byte
Conference paper
Electronic Resource
English
Multiple Feature Models for Image Matching
British Library Conference Proceedings | 2005
|Feature Matching for Building Extraction from Multiple Views
British Library Conference Proceedings | 1994
|Hierarchical semantic image matching using CNN feature pyramid
British Library Online Contents | 2018
|Image Feature Matching Algorithm Research on Topography Measurement
British Library Online Contents | 2008
|