Multiple feature models for image matching
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Knowledge AreaTeoría de la Señal y las Comunicaciones
SponsorsThis work is partially supported by the Spanish Ministerio de Ciencia y Tecnología,under grant TIC2002-03033.
PublisherIEEE Institute of Electrical and Electronics
Bibliographic CitationMORALES, Juan, VERDÚ, Rafael, SÁNCHO, José Luis, WERUAGA, Luis. Multiple feature models for image matching. En: IEEE International Conference on Imagen Processing (2005: Génova). International Conference on Image Processing (ICIP 2005), Genova, Italy, September 11-14, 2005. Genova: IEEE, 2005
POCS (Proyección de Conjuntos Convexos)
Regularización de Tikhonov
Mapa de vectores
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 draw back of this method takes place when more than one matching is likely. A first simplification to this ambiguity is to represent with apara-metric model the point locus where the matching is highly likely,and then use a POCS(projection on to convex sets)procedure combined with Tikhonov regularization that results in the mapping vectors. However,if there is more than one model perpixel,the regularization and constrainforcing process faces a multiplechoice dilemma that has no easy solution. This work proposes a frame work to overcome this draw back: the combined projection over multiple models base don the norm of the projection–pointdis-tance. This approach is tested on a stereo-pair that presents multiple choices of similar likelihood.
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