Multiple feature models for image matching
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URI: http://hdl.handle.net/10317/700Compartir
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Teoría de la Señal y las ComunicacionesPatrocinadores
This work is partially supported by the Spanish Ministerio de Ciencia y Tecnología,under grant TIC2002-03033.Fecha de publicación
2005-09Editorial
IEEE Institute of Electrical and ElectronicsCita bibliográfica
MORALES SÁNCHEZ, Juan et al. 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, 2005Palabras clave
Modelo paramétricoPOCS (Proyección de Conjuntos Convexos)
Regularización de Tikhonov
Mapa de vectores
Resumen
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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