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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Multiple feature models for image matching


    Contributors:
    Morales, J. (author) / Verdu, R. (author) / Sancho, J.L. (author) / Weruaga, L. (author)


    Publication date :

    2005-01-01


    Size :

    280936 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Multiple Feature Models for Image Matching

    Morales, J. / Verdu, R. / Sancho, J. L. et al. | British Library Conference Proceedings | 2005



    Feature Matching for Building Extraction from Multiple Views

    Roux, M. / McKeown, D. M. / Institute of Electrical and Electronics Engineers; Computer Society | British Library Conference Proceedings | 1994


    Hierarchical semantic image matching using CNN feature pyramid

    Yu, Wei / Sun, Xiaoshuai / Yang, Kuiyuan et al. | British Library Online Contents | 2018


    Image Feature Matching Algorithm Research on Topography Measurement

    Wang, W. / Li, P. / Zhang, Y. | British Library Online Contents | 2008