We present a robust method for matching point features across a set of images under full perspective projection. An expectation-maximization-like algorithm is developed to build an optimal potential match set (PMS) between each consecutive pair of views, by iteratively maximizing a heuristic objective function. All two-view matches are combined to form an M-view potential match set (MPMS) with a low contamination rate. Outliers in MPMS are removed incorporating the least-median-of-squares technique with projective reconstruction. The current work extends previous ones in two- or three-view matching, or under affine camera projection. Results on real imagery demonstrate the validity of the proposed method.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Robust point feature matching in projective space


    Contributors:
    Chen, G.Q. (author)


    Publication date :

    2001-01-01


    Size :

    711050 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Robust Point Feature Matching in Projective Space

    Chen, G. Q. / IEEE | British Library Conference Proceedings | 2001


    Two dimensional projective point matching

    Denton, J. / Beveridge, J.R. | IEEE | 2002


    Two Dimensional Projective Point Matching

    Denton, J. / Beveridge, J. R. / IEEE Computer Society et al. | British Library Conference Proceedings | 2002


    Feature matching using modified projective nonnegative matrix factorization

    Yan, W. / Tian, Z. / Wen, J. et al. | British Library Online Contents | 2012


    Robust feature point matching by preserving local geometric consistency

    Choi, O. / Kweon, I. S. | British Library Online Contents | 2009