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.
Robust point feature matching in projective space
01.01.2001
711050 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Robust Point Feature Matching in Projective Space
British Library Conference Proceedings | 2001
|Two dimensional projective point matching
IEEE | 2002
|Two Dimensional Projective Point Matching
British Library Conference Proceedings | 2002
|Feature matching using modified projective nonnegative matrix factorization
British Library Online Contents | 2012
|Robust feature point matching by preserving local geometric consistency
British Library Online Contents | 2009
|