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Robust point feature matching in projective space

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1 Author(s)
G. Q. Chen ; Human-Computer-Interface Labs., STMicroelectronics, San Diego, CA, USA

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.

Published in:

Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on  (Volume:1 )

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