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2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'01) - Volume 1
Robust Point Feature Matching In Projective Space
Kauai, Hawaii
December 08-December 14
ISBN: 0-7695-1272-0
George Q. Chen, STMicroelectronics
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.
Citation:
George Q. Chen, "Robust Point Feature Matching In Projective Space," cvpr, vol. 1, pp.717, 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'01) - Volume 1, 2001
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