Robust Point Feature Matching in Projective Space

Chen, George Q.

doi:10.1109/CVPR.2001.990546

Robust Point Feature Matching in Projective Space

George Q. Chen

CVPR 2001 pp. I:717-722

doi:10.1109/CVPR.2001.990546 /cvpr/2001/chen2001cvpr-robust/

Abstract

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.

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Cite

Text

Chen. "Robust Point Feature Matching in Projective Space." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2001. doi:10.1109/CVPR.2001.990546

Markdown

[Chen. "Robust Point Feature Matching in Projective Space." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2001.](https://mlanthology.org/cvpr/2001/chen2001cvpr-robust/) doi:10.1109/CVPR.2001.990546

BibTeX

@inproceedings{chen2001cvpr-robust,
  title     = {{Robust Point Feature Matching in Projective Space}},
  author    = {Chen, George Q.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2001},
  pages     = {I:717-722},
  doi       = {10.1109/CVPR.2001.990546},
  url       = {https://mlanthology.org/cvpr/2001/chen2001cvpr-robust/}
}