Robust Occluding Contour Detection Using the Hausdorff Distance

Yi, Xilin; Camps, Octavia I.

doi:10.1109/CVPR.1997.609444

Robust Occluding Contour Detection Using the Hausdorff Distance

Xilin Yi, Octavia I. Camps

CVPR 1997 pp. 962-968

doi:10.1109/CVPR.1997.609444 /cvpr/1997/yi1997cvpr-robust/

Abstract

In this paper, a correlational approach for distinguishing occluding contours from object markings for 3D object modeling is presented. The proposed method is valid under weak perspective projection, does not require to search for correspondences between frames, can handle scaling between consecutive images. Thus can estimate the full Euclidean surface structure, and does not require camera calibration or camera motion measurement. Extensive experimental results show that the method is robust to the occlusion of feature points and image noise unlike previous affine-based approaches. Qualitative and quantitative results for the relation between the required minimum viewing angle change for the detection and the surface curvature are also presented.

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Cite

Text

Yi and Camps. "Robust Occluding Contour Detection Using the Hausdorff Distance." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1997. doi:10.1109/CVPR.1997.609444

Markdown

[Yi and Camps. "Robust Occluding Contour Detection Using the Hausdorff Distance." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1997.](https://mlanthology.org/cvpr/1997/yi1997cvpr-robust/) doi:10.1109/CVPR.1997.609444

BibTeX

@inproceedings{yi1997cvpr-robust,
  title     = {{Robust Occluding Contour Detection Using the Hausdorff Distance}},
  author    = {Yi, Xilin and Camps, Octavia I.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1997},
  pages     = {962-968},
  doi       = {10.1109/CVPR.1997.609444},
  url       = {https://mlanthology.org/cvpr/1997/yi1997cvpr-robust/}
}