ML Anthology

Edge Localization in Surface Reconstruction Using Optimal Estimation Theory

Shailendra Mathur, Frank P. Ferrie
CVPR 1997 pp. 833-838
doi:10.1109/CVPR.1997.609424 /cvpr/1997/mathur1997cvpr-edge/

Abstract

Many relaxation based smoothing methods used in surface reconstruction algorithms filter out the effect of noise in image data, but result in the elimination of important discontinuity information as well. In this paper the inter-pixel interaction during relaxation is shown to be equivalent to a multiple measurement fusion problem which can be solved using optimal estimation theory. Pixels in a given neighbourhood act as noisy information sources, combining their information to update the state of that neighbourhood. By formulating discontinuities as another "noise" source in the image, and by using the so-called Curvature Consistency reconstruction algorithm on range images, it is shown that optimal estimation theory offers a method for the automatic and adaptive localization of discontinuities while providing a smooth piece wise continuous surface description.

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Text

Mathur and Ferrie. "Edge Localization in Surface Reconstruction Using Optimal Estimation Theory." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1997. doi:10.1109/CVPR.1997.609424

Markdown

[Mathur and Ferrie. "Edge Localization in Surface Reconstruction Using Optimal Estimation Theory." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1997.](https://mlanthology.org/cvpr/1997/mathur1997cvpr-edge/) doi:10.1109/CVPR.1997.609424

BibTeX

@inproceedings{mathur1997cvpr-edge,
  title     = {{Edge Localization in Surface Reconstruction Using Optimal Estimation Theory}},
  author    = {Mathur, Shailendra and Ferrie, Frank P.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1997},
  pages     = {833-838},
  doi       = {10.1109/CVPR.1997.609424},
  url       = {https://mlanthology.org/cvpr/1997/mathur1997cvpr-edge/}
}