Generalized Smoothing Networks in Early Vision

Liu, Shih-Chii; Harris, John G.

doi:10.1109/CVPR.1989.37848

Generalized Smoothing Networks in Early Vision

Shih-Chii Liu, John G. Harris

CVPR 1989 pp. 184-191

doi:10.1109/CVPR.1989.37848 /cvpr/1989/liu1989cvpr-generalized/

Abstract

Generalized smoothing networks have been developed which enforce smoothness constraints for any arbitrary level of derivative of the input data. Furthermore, discontinuities of any order of derivative can be detected by providing for continuous line processes, which selectively inhibit smoothing. Second- and higher-order networks are required for many problems in early vision; first-order networks are often unsatisfactory. Examples in surface interpolation, edge detection, and image segmentation are shown. Solution of these types of problems typically takes a prohibitive amount of time, even on supercomputers. A significant advantage of these proposed networks is that they can be mapped directly to analog VLSI hardware.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>

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Cite

Text

Liu and Harris. "Generalized Smoothing Networks in Early Vision." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1989. doi:10.1109/CVPR.1989.37848

Markdown

[Liu and Harris. "Generalized Smoothing Networks in Early Vision." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1989.](https://mlanthology.org/cvpr/1989/liu1989cvpr-generalized/) doi:10.1109/CVPR.1989.37848

BibTeX

@inproceedings{liu1989cvpr-generalized,
  title     = {{Generalized Smoothing Networks in Early Vision}},
  author    = {Liu, Shih-Chii and Harris, John G.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1989},
  pages     = {184-191},
  doi       = {10.1109/CVPR.1989.37848},
  url       = {https://mlanthology.org/cvpr/1989/liu1989cvpr-generalized/}
}