Adaptive Smoothing: A General Tool for Early Vision
Abstract
The authors present a method to smooth a signal-whether it is an intensity image, a range image, or a contour-which preserves discontinuities and thus facilitates their detection. This is achieved by repeatedly convolving the signal with a very small averaging filter modulated by a measure of the signal discontinuity at each point. This process is related to the anisotropic diffusion reported by P. Perona and J. Malik (1987) but it has a much simpler formulation and is not subject to instability or divergence. Real examples show how this approach can be applied to the smoothing of various types of signals. The detected features do not move, and thus no tracking is needed. The last property makes it possible to derive a novel scale-space representation of a signal using a small number of scales. Finally, this process is easily implemented on parallel architectures: the running time on a 16 K connection machine is three orders of magnitude faster than on a serial machine.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Cite
Text
Saint-Marc et al. "Adaptive Smoothing: A General Tool for Early Vision." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1989. doi:10.1109/CVPR.1989.37910Markdown
[Saint-Marc et al. "Adaptive Smoothing: A General Tool for Early Vision." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1989.](https://mlanthology.org/cvpr/1989/saintmarc1989cvpr-adaptive/) doi:10.1109/CVPR.1989.37910BibTeX
@inproceedings{saintmarc1989cvpr-adaptive,
title = {{Adaptive Smoothing: A General Tool for Early Vision}},
author = {Saint-Marc, Philippe and Chen, Jer-Sen and Medioni, Gérard G.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {1989},
pages = {618-624},
doi = {10.1109/CVPR.1989.37910},
url = {https://mlanthology.org/cvpr/1989/saintmarc1989cvpr-adaptive/}
}