Probabilistic Relaxation for Matching Problems in Computer Vision
Abstract
The authors present the theory of probabilistic relaxation for matching symbolic structures, derive as limiting cases the various heuristic formulas used by researchers in matching problems, and state the conditions under which they apply. They successfully apply the theory to the problem of matching and recognizing aerial road network images based on road network models and to the problem of edge matching in a stereo pair. For this purpose, each line network is represented by an attributed relational graph where each node is a straight line segment characterized by certain attributes and related with every other node via a set of binary relations.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Cite
Text
Kittler et al. "Probabilistic Relaxation for Matching Problems in Computer Vision." IEEE/CVF International Conference on Computer Vision, 1993. doi:10.1109/ICCV.1993.378148Markdown
[Kittler et al. "Probabilistic Relaxation for Matching Problems in Computer Vision." IEEE/CVF International Conference on Computer Vision, 1993.](https://mlanthology.org/iccv/1993/kittler1993iccv-probabilistic/) doi:10.1109/ICCV.1993.378148BibTeX
@inproceedings{kittler1993iccv-probabilistic,
title = {{Probabilistic Relaxation for Matching Problems in Computer Vision}},
author = {Kittler, Josef and Christmas, William J. and Petrou, Maria},
booktitle = {IEEE/CVF International Conference on Computer Vision},
year = {1993},
pages = {666-673},
doi = {10.1109/ICCV.1993.378148},
url = {https://mlanthology.org/iccv/1993/kittler1993iccv-probabilistic/}
}