ML Anthology

Optimal Multi-Scale Matching

Michael S. Lew, Thomas S. Huang
CVPR 1999 pp. 1088-1093
doi:10.1109/CVPR.1999.786922 /cvpr/1999/lew1999cvpr-optimal/

Abstract

The coarse-to-fine search strategy is extensively used in current reported research. However, it has the same problem as any hill climbing algorithm, most importantly, it often finds local instead of global minima. Drawing upon the artificial intelligence literature, we applied an optimal graph search, namely A*, to the problem. Using real stereo and video test sets, we compared the A* method to both template and hill climbing. Our results show that A* has greater accuracy than the ubiquitous coarse-to-fine hill climbing pyramidal search algorithm in both stereo matching and motion tracking.

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Cite

Text

Lew and Huang. "Optimal Multi-Scale Matching." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1999. doi:10.1109/CVPR.1999.786922

Markdown

[Lew and Huang. "Optimal Multi-Scale Matching." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1999.](https://mlanthology.org/cvpr/1999/lew1999cvpr-optimal/) doi:10.1109/CVPR.1999.786922

BibTeX

@inproceedings{lew1999cvpr-optimal,
  title     = {{Optimal Multi-Scale Matching}},
  author    = {Lew, Michael S. and Huang, Thomas S.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1999},
  pages     = {1088-1093},
  doi       = {10.1109/CVPR.1999.786922},
  url       = {https://mlanthology.org/cvpr/1999/lew1999cvpr-optimal/}
}