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A near Optimal Acceptance-Rejection Algorithm for Exact Cross-Correlation Search

Haim Schweitzer, Robert Finis Anderson, Rui A. Deng
ICCV 2009 pp. 1089-1094
doi:10.1109/ICCV.2009.5459404 /iccv/2009/schweitzer2009iccv-near/

Abstract

We describe a fast algorithm that searches for the k most likely locations of a template in an image according to the standard normalized correlations criterion. The algorithm is exact; it always finds the best matches. Its speed is achieved by utilizing an acceptance-rejection pruning scheme, applied to easily computed bounds on the normalized correlation values. Previously proposed rejection schemes require a rejection threshold that has to be provided or estimated from the data. Our algorithm does not use such thresholds explicitly, but performs as well as if the perfect rejection threshold is known.

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Cite

Text

Schweitzer et al. "A near Optimal Acceptance-Rejection Algorithm for Exact Cross-Correlation Search." IEEE/CVF International Conference on Computer Vision, 2009. doi:10.1109/ICCV.2009.5459404

Markdown

[Schweitzer et al. "A near Optimal Acceptance-Rejection Algorithm for Exact Cross-Correlation Search." IEEE/CVF International Conference on Computer Vision, 2009.](https://mlanthology.org/iccv/2009/schweitzer2009iccv-near/) doi:10.1109/ICCV.2009.5459404

BibTeX

@inproceedings{schweitzer2009iccv-near,
  title     = {{A near Optimal Acceptance-Rejection Algorithm for Exact Cross-Correlation Search}},
  author    = {Schweitzer, Haim and Anderson, Robert Finis and Deng, Rui A.},
  booktitle = {IEEE/CVF International Conference on Computer Vision},
  year      = {2009},
  pages     = {1089-1094},
  doi       = {10.1109/ICCV.2009.5459404},
  url       = {https://mlanthology.org/iccv/2009/schweitzer2009iccv-near/}
}