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Hybrid Evolutionary Ridge Regression Approach for High-Accurate Corner Extraction

Gustavo Olague, Benjamín Hernández, Enrique Dunn
CVPR 2003 pp. 744-749
doi:10.1109/CVPR.2003.1211427 /cvpr/2003/olague2003cvpr-hybrid/

Abstract

Corner measurement is of main concern within the following tasks: camera calibration, image matching, object tracking, recognition and reconstruction. This paper presents a hybrid evolutionary ridge regression approach for the problem of corner modeling. We search model parameters characterizing L-corner models by means of fitting the model to the image data. As the model fitting relies on an initial parameter estimation, we use a global approach to find the global minimum. Experimental results applied to an L-corner using several levels of noise show the advantages and disadvantages of our evolutionary algorithm compared to down-hill simplex and simulated annealing.

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Cite

Text

Olague et al. "Hybrid Evolutionary Ridge Regression Approach for High-Accurate Corner Extraction." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2003. doi:10.1109/CVPR.2003.1211427

Markdown

[Olague et al. "Hybrid Evolutionary Ridge Regression Approach for High-Accurate Corner Extraction." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2003.](https://mlanthology.org/cvpr/2003/olague2003cvpr-hybrid/) doi:10.1109/CVPR.2003.1211427

BibTeX

@inproceedings{olague2003cvpr-hybrid,
  title     = {{Hybrid Evolutionary Ridge Regression Approach for High-Accurate Corner Extraction}},
  author    = {Olague, Gustavo and Hernández, Benjamín and Dunn, Enrique},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2003},
  pages     = {744-749},
  doi       = {10.1109/CVPR.2003.1211427},
  url       = {https://mlanthology.org/cvpr/2003/olague2003cvpr-hybrid/}
}