ML Anthology

Unbounded Sparse Census Transform Using Genetic Algorithm

Carl Ahlberg, Miguel León Ortiz, Fredrik Ekstrand, Mikael Ekström
WACV 2019 pp. 1616-1625
doi:10.1109/WACV.2019.00177 /wacv/2019/ahlberg2019wacv-unbounded/

Abstract

The Census Transform (CT) is a well proven method for stereo vision that provides robust matching, with respect to object boundaries, outliers and radiometric distortion, at a low computational cost. Recent CT methods propose patterns for pixel comparison and sparsity, to increase matching accuracy and reduce resource requirements. However, these methods are bounded with respect to symmetry and/or edge length. In this paper, a Genetic algorithm (GA) is applied to find a new and powerful CT method. The proposed method, Genetic Algorithm Census Transform (GACT), is compared with the established CT methods, showing better results for benchmarking datasets. Additional experiments have been performed to study the search space and the correlation between training and evaluation data.

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Cite

Text

Ahlberg et al. "Unbounded Sparse Census Transform Using Genetic Algorithm." IEEE/CVF Winter Conference on Applications of Computer Vision, 2019. doi:10.1109/WACV.2019.00177

Markdown

[Ahlberg et al. "Unbounded Sparse Census Transform Using Genetic Algorithm." IEEE/CVF Winter Conference on Applications of Computer Vision, 2019.](https://mlanthology.org/wacv/2019/ahlberg2019wacv-unbounded/) doi:10.1109/WACV.2019.00177

BibTeX

@inproceedings{ahlberg2019wacv-unbounded,
  title     = {{Unbounded Sparse Census Transform Using Genetic Algorithm}},
  author    = {Ahlberg, Carl and Ortiz, Miguel León and Ekstrand, Fredrik and Ekström, Mikael},
  booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
  year      = {2019},
  pages     = {1616-1625},
  doi       = {10.1109/WACV.2019.00177},
  url       = {https://mlanthology.org/wacv/2019/ahlberg2019wacv-unbounded/}
}