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Distributed Computer Vision Algorithms Through Distributed Averaging

Roberto Tron, René Vidal
CVPR 2011 pp. 57-63
doi:10.1109/CVPR.2011.5995654 /cvpr/2011/tron2011cvpr-distributed/

Abstract

Traditional computer vision and machine learning algorithms have been largely studied in a centralized setting, where all the processing is performed at a single central location. However, a distributed approach might be more appropriate when a network with a large number of cameras is used to analyze a scene. In this paper we show how centralized algorithms based on linear algebraic operations can be made distributed by using simple distributed averages. We cover algorithms such as SVD, least squares, PCA, GPCA, 3-D point triangulation, pose estimation and affine SfM.

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Text

Tron and Vidal. "Distributed Computer Vision Algorithms Through Distributed Averaging." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2011. doi:10.1109/CVPR.2011.5995654

Markdown

[Tron and Vidal. "Distributed Computer Vision Algorithms Through Distributed Averaging." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2011.](https://mlanthology.org/cvpr/2011/tron2011cvpr-distributed/) doi:10.1109/CVPR.2011.5995654

BibTeX

@inproceedings{tron2011cvpr-distributed,
  title     = {{Distributed Computer Vision Algorithms Through Distributed Averaging}},
  author    = {Tron, Roberto and Vidal, René},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2011},
  pages     = {57-63},
  doi       = {10.1109/CVPR.2011.5995654},
  url       = {https://mlanthology.org/cvpr/2011/tron2011cvpr-distributed/}
}