ML Anthology

Regression from Local Features for Viewpoint and Pose Estimation

Marwan Torki, Ahmed M. Elgammal
ICCV 2011 pp. 2603-2610
doi:10.1109/ICCV.2011.6126549 /iccv/2011/torki2011iccv-regression/

Abstract

In this paper we propose a framework for learning a regression function form a set of local features in an image. The regression is learned from an embedded representation that reflects the local features and their spatial arrangement as well as enforces supervised manifold constraints on the data. We applied the approach for viewpoint estimation on a Multiview car dataset, a head pose dataset and arm posture dataset. The experimental results show that this approach has superior results (up to 67% improvement) to the state-of-the-art approaches in very challenging datasets.

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Text

Torki and Elgammal. "Regression from Local Features for Viewpoint and Pose Estimation." IEEE/CVF International Conference on Computer Vision, 2011. doi:10.1109/ICCV.2011.6126549

Markdown

[Torki and Elgammal. "Regression from Local Features for Viewpoint and Pose Estimation." IEEE/CVF International Conference on Computer Vision, 2011.](https://mlanthology.org/iccv/2011/torki2011iccv-regression/) doi:10.1109/ICCV.2011.6126549

BibTeX

@inproceedings{torki2011iccv-regression,
  title     = {{Regression from Local Features for Viewpoint and Pose Estimation}},
  author    = {Torki, Marwan and Elgammal, Ahmed M.},
  booktitle = {IEEE/CVF International Conference on Computer Vision},
  year      = {2011},
  pages     = {2603-2610},
  doi       = {10.1109/ICCV.2011.6126549},
  url       = {https://mlanthology.org/iccv/2011/torki2011iccv-regression/}
}