ML Anthology

Adaptive Contour Features in Oriented Granular Space for Human Detection and Segmentation

Wei Gao, Haizhou Ai, Shihong Lao
CVPR 2009 pp. 1786-1793
doi:10.1109/CVPR.2009.5206762 /cvpr/2009/gao2009cvpr-adaptive/

Abstract

In this paper, a novel feature named adaptive contour feature (ACF) is proposed for human detection and segmentation. This feature consists of a chain of a number of granules in oriented granular space (OGS) that is learnt via the AdaBoost algorithm. Three operations are defined on the OGS to mine object contour feature and feature co-occurrences automatically. A heuristic learning algorithm is proposed to generate an ACF that at the same time define a weak classifier for human detection or segmentation. Experiments on two open datasets show that the ACF outperform several well-known existing features due to its stronger discriminative power rooted in the nature of its flexibility and adaptability to describe an object contour element.

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Text

Gao et al. "Adaptive Contour Features in Oriented Granular Space for Human Detection and Segmentation." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2009. doi:10.1109/CVPR.2009.5206762

Markdown

[Gao et al. "Adaptive Contour Features in Oriented Granular Space for Human Detection and Segmentation." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2009.](https://mlanthology.org/cvpr/2009/gao2009cvpr-adaptive/) doi:10.1109/CVPR.2009.5206762

BibTeX

@inproceedings{gao2009cvpr-adaptive,
  title     = {{Adaptive Contour Features in Oriented Granular Space for Human Detection and Segmentation}},
  author    = {Gao, Wei and Ai, Haizhou and Lao, Shihong},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2009},
  pages     = {1786-1793},
  doi       = {10.1109/CVPR.2009.5206762},
  url       = {https://mlanthology.org/cvpr/2009/gao2009cvpr-adaptive/}
}