ML Anthology

Deformable Shape Detection and Description via Model-Based Region

Lifeng Liu, Stan Sclaroff
CVPR 1999 pp. 2021-2027
doi:10.1109/CVPR.1999.784603 /cvpr/1999/liu1999cvpr-deformable/

Abstract

A method for deformable shape detection and recognition is described. Deformable shape templates are used to partition the image into a globally consistent interpretation, determined in part by the minimum description length principle. Statistical shape models enforce the prior probabilities on global, parametric deformations for each object class. Once trained, the system autonomously segments deformed shapes from the background, while not merging them with adjacent objects or shadows. The formulation can be used to group image regions based on any image homogeneity predicate; e.g., texture, color or motion. The recovered shape models can be used directly in object recognition. Experiments with color imagery are reported.

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Cite

Text

Liu and Sclaroff. "Deformable Shape Detection and Description via Model-Based Region." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1999. doi:10.1109/CVPR.1999.784603

Markdown

[Liu and Sclaroff. "Deformable Shape Detection and Description via Model-Based Region." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1999.](https://mlanthology.org/cvpr/1999/liu1999cvpr-deformable/) doi:10.1109/CVPR.1999.784603

BibTeX

@inproceedings{liu1999cvpr-deformable,
  title     = {{Deformable Shape Detection and Description via Model-Based Region}},
  author    = {Liu, Lifeng and Sclaroff, Stan},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1999},
  pages     = {2021-2027},
  doi       = {10.1109/CVPR.1999.784603},
  url       = {https://mlanthology.org/cvpr/1999/liu1999cvpr-deformable/}
}