A Probabilistic Model for Object Recognition, Segmentation, and Non-Rigid Correspondence

Simon, Ian; Seitz, Steven M.

doi:10.1109/CVPR.2007.383015

A Probabilistic Model for Object Recognition, Segmentation, and Non-Rigid Correspondence

Ian Simon, Steven M. Seitz

CVPR 2007

doi:10.1109/CVPR.2007.383015 /cvpr/2007/simon2007cvpr-probabilistic/

Abstract

We describe a method for fully automatic object recognition and segmentation using a set of reference images to specify the appearance of each object. Our method uses a generative model of image formation that takes into account occlusions, simple lighting changes, and object deformations. We take advantage of local features to identify, locate, and extract multiple objects in the presence of large viewpoint changes, nonrigid motions with large numbers of degrees of freedom, occlusions, and clutter. We simultaneously compute an object-level segmentation and a dense correspondence between the pixels of the appropriate reference images and the image to be segmented.

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Cite

Text

Simon and Seitz. "A Probabilistic Model for Object Recognition, Segmentation, and Non-Rigid Correspondence." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007. doi:10.1109/CVPR.2007.383015

Markdown

[Simon and Seitz. "A Probabilistic Model for Object Recognition, Segmentation, and Non-Rigid Correspondence." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007.](https://mlanthology.org/cvpr/2007/simon2007cvpr-probabilistic/) doi:10.1109/CVPR.2007.383015

BibTeX

@inproceedings{simon2007cvpr-probabilistic,
  title     = {{A Probabilistic Model for Object Recognition, Segmentation, and Non-Rigid Correspondence}},
  author    = {Simon, Ian and Seitz, Steven M.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2007},
  doi       = {10.1109/CVPR.2007.383015},
  url       = {https://mlanthology.org/cvpr/2007/simon2007cvpr-probabilistic/}
}