Probabilistic Object Recognition and Localization
Abstract
Objects can be represented by regions of local structure as well as dependencies between these regions. The appearance of local structure can be characterized by a vector of local features measured by local operators such as Gaussian derivatives or Gabor filters. This paper presents a technique in which the appearance of objects is represented by the joint statistics of local neighborhood operators. A probabilistic technique based on joint statistics is developed for the identification of multiple objects at arbitrary positions and orientations. Furthermore, by incorporating structural dependencies, a procedure for probabilistic localization of objects is obtained. The current recognition system runs at approximately 10 Hz on a Silicon 02. Experimental results are provided and an application using a head mounted camera is described.
Cite
Text
Schiele and Pentland. "Probabilistic Object Recognition and Localization." IEEE/CVF International Conference on Computer Vision, 1999. doi:10.1109/ICCV.1999.791215Markdown
[Schiele and Pentland. "Probabilistic Object Recognition and Localization." IEEE/CVF International Conference on Computer Vision, 1999.](https://mlanthology.org/iccv/1999/schiele1999iccv-probabilistic/) doi:10.1109/ICCV.1999.791215BibTeX
@inproceedings{schiele1999iccv-probabilistic,
title = {{Probabilistic Object Recognition and Localization}},
author = {Schiele, Bernt and Pentland, Alex},
booktitle = {IEEE/CVF International Conference on Computer Vision},
year = {1999},
pages = {177-182},
doi = {10.1109/ICCV.1999.791215},
url = {https://mlanthology.org/iccv/1999/schiele1999iccv-probabilistic/}
}