Learning and Evaluating Visual Features for Pose Estimation
Abstract
We present a method for learning a set of visual landmarks which are useful for pose estimation. The landmark learning mechanism is designed to be applicable to a wide range of environments, and generalized for different approaches to computing a pose estimate. Initially, each landmark is detected as a focal extremum of a measure of distinctiveness and represented by a principal components encoding which is exploited for matching. Attributes of the observed landmarks can be parameterized using a generic parameterization method and then evaluated in terms of their utility for pose estimation. We present experimental evidence that demonstrates the utility of the method.
Cite
Text
Sim and Dudek. "Learning and Evaluating Visual Features for Pose Estimation." IEEE/CVF International Conference on Computer Vision, 1999. doi:10.1109/ICCV.1999.790419Markdown
[Sim and Dudek. "Learning and Evaluating Visual Features for Pose Estimation." IEEE/CVF International Conference on Computer Vision, 1999.](https://mlanthology.org/iccv/1999/sim1999iccv-learning/) doi:10.1109/ICCV.1999.790419BibTeX
@inproceedings{sim1999iccv-learning,
title = {{Learning and Evaluating Visual Features for Pose Estimation}},
author = {Sim, Robert and Dudek, Gregory},
booktitle = {IEEE/CVF International Conference on Computer Vision},
year = {1999},
pages = {1217-1222},
doi = {10.1109/ICCV.1999.790419},
url = {https://mlanthology.org/iccv/1999/sim1999iccv-learning/}
}