Connected Operators on 3D Data for Human Body Analysis
Abstract
This paper presents a novel method for filtering and extraction of human body features from 3D data, either from multi-view images or range sensors. The proposed algorithm consists in processing the geodesic distances on a 3D surface representing the human body in order to find prominent maxima representing salient points of the human body. We introduce a 3D surface graph representation and filtering strategies to enhance robustness to noise and artifacts present in this kind of data. We conduct several experiments on different datasets involving 2 multi-view setups and 2 range data sensors: Kinect and Mesa SR4000. In all of them, the proposed algorithm shows a promising performance towards human body analysis with 3D data.
Cite
Text
Alcoverro et al. "Connected Operators on 3D Data for Human Body Analysis." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2011. doi:10.1109/CVPRW.2011.5981772Markdown
[Alcoverro et al. "Connected Operators on 3D Data for Human Body Analysis." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2011.](https://mlanthology.org/cvprw/2011/alcoverro2011cvprw-connected/) doi:10.1109/CVPRW.2011.5981772BibTeX
@inproceedings{alcoverro2011cvprw-connected,
title = {{Connected Operators on 3D Data for Human Body Analysis}},
author = {Alcoverro, Marcel and López-Mendez, Adolfo and Pardàs, Montse and Casas, Josep R.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2011},
pages = {9-14},
doi = {10.1109/CVPRW.2011.5981772},
url = {https://mlanthology.org/cvprw/2011/alcoverro2011cvprw-connected/}
}