Probabilistic Occlusion Boundary Detection on Spatio-Temporal Lattices
Abstract
In this paper, we present an algorithm for occlusion boundary detection. The main contribution is a probabilistic detection framework defined on spatio-temporal lattices, which enables joint analysis of image frames. For this purpose, we introduce two complementary cost functions for creating the spatio-temporal lattice and for performing global inference of the occlusion boundaries, respectively. In addition, a novel combination of low-level occlusion features is discriminatively learnt in the detection framework. Simulations on the CMU Motion Dataset provide ample evidence that proposed algorithm outperforms the leading existing methods.
Cite
Text
Sargin et al. "Probabilistic Occlusion Boundary Detection on Spatio-Temporal Lattices." IEEE/CVF International Conference on Computer Vision, 2009. doi:10.1109/ICCV.2009.5459190Markdown
[Sargin et al. "Probabilistic Occlusion Boundary Detection on Spatio-Temporal Lattices." IEEE/CVF International Conference on Computer Vision, 2009.](https://mlanthology.org/iccv/2009/sargin2009iccv-probabilistic/) doi:10.1109/ICCV.2009.5459190BibTeX
@inproceedings{sargin2009iccv-probabilistic,
title = {{Probabilistic Occlusion Boundary Detection on Spatio-Temporal Lattices}},
author = {Sargin, Mehmet Emre and Bertelli, Luca and Manjunath, Bangalore S. and Rose, Kenneth},
booktitle = {IEEE/CVF International Conference on Computer Vision},
year = {2009},
pages = {560-567},
doi = {10.1109/ICCV.2009.5459190},
url = {https://mlanthology.org/iccv/2009/sargin2009iccv-probabilistic/}
}