@InProceedings{AntonioHernandez2012,
author="Antonio Hernandez
and Nadezhda Zlateva
and Alexander Marinov
and Miguel Reyes
and Petia Radeva
and Dimo Dimov
and Sergio Escalera",
title="Graph Cuts Optimization for Multi-Limb Human Segmentation in Depth Maps",
booktitle="25th IEEE Conference on Computer Vision and Pattern Recognition",
year="2012",
publisher="IEEE Xplore",
pages="726--732",
abstract="We present a generic framework for object segmentation using depth maps based on Random Forest and Graph-cuts theory, and apply it to the segmentation of human limbs in depth maps. First, from a set of random depth features, Random Forest is used to infer a set of label probabilities for each data sample. This vector of probabilities is used as unary term in $\alpha$-$\beta$ swap Graph-cuts algorithm. Moreover, depth of spatio-temporal neighboring data points are used as boundary potentials. Results on a new multi-label human depth data set show high performance in terms of segmentation overlapping of the novel methodology compared to classical approaches.",
optnote="MILAB;HuPBA",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2046), last updated on Thu, 10 Nov 2016 11:59:45 +0100",
isbn="978-1-4673-1226-4",
issn="1063-6919",
doi="10.1109/CVPR.2012.6247742",
file=":http://refbase.cvc.uab.es/files/HZM2012b.pdf:PDF"
}