@Article{AntonioHernandez2012,
author="Antonio Hernandez
and Nadezhda Zlateva
and Alexander Marinov
and Miguel Reyes
and Petia Radeva
and Dimo Dimov
and Sergio Escalera",
title="Human Limb Segmentation in Depth Maps based on Spatio-Temporal Graph Cuts Optimization",
journal="Journal of Ambient Intelligence and Smart Environments",
year="2012",
volume="4",
number="6",
pages="535--546",
optkeywords="Multi-modal vision processing",
optkeywords="Random Forest",
optkeywords="Graph-cuts",
optkeywords="multi-label segmentation",
optkeywords="human body segmentation",
abstract="We present a framework for object segmentation using depth maps based on Random Forest and Graph-cuts theory, and apply it to the segmentation of human limbs. 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 values 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=2006), last updated on Thu, 13 Mar 2014 13:22:43 +0100",
issn="1876-1364",
doi="10.3233/AIS-2012-0176",
file=":http://refbase.cvc.uab.es/files/HZM2012a.pdf:PDF"
}