%0 Conference Proceedings
%T Graph Cuts Optimization for Multi-Limb Human Segmentation in Depth Maps
%A Antonio Hernandez
%A Nadezhda Zlateva
%A Alexander Marinov
%A Miguel Reyes
%A Petia Radeva
%A Dimo Dimov
%A Sergio Escalera
%B 25th IEEE Conference on Computer Vision and Pattern Recognition
%D 2012
%I IEEE Xplore
%@ 1063-6919
%@ 978-1-4673-1226-4
%F Antonio Hernandez2012
%O MILAB;HuPBA
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2046), last updated on Thu, 10 Nov 2016 11:59:45 +0100
%X 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 α-β 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.
%U http://refbase.cvc.uab.es/files/HZM2012b.pdf
%U http://dx.doi.org/10.1109/CVPR.2012.6247742
%P 726-732