PT Unknown
AU Antonio Hernandez
   Nadezhda Zlateva
   Alexander Marinov
   Miguel Reyes
   Petia Radeva
   Dimo Dimov
   Sergio Escalera
TI Graph Cuts Optimization for Multi-Limb Human Segmentation in Depth Maps
BT 25th IEEE Conference on Computer Vision and Pattern Recognition
PY 2012
BP 726
EP 732
DI 10.1109/CVPR.2012.6247742
AB 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.
ER