Graph cuts optimization for multi-limb human segmentation in depth maps

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 s...

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Bibliographic Details
Published in2012 IEEE Conference on Computer Vision and Pattern Recognition pp. 726 - 732
Main Authors Hernandez-Vela, A., Zlateva, N., Marinov, A., Reyes, M., Radeva, P., Dimov, D., Escalera, S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2012
Subjects
Humans
Image segmentation
Joints
Radio frequency
Training
Vectors
Vegetation
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Summary: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.
ISBN:9781467312264
1467312266
ISSN:1063-6919
DOI:10.1109/CVPR.2012.6247742