SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
ACM SIGGRAPH 2004 Papers
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bi-Layer Segmentation of Binocular Stereo Video
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
ACM SIGGRAPH 2005 Papers
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Bilayer Segmentation of Live Video
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Automatic Natural Video Matting with Depth
PG '07 Proceedings of the 15th Pacific Conference on Computer Graphics and Applications
Robust Real-Time Bi-Layer Video Segmentation Using Infrared Video
CRV '08 Proceedings of the 2008 Canadian Conference on Computer and Robot Vision
Live Video Segmentation in Dynamic Backgrounds Using Thermal Vision
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Color image segmentation based on an iterative graph cut algorithm using time-of-flight cameras
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
Journal of Visual Communication and Image Representation
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This paper introduces an approach for automatic foreground extraction from videos utilizing depth information from time of flight(ToF) cameras.We give a clear definition of background and foreground based on 3D scene geometry and provide means of foreground extraction based on one-dimensional histograms in 3D space. Further a refinement step based on hierarchical grab-cut segmentation in a video volume with incorporated time constraints is proposed. Our approach is able to extract timeconsistent foreground objects even for a moving camera and for dynamic scene content, but is limited to indoor scenarios.