Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGGRAPH 2004 Papers
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Interactive Graph Cut Based Segmentation with Shape Priors
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Graph Cut Based Multiple View Segmentation for 3D Reconstruction
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
On using silhouettes for camera calibration
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Adaptive Foreground/Background Segmentation Using Multiview Silhouette Fusion
Proceedings of the 31st DAGM Symposium on Pattern Recognition
Automatic 3D object segmentation in multiple views using volumetric graph-cuts
Image and Vision Computing
N-view human silhouette segmentation in cluttered, partially changing environments
Proceedings of the 32nd DAGM conference on Pattern recognition
Spatio-temporal optimization for foreground/background segmentation
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume Part I
Shape from Silhouette Consensus
Pattern Recognition
iModel: interactive co-segmentation for object of interest 3d modeling
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part II
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In this paper, we present a novel foreground extraction method that automatically identifies image regions corresponding to a common space region seen from multiple cameras. We assume that background regions present some color coherence in each image and we exploit the spatial consistency constraint that several image projections of the same space region must satisfy. Integrating both color and spatial consistency constraints allows to fully automatically segment foreground and background regions in multiple images. In contrast to standard background subtraction approaches, the proposed approach does not require any a priori knowledge on the background nor user interactions. We demonstrate the effectiveness of the method for multiple camera setups with experimental results on standard real data sets.