Skin Color-Based Video Segmentation under Time-Varying Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-View Stereo via Volumetric Graph-Cuts
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Moving Cast Shadow Detection from a Gaussian Mixture Shadow Model
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Identifying foreground from multiple images
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
A bayesian approach to image-based visual hull reconstruction
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Robust variational segmentation of 3d objects from multiple views
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Foreground regions extraction and characterization towards real-time object tracking
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
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PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
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
N-tuple color segmentation for multi-view silhouette extraction
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
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We present a novel approach for adaptive foreground/background segmentation in non-static environments using multiview silhouette fusion. Our focus is on coping with moving objects in the background and influences of lighting conditions. It is shown, that by integrating 3d scene information, background motion can be compensated to achieve a better segmentation and a less error prone 3d reconstruction of the foreground. The proposed algorithm is based on a closed loop idea of segmentation and 3d reconstruction in form of a low level vision feedback system. The functionality of our approach is evaluated on two different data sets in this paper and the benefits of our algorithm are finally shown based on a quantitative error analysis.