Visual hull construction, alignment and refinement for human kinematic modeling, motion tracking and rendering
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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
Adaptive Foreground/Background Segmentation Using Multiview Silhouette Fusion
Proceedings of the 31st DAGM Symposium on Pattern Recognition
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
A system for marker-less human motion estimation
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
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We introduce a procedure for calibrated multi camera setups in which observed persons within a realistic and, thus, difficult surrounding are determined as foreground in image sequences via a fully automatic purely data driven segmentation. In order to gain an optimal separation of fore- and background for each frame in terms of Expectation Maximization (EM), an algorithm is proposed which utilizes a combination of geometrical constraints of the scene and, additionally, temporal constraints for a optimization over the entire sequence to estimate the background. This background information is then used to determine accurate silhouettes of the foreground. We demonstrate the effectiveness of our approach based on a qualitative data analysis and compare it to other state of the art approaches.