Schematic storyboarding for video visualization and editing
ACM SIGGRAPH 2006 Papers
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Learning Layered Motion Segmentations of Video
International Journal of Computer Vision
Incremental discovery of object parts in video sequences
Computer Vision and Image Understanding
Efficient MRF deformation model for non-rigid image matching
Computer Vision and Image Understanding
Occlusion Boundaries from Motion: Low-Level Detection and Mid-Level Reasoning
International Journal of Computer Vision
Local detection of occlusion boundaries in video
Image and Vision Computing
Foreground Segmentation via Segments Tracking
ICCVG 2008 Proceedings of the International Conference on Computer Vision and Graphics: Revised Papers
International Journal of Computer Vision
RBF based spatio-temporal representation technique for video compression
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Upper Body Detection and Tracking in Extended Signing Sequences
International Journal of Computer Vision
Bilayer segmentation augmented with future evidence
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part II
SuperFloxels: a mid-level representation for video sequences
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Hierarchical object discovery and dense modelling from motion cues in RGB-D video
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
| Hi-index | 0.00 |
We present an unsupervised approach for learning a generative layered representation of a scene from a video for motion segmentation. The learnt model is a composition of layers, which consist of one or more segments. Included in the model are the effects of image projection, lighting, and motion blur. The two main contributions of our method are: (i) A novel algorithm for obtaining the initial estimate of the model using efficient loopy belief propagation; (ii) Using 驴β-swap and 驴-expansion algorithms, which guarantee a strong local minima, for refining the initial estimate. Results are presented on several classes of objects with different types of camera motion. We compare our method with the state of the art and demonstrate significant improvements.