Computing occluding and transparent motions
International Journal of Computer Vision
Region-based tracking using affine motion models in long image sequences
CVGIP: Image Understanding
Compact Representations of Videos Through Dominant and Multiple Motion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
International Journal of Computer Vision
Efficient Region Tracking With Parametric Models of Geometry and Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Stereo Matching with Transparency and Matting
International Journal of Computer Vision - 1998 Marr Prize
A Probabilistic Exclusion Principle for Tracking Multiple Objects
International Journal of Computer Vision
Empirical Bayesian Motion Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing planned multiperson action
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Probabilistic Data Association Methods for Tracking Complex Visual Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cooperative Robust Estimation Using Layers of Support
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-Based Object Tracking in Traffic Scenes
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Partitioned Sampling, Articulated Objects, and Interface-Quality Hand Tracking
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Layer Extraction with a Bayesian Model of Shapes
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Stochastic Tracking of 3D Human Figures Using 2D Image Motion
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Smoothness in Layers: Motion segmentation using nonparametric mixture estimation.
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Tracking People with Twists and Exponential Maps
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human-centered multimedia: representations and challenges
Proceedings of the 1st ACM international workshop on Human-centered multimedia
Video object segmentation by motion-based sequential feature clustering
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Unwrap mosaics: a new representation for video editing
ACM SIGGRAPH 2008 papers
Occlusion Boundaries from Motion: Low-Level Detection and Mid-Level Reasoning
International Journal of Computer Vision
Multiview segmentation and tracking of dynamic occluding layers
Image and Vision Computing
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Recognizing objects in adversarial clutter: breaking a visual captcha
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Detachable object detection with efficient model selection
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Automatic moving object segmentation with accurate boundaries
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Learning hierarchical shape models from examples
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Optimal Image and Video Closure by Superpixel Grouping
International Journal of Computer Vision
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We describe a 2.5D layered representation for visual motion analysis. The representation provides a global interpretation of image motion in terms of several spatially localized foreground regions along with a background region. Each of these regions comprises a parametric shape model and a parametric motion model. The representation also contains depth ordering so visibility and occlusion are rightly included in the estimation of the model parameters. Finally, because the number of objects, their positions, shapes and sizes, and their relative depths are all unknown, initial models are drawn from a proposal distribution, and then compared using a penalized likelihood criterion. This allows us to automatically initialize new models, and to compare different depth orderings.