Multibody Grouping from Motion Images
International Journal of Computer Vision
Multi-Frame Correspondence Estimation Using Subspace Constraints
International Journal of Computer Vision
Factorization with Uncertainty
International Journal of Computer Vision
Detecting and Tracking Multiple Moving Objects Using Temporal Integration
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Concerning Bayesian Motion Segmentation, Model, Averaging, Matching and the Trifocal Tensor
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
A multi-body factorization method for motion analysis
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
A Unified Factorization Algorithm for Points, Line Segments and Planes with Uncertainty Models
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Learning to Look at Humans -- What Are the Parts of a Moving Body?
AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
Attention-from-motion: A factorization approach for detecting attention objects in motion
Computer Vision and Image Understanding
Analysis of Rain and Snow in Frequency Space
International Journal of Computer Vision
3D motion segmentation using intensity trajectory
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Towards unsupervised segmentation of semi-rigid low-resolution molecular surfaces
GMP'06 Proceedings of the 4th international conference on Geometric Modeling and Processing
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
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Dynamic analysis of video sequences often relies on the segmentation of the sequence into regions of consistent motions. Approaching this problem requires a definition of which motions are regarded as consistent. Common approaches to motion segmentation usually group together points or image regions that have the same motion between successive frames (where the same motion can be 2D, 3D, or non-rigid). In this paper we define a new type of motion consistency, which is based on temporal consistency of behaviors across multiple frames in the video sequence. Our definition of consistent "temporal behavior" is expressed in terms of multi-frame linear subspace constraints. This definition applies to 2D, 3D, and some non-rigid motions without requiring prior model selection. We further show that our definition of motion consistency extends to data with directional uncertainty, thus leading to a dense segmentation of the entire image. Such segmentation is obtained by applying the new motion consistency constraints directly to covariance-weighted image brightness measurements. This is done without requiring prior correspondence estimation nor feature tracking.