IEEE Transactions on Pattern Analysis and Machine Intelligence
Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Multidimensional Orientation Estimation with Applications to Texture Analysis and Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Estimation of Motion Vector Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computation of discontinuous optical flow by domain decomposition and shape optimization
International Journal of Computer Vision
Motion segmentation and qualitative dynamic scene analysis from an image sequence
International Journal of Computer Vision
A level set approach for computing solutions to incompressible two-phase flow
Journal of Computational Physics
Variational methods in image segmentation
Variational methods in image segmentation
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIAM Journal on Numerical Analysis
Direct incremental model-based image motion segmentation for video analysis
Signal Processing - Video segmentation for content-based processing manipulation
Image Sequence Analysis via Partial Differential Equations
Journal of Mathematical Imaging and Vision
Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Integrated Bayesian Approach to Layer Extraction from Image Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Theoretical Framework for Convex Regularizers in PDE-Based Computation of Image Motion
International Journal of Computer Vision
Multimodal Estimation of Discontinuous Optical Flow using Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Motion Competition: Variational Integration of Motion Segmentation and Shape Regularization
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Smoothness in Layers: Motion segmentation using nonparametric mixture estimation.
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Gradient flows and geometric active contour models
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Motion Segmentation and Tracking Using Normalized Cuts
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Variational Space-Time Motion Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Motion Competition: A Variational Approach to Piecewise Parametric Motion Segmentation
International Journal of Computer Vision
A multiphase level set framework for motion segmentation
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
A variational framework for image segmentation combining motion estimation and shape regularization
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Generalized principal component analysis (GPCA)
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Dense estimation and object-based segmentation of the optical flow with robust techniques
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Divide-and-conquer strategies for estimating multiple transparent motions
IWCM'04 Proceedings of the 1st international conference on Complex motion
Statistical methods for data mining and knowledge discovery
ICFCA'10 Proceedings of the 8th international conference on Formal Concept Analysis
Morphons: paint on priors and elastic canvas for segmentation and registration
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
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We present a variational approach for segmenting the image plane into regions of piecewise parametric motion given two or more frames from an image sequence. Our model is based on a conditional probability for the spatio-temporal image gradient, given a particular velocity model, and on a geometric prior on the estimated motion field favoring motion boundaries of minimal length. We cast the problem of motion segmentation as one of Bayesian inference, we derive a cost functional which depends on parametric motion models for each of a set of domains and on the boundary separating them. The resulting functional can be interpreted as an extension of the Mumford-Shah functional from intensity segmentation to motion segmentation. In contrast to most alternative approaches, the problems of segmentation and motion estimation are jointly solved by continuous minimization of a single functional. Minimization results in an eigen value problem for the motion parameters and in a gradient descent evolution for the motion boundary. The evolution of the motion boundaries is implemented by a multiphase level set formulation which allows for the segmentation of an arbitrary number of multiply connected moving objects. We further extend this approach to the segmentation of space-time volumes of coherent motion from video sequences. To this end, motion boundaries are represented by a set of surfaces in space-time. An implementation by a higher-dimensional multiphase level set model allows the evolving surfaces to undergo topological changes. In contrast to an iterative segmentation of consecutive frame pairs, a constraint on the area of these surfaces leads to an additional temporal regularization of the computed motion boundaries. Numerical results demonstrate the capacity of our approach to segment objects based exclusively on their relative motion.