IEEE Transactions on Pattern Analysis and Machine Intelligence
Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
International Journal of Computer Vision
Motion segmentation and qualitative dynamic scene analysis from an image sequence
International Journal of Computer Vision
Performance of optical flow techniques
International Journal of Computer Vision
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
International Journal of Computer Vision
Reliable Estimation of Dense Optical Flow Fields with Large Displacements
International Journal of Computer Vision
Hierarchical Estimation and Segmentation of Dense Motion Fields
International Journal of Computer Vision
A Multigrid Approach for Hierarchical Motion Estimation
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A Multigrid Approach for Hierarchical Motion Estimation
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods
International Journal of Computer Vision
Motion Competition: A Variational Approach to Piecewise Parametric Motion Segmentation
International Journal of Computer Vision
Towards Ultimate Motion Estimation: Combining Highest Accuracy with Real-Time Performance
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Highly Accurate Optic Flow Computation with Theoretically Justified Warping
International Journal of Computer Vision
Geodesic active regions and level set methods for motion estimation and tracking
Computer Vision and Image Understanding
IEEE Transactions on Image Processing
International Journal of Computer Vision
Coarse to over-fine optical flow estimation
Pattern Recognition
Over-Parameterized Variational Optical Flow
International Journal of Computer Vision
Multi-scale 3D scene flow from binocular stereo sequences
Computer Vision and Image Understanding
A Variational Technique for Time Consistent Tracking of Curves and Motion
Journal of Mathematical Imaging and Vision
A Segmentation Based Variational Model for Accurate Optical Flow Estimation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Dynamic Texture Detection Based on Motion Analysis
International Journal of Computer Vision
A Geometric Framework and a New Criterion in Optical Flow Modeling
Journal of Mathematical Imaging and Vision
Object motion detection using information theoretic spatio-temporal saliency
Pattern Recognition
Model-based plane-segmentation using optical flow and dominant plane
MIRAGE'07 Proceedings of the 3rd international conference on Computer vision/computer graphics collaboration techniques
Detecting regions of dynamic texture
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Interactive motion segmentation
Proceedings of the 32nd DAGM conference on Pattern recognition
Near real-time motion segmentation using graph cuts
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
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We suggest a variational method for the joint estimation of optic flow and the segmentation of the image into regions of similar motion. It makes use of the level set framework following the idea of motion competition, which is extended to non-parametric motion. Moreover, we automatically determine an appropriate initialization and the number of regions by means of recursive two-phase splits with higher order region models. The method is further extended to the spatiotemporal setting and the use of additional cues like the gray value or color for the segmentation. It need not fear a quantitative comparison to pure optic flow estimation techniques: For the popular Yosemite sequence with clouds we obtain the currently most accurate result. We further uncover a mistake in the ground truth. Coarsely correcting this, we get an average angular error below 1 degree.