Subspace methods for recovering rigid motion I: algorithm and implementation
International Journal of Computer Vision
Direct Recovery of Motion and Shape in the General Case by Fixation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Computational analysis of visual motion
Computational analysis of visual motion
3-D motion estimation from motion field
Artificial Intelligence - Special volume on computer vision
Segmentation and Factorization-Based Motion and Structure Estimation for Long Image Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dense structure from a dense optical flow sequence
Computer Vision and Image Understanding
A Kalman Filter Approach to Direct Depth Estimation Incorporating Surface Structure
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computing optical flow via variational techniques
SIAM Journal on Applied Mathematics
Extracting Structure from Optical Flow Using the Fast Error Search Technique
International Journal of Computer Vision
Structure from Motion: Beyond the Epipolar Constraint
International Journal of Computer Vision
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Computer Methods for Mathematical Computations
Computer Methods for Mathematical Computations
Rigid Body Segmentation and Shape Description from Dense Optical Flow Under Weak Perspective
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Hierarchical Approach for Obtaining Structure from Two-Frame Optical Flow
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
A Variational Framework for Active and Adaptative Segmentation of Vector Valued Images
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
What Can Projections of Flow Fields Tell Us About the Visual Motion
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Dense 3D Interpretation of Image Sequences: A Variational Approach Using Anisotropic Diffusion
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Dense 3D Interpretation of Image Sequences: A Variational Approach Using Anisotropic Diffusion
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Motion Competition: A Variational Approach to Piecewise Parametric Motion Segmentation
International Journal of Computer Vision
A projection method to generate anaglyph stereo images
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Concurrent 3-D motion segmentation and 3-D interpretation of temporal sequences of monocular images
IEEE Transactions on Image Processing
Over-Parameterized Variational Optical Flow
International Journal of Computer Vision
Fast Saliency-Based Motion Segmentation Algorithm for an Active Vision System
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Computer Vision and Image Understanding
Joint Tracking of Cell Morphology and Motion
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
Taxonomy of directing semantics for film shot classification
IEEE Transactions on Circuits and Systems for Video Technology
Video segmentation based on motion coherence of particles in a video sequence
IEEE Transactions on Image Processing
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This paper describes a variational method with active curve evolution and level sets for the estimation, segmentation, and 3D interpretation of optical flow generated by independently moving rigid objects in space. Estimation, segmentation, and 3D interpretation are performed jointly. Segmentation is based on an estimate of optical flow consistent with a single rigid motion in each segmentation region. The method, which allows both viewing system and viewed objects to move, results in three steps iterated until convergence: (a) evolution of closed curves via level sets and, in each region of the segmentation, (b) linear least squares computation of the essential parameters of rigid motion, (c) estimation of optical flow consistent with a single rigid motion. The translational and rotational components of rigid motion and regularized relative depth are recovered analytically for each region of the segmentation from the estimated essential parameters and optical flow. Several examples with real image sequences are provided which verify the validity of the method.