IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Variational methods in image segmentation
Variational methods in image segmentation
Computational analysis of visual motion
Computational analysis of visual motion
3-D motion estimation from motion field
Artificial Intelligence - Special volume on computer vision
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
SIAM Review
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
Geometric Level Set Methods in Imaging,Vision,and Graphics
Geometric Level Set Methods in Imaging,Vision,and Graphics
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
Computer Vision and Image Understanding
On convergence of the Horn and Schunck optical-flow estimation method
IEEE Transactions on Image Processing
Concurrent 3-D motion segmentation and 3-D interpretation of temporal sequences of monocular images
IEEE Transactions on Image Processing
High-speed target tracking by fuzzy hostility-induced segmentation of optical flow field
Applied Soft Computing
A Statistical Overlap Prior for Variational Image Segmentation
International Journal of Computer Vision
STARS: Sign tracking and recognition system using input-output HMMs
Pattern Recognition Letters
Joint Tracking of Cell Morphology and Motion
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
Video segmentation based on motion coherence of particles in a video sequence
IEEE Transactions on Image Processing
International Journal of Applied Mathematics and Computer Science
3D Video Based Segmentation and Motion Estimation with Active Surface Evolution
Journal of Signal Processing Systems
Hi-index | 0.14 |
This study investigates a variational, active curve evolution method for dense three-dimentional (3D) segmentation and interpretation of optical flow in an image sequence of a scene containing moving rigid objects viewed by a possibly moving camera. This method jointly performs 3D motion segmentation, 3D interpretation (recovery of 3D structure and motion), and optical flow estimation. The objective functional contains two data terms for each segmentation region, one based on the motion-only equation which relates the essential parameters of 3D rigid body motion to optical flow, and the other on the Horn and Schunck optical flow constraint. It also contains two regularization terms for each region, one for optical flow, the other for the region boundary. The necessary conditions for a minimum of the functional result in concurrent 3D-motion segmentation, by active curve evolution via level sets, and linear estimation of each region essential parameters and optical flow. Subsequently, the screw of 3D motion and regularized relative depth are recovered analytically for each region from the estimated essential parameters and optical flow. Examples are provided which verify the method and its implementation.