IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance of optical flow techniques
International Journal of Computer Vision
A Fast Scalable Algorithm for Discontinuous Optical Flow Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Journal of the ACM (JACM)
Computational Framework for Segmentation and Grouping
Computational Framework for Segmentation and Grouping
Multimodal Estimation of Discontinuous Optical Flow using Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Perceptual Grouping from Motion Cues Using Tensor Voting in 4-D
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Smoothness in Layers: Motion segmentation using nonparametric mixture estimation.
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Motion Segmentation and Tracking Using Normalized Cuts
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A Voting-Based Computational Framework for Visual Motion Analysis and Interpretation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Disambiguating Visual Motion by Form-Motion Interaction--a Computational Model
International Journal of Computer Vision
Optimal Camera Placement for Automated Surveillance Tasks
Journal of Intelligent and Robotic Systems
Figure-ground separation by cue integration
Neural Computation
Curvature Estimation and Curve Inference with Tensor Voting: A New Approach
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Automatic moving object segmentation with accurate boundaries
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
An improved representation of junctions through asymmetric tensor diffusion
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
Improving the robustness of variational optical flow through tensor voting
Computer Vision and Image Understanding
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Producing an accurate motion flow field is very difficult at motion boundaries. We present a novel, noniterative approach for segmentation from image motion, based on two voting processes, in different dimensional spaces. By expressing the motion layers as surfaces in a 4-D space, a voting process is first used to enforce the smoothness of motion and determine an estimation of pixel velocities, motion regions and boundaries. The boundary estimation is then combined with intensity information from the original images in order to locally define a boundary tensor field. The correct boundary is inferred by a 2-D voting process within this field, that enforces the smoothness of boundaries. Finally, correct velocities are computed for the pixels near boundaries, as they are reassigned to different regions. We demonstrate our contribution by analyzing several image sequences, containing multiple types of motion.