IEEE Transactions on Pattern Analysis and Machine Intelligence
A Three-Frame Algorithm for Estimating Two-Component Image Motion
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
Inference of Surfaces, 3D Curves, and Junctions from Sparse, Noisy, 3D Data
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
Optical-Flow Estimation while Preserving Its Discontinuities: A Variational Approach
ACCV '95 Invited Session Papers from the Second Asian Conference on Computer Vision: Recent Developments in Computer Vision
A region-level graph labeling approach to motion-based segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Smoothness in Layers: Motion segmentation using nonparametric mixture estimation.
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Markov Random Fields with Efficient Approximations
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Motion Feature Detection Using Steerable Flow Fields
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Accurate Motion Flow Estimation with Discontinuities
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Optical Flow Estimation Using Wavelet Motion Model
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Motion Segmentation and Tracking Using Normalized Cuts
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
IEEE Transactions on Image Processing
First Order Augmentation to Tensor Voting for Boundary Inference and Multiscale Analysis in 3D
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Voting-Based Computational Framework for Visual Motion Analysis and Interpretation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Disambiguating Visual Motion Through Contextual Feedback Modulation
Neural Computation
A Fast Biologically Inspired Algorithm for Recurrent Motion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Iterated tensor voting and curvature improvement
Signal Processing
Variational Multi-Valued Velocity Field Estimation for Transparent Sequences
Journal of Mathematical Imaging and Vision
2D motion description and contextual motion analysis: issues and new models
SCVMA'04 Proceedings of the First international conference on Spatial Coherence for Visual Motion Analysis
Analysis and interpretation of multiple motions through surface saliency
SCVMA'04 Proceedings of the First international conference on Spatial Coherence for Visual Motion Analysis
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We address the problem of perceptual grouping from motion cues by formulating it as a motion layers inference from a sparse and noisy point set in a 4D space. Our approach is based on a layered 4D representation of data, and a voting scheme for token communication, within a tensor voting computational framework. Given two sparse sets of point tokens, the image position and potential velocity of each token are encoded into a 4D tensor. By enforcing the smoothness of motion through a voting process, the correct velocity is selected for each input point as the most salient token. An additional dense voting step allows for the inference of a dense representation in terms of pixel velocities, motion regions, and boundaries. Using a 4D space for this tensor voting approach is essential since it allows for a spatial separation of the points according to both their velocities and image coordinates. Unlike most other methods that optimize certain objective functions, our approach is noniterative and, therefore, does not suffer from local optima or poor convergence problems. We demonstrate our method with synthetic and real images, by analyzing several difficult cases驴opaque and transparent motion, rigid and nonrigid motion, curves and surfaces in motion.