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
Smoothness in Layers: Motion segmentation using nonparametric mixture estimation.
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Accurate Motion Flow Estimation with Discontinuities
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Point Matching under Large Image Deformations and Illumination Changes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simultaneous Two-View Epipolar Geometry Estimation and Motion Segmentation by 4D Tensor Voting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion segmentation with accurate boundaries: a tensor voting approach
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Video scene interpretation using perceptual prominence and mise-en-scène features
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
| Hi-index | 0.00 |
We present a novel approach for motion grouping from two frames, that recovers the dense velocity field, motion boundaries and regions, based on a 4-D Tensor Voting computational framework. Given two sparse sets of point tokens, we encode the image position and potential velocity for each token into a 4-D tensor. The voting process then enforces the motion smoothness while preserving motion discontinuities, thus selecting the correct velocity for each input point, as the most salient token. By performing an additional dense voting step we infer velocities at every pixel location, motion boundaries and regions. Using a 4-D 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 other methods that optimize a specific objective function, our approach does not involve initialization or search in a parametric space, 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 - opaque and transparent motion, rigid and non-rigid motion, curves and surfaces in motion.