IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Analysis Using Multigrid Relaxation Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
Investigations of multigrid algorithms for the estimation of optical flow fieldsin image sequences
Computer Vision, Graphics, and Image Processing
On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Computing optical flow across multiple scales: an adaptive coarse-to-fine strategy
International Journal of Computer Vision
Performance of optical flow techniques
International Journal of Computer Vision
Line search algorithms with guaranteed sufficient decrease
ACM Transactions on Mathematical Software (TOMS)
A Fast Scalable Algorithm for Discontinuous Optical Flow Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion Estimation with Quadtree Splines
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
A multigrid tutorial (2nd ed.)
A multigrid tutorial (2nd ed.)
Reliable Estimation of Dense Optical Flow Fields with Large Displacements
International Journal of Computer Vision
A Theoretical Framework for Convex Regularizers in PDE-Based Computation of Image Motion
International Journal of Computer Vision
Optimal Control Formulation for Determining Optical Flow
SIAM Journal on Scientific Computing
Efficient Implementation of the Truncated-Newton Algorithm for Large-Scale Chemistry Applications
SIAM Journal on Optimization
A Scale-Space Approach to Nonlocal Optical Flow Calculations
SCALE-SPACE '99 Proceedings of the Second International Conference on Scale-Space Theories in Computer Vision
A Multigrid Approach for Hierarchical Motion Estimation
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A Multigrid Approach for Hierarchical Motion Estimation
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Model Problems for the Multigrid Optimization of Systems Governed by Differential Equations
SIAM Journal on Scientific Computing
Highly Accurate Optic Flow Computation with Theoretically Justified Warping
International Journal of Computer Vision
Recursive Trust-Region Methods for Multiscale Nonlinear Optimization
SIAM Journal on Optimization
Constraints for the estimation of displacement vector fields from image sequences
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
A contrast invariant approach to motion estimation
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
Dense estimation and object-based segmentation of the optical flow with robust techniques
IEEE Transactions on Image Processing
Differentiation of discrete multidimensional signals
IEEE Transactions on Image Processing
Variational optical flow computation in real time
IEEE Transactions on Image Processing
Video segmentation algorithm based on modified Mumford-Shah functional
MACMESE'11 Proceedings of the 13th WSEAS international conference on Mathematical and computational methods in science and engineering
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We evaluate the performance of different optimization techniques developed in the context of optical flow computation with different variational models. In particular, based on truncated Newton (TN) methods that have been an effective approach for large-scale unconstrained optimization, we develop the use of efficient multilevel schemes for computing the optical flow. More precisely, we compare the performance of a standard unidirectional multilevel algorithm—called multiresolution optimization (MR/Opt)—with that of a bidirectional multilevel algorithm—called full multigrid optimization (FMG/Opt). The FMG/Opt algorithm treats the coarse grid correction as an optimization search direction and eventually scales it using a line search. Experimental results on three image sequences using four models of optical flow with different computational efforts show that the FMG/Opt algorithm outperforms both the TN and MR/Opt algorithms in terms of the computational work and the quality of the optical flow estimation.