IEEE Transactions on Pattern Analysis and Machine Intelligence
Computation of component image velocity from local phase information
International Journal of Computer Vision
Multidimensional Orientation Estimation with Applications to Texture Analysis and Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance of optical flow techniques
International Journal of Computer Vision
Determination of optical flow and its discontinuities using non-linear diffusion
ECCV '94 Proceedings of the third European conference on Computer Vision (Vol. II)
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Exploiting Discontinuities in Optical Flow
International Journal of Computer Vision
Computing optical flow via variational techniques
SIAM Journal on Applied Mathematics
Computer Vision and Image Understanding - Special issue on empirical evaluation of computer vision algorithms
A Theoretical Framework for Convex Regularizers in PDE-Based Computation of Image Motion
International Journal of Computer Vision
Multimodal Estimation of Discontinuous Optical Flow using Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '90 Proceedings of the First European Conference on Computer Vision
Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods
International Journal of Computer Vision
Highly Accurate Optic Flow Computation with Theoretically Justified Warping
International Journal of Computer Vision
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 VLSI architecture and algorithm for Lucas-Kanade-based optical flow computation
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Robust bioinspired architecture for optical-flow computation
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
A Database and Evaluation Methodology for Optical Flow
International Journal of Computer Vision
Optical flow: a curve evolution approach
IEEE Transactions on Image Processing
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The computation of optical flow within an image sequence is one of the most widely used techniques in computer vision. In this paper, we present a new approach to estimate the velocity field for motion-compensated compression. It is derived by a nonlinear system using the direct temporal integral of the brightness conservation constraint equation or the Displaced Frame Difference (DFD) equation. To solve the nonlinear system of equations, an adaptive framework is used, which employs velocity field modeling, a nonlinear least-squares model, Gauss-Newton and Levenberg-Marquardt techniques, and an algorithm of the progressive relaxation of the over-constraint. The three criteria by which successful motion-compensated compression is judged are 1.) The fidelity with which the estimated optical flow matches the ground truth motion, 2.) The relative absence of artifacts and ''dirty window'' effects for frame interpolation, and 3.) The cost to code the motion vector field. We base our estimated flow field on a single minimized target function, which leads to motion-compensated predictions without incurring penalties in any of these three criteria. In particular, we compare our proposed algorithm results with those from Block-Matching Algorithms (BMA), and show that with nearly the same number of displacement vectors per fixed block size, the performance of our algorithm exceeds that of BMA in all the three above points. We also test the algorithm on synthetic and natural image sequences, and use it to demonstrate applications for motion-compensated compression.