On the estimation of optical flow: relations between different approaches and some new results
Artificial Intelligence
Bayesian Estimation of Motion Vector Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance of optical flow techniques
International Journal of Computer Vision
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Computation and analysis of image motion: a synopsis of current problems and methods
International Journal of Computer Vision
Computing optical flow via variational techniques
SIAM Journal on Applied Mathematics
Multimodal Estimation of Discontinuous Optical Flow using Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters
Robust motion estimation under varying illumination
Image and Vision Computing
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deterministic edge-preserving regularization in computed imaging
IEEE Transactions on Image Processing
Efficient multiscale regularization with applications to the computation of optical flow
IEEE Transactions on Image Processing
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Like many problems in image analysis, motion estimation is an ill-posed one, since the available data do not always sufficiently constrain the solution. It is therefore necessary to regularize the solution by imposing a smoothness constraint. One of the main difficulties while estimating motion is to preserve the discontinuities of the motion field. In this paper, we address this problem by integrating the motion magnitude information obtained by the Fourier analysis into the smoothness constraint, resulting in an adaptive smoothness. We describe how to achieve this with two different motion estimation approaches: the Horn and Schunck method and the Markov Random Field (MRF) modeling. The two smoothness constraints obtained are compared with standard solutions. Experimental results with synthetic and real-life image sequences show a significant improvement of motion estimation in both cases.