Comutations underlying the measuremnt of visual motion.
Artificial Intelligence
A Maximum Likelihood Framework for Determining Moving Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
The theory and practice of Bayesian image labeling
International Journal of Computer Vision
On the Detection of Motion and the Computation of Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
A theory of the motion fields of curves
International Journal of Computer Vision
Multimodal Estimation of Discontinuous Optical Flow using Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This paper is concerned with the problem of computing normal displacements along contours in image sequences. Our estimation is restricted to the perpendicular-to-the-edge velocity component, since the well-known "aperture problem" restricts any local estimation to this only component. We model moving edges as spatio-temporal surface patches in the image sequence space (x, y, t). A statistical regularization scheme based on Markov random fields allows us to get a homogeneous and relevant normal motion field along contours. It turns out that it can be implemented in an efficient way, mostly leading to convolution-like computations. Subpixel accuracy comes straightforwardly with this modeling, and is handled within the optimization stage itself, not as a post-processing step. Results are presented concerning synthetic experiments and real-world sequences.