Comutations underlying the measuremnt of visual motion.
Artificial Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Diffusions for global optimizations
SIAM Journal on Control and Optimization
Scene Segmentation from Visual Motion Using Global Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
The theory and practice of Bayesian image labeling
International Journal of Computer Vision
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
The mean field theory for image motion estimation
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: image and multidimensional signal processing - Volume V
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The estimation of 2D motion from spatio-temporally sampled image sequences is discussed, concentrating on the optimization aspect of the problem formulated through a Bayesian framework based on Markov random field (MRF) models. First, the Maximum A Posteriori Probability (MAP) formulation for motion estimation over discrete and continuous state spaces is reviewed along with the solution method using simulated annealing (SA). Then, instantaneous 'freezing' is applied to the stochastic algorithms resulting in well known deterministic methods. The stochastic algorithms are compared with their deterministic approximations over image sequences with natural data and synthetic as well as natural motion.