Abstract
A new stochastic motion estimation method based on the Maximum A Posteriori Probability (MAP) criterion is developed. Deterministic algorithms approximating the MAP estimation over discrete and continuous state spaces are proposed. These approximations result in known motion estimation algorithms. The theoretical superiority of the stochastic algorithms over deterministic approximations in locating the global optimum is confirmed experimentally.
Work supported by the Natural Sciences and Engineering Research Council of Canada under Strategic Grant G-1357
This research was conducted when the author was also with the Department of Electrical Engineering, McGill University, Montreal, Canada, on leave from the Technical University of Szezecin, Poland
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Konrad, J., Dubois, E. (1990). A comparison of stochastic and deterministic solution methods in Bayesian estimation of 2-D motion. In: Faugeras, O. (eds) Computer Vision — ECCV 90. ECCV 1990. Lecture Notes in Computer Science, vol 427. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0014861
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DOI: https://doi.org/10.1007/BFb0014861
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