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Abstract: This paper presents a new approach to the estimation of two-dimensional motion vector fields from time-varying images. The approach is stochastic, both in its formulation and in the solution method. The formulation involves the specification of a deterministic structural model, along with stochastic observation and motion field models. Two motion models are proposed: a globally smooth model based on vector Markov random fields and a piecewise smooth model derived from coupled vector-binary... (Update)
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BibTeX entry: (Update)
J. Konrad and E. Dubois, "Bayesian estimation of motion vector fields," IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-14, pp. 910--927, Sept. 1992. http://citeseer.ist.psu.edu/konrad92bayesian.html More
@article{ konrad92bayesian,
author = "Konrad, J. and Dubois, E.",
title = "Bayesian Estimation of Motion Vector Fields",
journal = "PAMI",
volume = "14",
number = "9",
month = "September",
pages = "910-927",
year = "1992",
url = "citeseer.ist.psu.edu/konrad92bayesian.html" }
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105 An investigation of smoothness constraints for the estimatio.. (context) - Nagel, Enkelmann - 1986
101 Applied Probability Models with Optimization Applications (context) - Ross - 1970
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76 Fast filter transforms for image processing (context) - Burt - 1981
74 Scene segmentation from visual motion using global optimizat.. (context) - Murray, Buxton - 1987
63 Investigations of multigrid algorithms for the estimation of.. (context) - Enkelmann - 1988
58 Computations underlying the measurement of visual motion (context) - Hildreth - 1984
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8 Bayesian estimation of motion fields from image sequences (context) - Konrad - 1989
8 Motion estimation and segmentation using a global Bayesian a.. (context) - Heitz, Bouthemy - 1990
7 Model-based motion estimation and its application to restora.. (context) - Martinez - 1986
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4 Hierarchical motion detection (context) - Glazer - 1987
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