Probabilistic Motion Estimation Based on Temporal Coherence

Details Probabilistic Motion Estimation Based on Temporal Coherence Probabilistic Motion Estimation Based on Temporal Coherence

2012-01-05

arxiv.org/abs/1201.1216v1

Neural Computation, 2000, vol. 12, no. 8, p. 1839-1867

Computer Science

Computer Vision and Pattern Recognition

40 pages, 7 figures

Scientific paper

10.1162/089976600300015169

We develop a theory for the temporal integration of visual motion motivated by psychophysical experiments. The theory proposes that input data are temporally grouped and used to predict and estimate the motion flows in the image sequence. This temporal grouping can be considered a generalization of the data association techniques used by engineers to study motion sequences. Our temporal-grouping theory is expressed in terms of the Bayesian generalization of standard Kalman filtering. To implement the theory we derive a parallel network which shares some properties of cortical networks. Computer simulations of this network demonstrate that our theory qualitatively accounts for psychophysical experiments on motion occlusion and motion outliers.

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