Probabilistic Motion Estimation Based on Temporal Coherence

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.Comment: 40 pages, 7 figure

Target Tracking in Non-Gaussian Environment

Masreliez filter which is a Kalman type of recursive filter is implemented and validated. The main computation in Masreliez filter is to evaluate the score function which directly influences the estimates of the target states. Scalar approximation for score function evaluation is extended to vector observations, implemented and validated. The simulation studies have shown that the performance of the Masreliez filter is relatively better than that of the conventional Kalman filter in the presence of significant glint noise in the observation