Tracking and data association
Elements of information theory
Elements of information theory
Active vision
Local parallel computation of stochastic completion fields
Neural Computation
Data Fusion for Sensory Information Processing Systems
Data Fusion for Sensory Information Processing Systems
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Visual Motion Estimation and Prediction: A Probabilistic Network Model for Temporal Coherence
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A Mixed-State Condensation Tracker with Automatic Model-Switching
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Parametric measurements of optic flow by humans
Optic flow and beyond
Computational algorithms and neuronal network models underlying decision processes
Neural Networks - 2006 Special issue: Neurobiology of decision making
Motion-based prediction is sufficient to solve the aperture problem
Neural Computation
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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 that engineers use 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 that 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. In deriving our theory, we assumed spatial factorizability of the probability distributions and made the approximation of updating the marginal distributions of velocity at each point. This allowed us to perform local computations and simplified our implementation. We argue that these approximations are suitable for the stimuli we are considering (for which spatial coherence effects are negligible).