Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic interpretation of population codes
Neural Computation
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Computational models for neuroscience
Understanding belief propagation and its generalizations
Exploring artificial intelligence in the new millennium
Bayesian computation in recurrent neural circuits
Neural Computation
Machine Learning: Discriminative and Generative (Kluwer International Series in Engineering and Computer Science)
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
Correctness of Local Probability Propagation in Graphical Models with Loops
Neural Computation
Intermediate-level visual representations and the construction of surface perception
Journal of Cognitive Neuroscience
Bayesian tree-structured image modeling using wavelet-domain hidden Markov models
IEEE Transactions on Image Processing
Disambiguating Visual Motion by Form-Motion Interaction--a Computational Model
International Journal of Computer Vision
What can we learn from biological vision studies for human motion segmentation?
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
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One of the challenges faced by the visual system is integrating cues within and across processing streams for inferring scene properties and structure. This is particularly apparent in the inference of object motion, where psychophysical experiments have shown that integration of motion signals, distributed across space, must also be integrated with form cues. This has led several to conclude that there exist mechanisms which enable form cues to 'veto' or completely suppress ambiguous motion signals. We describe a probabilistic approach which uses a generative network model for integrating form and motion cues using the machinery of belief propagation and Bayesian inference. We show, using computer simulations, that motion integration can be mediated via a local, probabilistic representation of contour ownership, which we have previously termed 'direction of figure'. The uncertainty of this inferred form cue is used to modulate the covariance matrix of network nodes representing local motion estimates in the motion stream. We show with results for two sets of stimuli that the model does not completely suppress ambiguous cues, but instead integrates them in a way that is a function of their underlying uncertainty. The result is that the model can account for the continuum of bias seen for motion coherence and perceived object motion in psychophysical experiments.