Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Computer graphics: principles and practice (2nd ed.)
Computer graphics: principles and practice (2nd ed.)
Perception as Bayesian inference
Perception as Bayesian inference
Bayesian decision theory and psychophysics
Perception as Bayesian inference
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Data Fusion for Sensory Information Processing Systems
Data Fusion for Sensory Information Processing Systems
How Optimal Depth Cue Integration Depends on the Task
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Minimax Entropy Principle and Its Application to Texture Modeling
Neural Computation
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Humans perceive object properties such as shape and material quickly and reliably despite the complexity and objective ambiguities of natural images. The visual system does this by integrating prior object knowledge with critical image features appropriate for each of a discrete number of tasks. Bayesian decision theory provides a prescription for the optimal utilization of knowledge for a task that can guide the possibly sub-optimal models of human vision. However, formulating optimal theories for realistic vision problems is a non-trivial problem, and we can gain insight into visual inference by first characterizing the causal structure of image features-the generative model. I describe some experimental results that apply generative models and Bayesian decision theory to investigate human object perception.