Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Bayesian decision theory and psychophysics
Perception as Bayesian inference
Shape from texture: ideal observers and human psychophysics
Perception as Bayesian inference
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Data Fusion for Sensory Information Processing Systems
Data Fusion for Sensory Information Processing Systems
Object Perception: Generative Image Models and Bayesian Inference
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Auxiliary object knowledge influences visually-guided interception behavior
APGV '05 Proceedings of the 2nd symposium on Applied perception in graphics and visualization
Solving the process of hysteresis without determining the optimal thresholds
Pattern Recognition
A framework for enhancing depth perception in computer graphics
Proceedings of the 7th Symposium on Applied Perception in Graphics and Visualization
Pattern Recognition Letters
Multistability and perceptual inference
Neural Computation
A framework for applying the principles of depth perception to information visualization
ACM Transactions on Applied Perception (TAP)
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Bayesian parameter estimation can be used to generate statistically optimal solutions to the problem of cue integration. However, the complexity and dimensionality of these solutions is frequently prohibitive. In this paper, we show how the complexity and performance characteristics of the optimal estimator for a task depend strongly on the detailed formulation of the task, including the choice of representation for the scene variables. In particular, some representations lead to simpler inference algorithms than others. We illustrate the problem of cue integration for the perception of depth from two highly disparate cues, cast shadow position and image size, and show how the complexity and performance of the depth estimators depends on the specific representation (choice) of depth parameter. From the analysis we predict human performance on a simple depth discrimination task from the optimal cue integration in each depth representation. We find that the cue-integration strategy used by human subjects can be described as near-optimal using a particular choice of depth representation.