2004 ObjectPerceptionAsBayesianInfer

• (Kersten et al., 2004) ⇒ Daniel Kersten, Pascal Mamassian, and Alan Yuille. (2004). “Object Perception As Bayesian Inference.” In: Annu. Rev. Psychol. Journal, 55. doi:10.1146/annurev.psych.55.090902.142005

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• http://scholar.google.com/scholar?q=%22Object+perception+as+Bayesian+inference%22+2004

Quotes

Author Keywords

• shape; material; depth; vision; neural; psychophysics; fMRI; computer vision

Abstract

We perceive the shapes and material properties of objects quickly and reliably despite the complexity and objective ambiguities of natural images. Typical images are highly complex because they consist of many objects embedded in background clutter. Moreover, the image features of an object are extremely variable and ambiguous owing to the effects of projection, occlusion, background clutter, and illumination. The very success of everyday vision implies neural mechanisms, yet to be understood, that discount irrelevant information and organize ambiguous or noisy local image features into objects and surfaces. Recent work in Bayesian theories of visual perception has shown how complexity may be managed and ambiguity resolved through the task-dependent, probabilistic integration of prior object knowledge with image features.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2004 ObjectPerceptionAsBayesianInfer	Daniel Kersten Pascal Mamassian Alan Yuille			Object Perception As Bayesian Inference				10.1146/annurev.psych.55.090902.142005		2004