Journal
ANNUAL REVIEW OF PSYCHOLOGY
Volume 55, Issue -, Pages 271-304Publisher
ANNUAL REVIEWS
DOI: 10.1146/annurev.psych.55.090902.142005
Keywords
shape; material; depth; vision; neural; psychophysics; fMRI; computer vision
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Funding
- NATIONAL EYE INSTITUTE [R01EY012691, R01EY011507, R01EY013875] Funding Source: NIH RePORTER
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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.
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