Bayesian models of object perception

Daniel Kersten, Alan Yuille

Research output: Contribution to journalReview articlepeer-review

183 Scopus citations

Abstract

The human visual system is the most complex pattern recognition device known. In ways that are yet to be fully understood, the visual cortex arrives at a simple and unambiguous interpretation of data from the retinal image that is useful for the decisions and actions of everyday life. Recent advances in Bayesian models of computer vision and in the measurement and modeling of natural image statistics are providing the tools to test and constrain theories of human object perception. In turn, these theories are having an impact on the interpretation of cortical function.

Original languageEnglish (US)
Pages (from-to)150-158
Number of pages9
JournalCurrent opinion in neurobiology
Volume13
Issue number2
DOIs
StatePublished - Apr 2003

Bibliographical note

Funding Information:
Supported by grants from the National Institute of Health RO1 EY11507-001, EY02857 and EY12691, and National Science Foundation SBR-9631682.

Fingerprint Dive into the research topics of 'Bayesian models of object perception'. Together they form a unique fingerprint.

Cite this