A feedforward architecture accounts for rapid categorization.

A feedforward architecture accounts for rapid categorization. Research Abstract Details 

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A feedforward architecture accounts for rapid categorization. Abstract Text:

Thomas Serre,Aude Oliva,Tomaso Poggio,

Primates are remarkably good at recognizing objects. The level of performance of their visual system and its robustness to image degradations still surpasses the best computer vision systems despite decades of engineering effort. In particular, the high accuracy of primates in ultra rapid object categorization and rapid serial visual presentation tasks is remarkable. Given the number of processing stages involved and typical neural latencies, such rapid visual processing is likely to be mostly feedforward. Here we show that a specific implementation of a class of feedforward theories of object recognition (that extend the Hubel and Wiesel simple-to-complex cell hierarchy and account for many anatomical and physiological constraints) can predict the level and the pattern of performance achieved by humans on a rapid masked animal vs. non-animal categorization task.

A feedforward architecture accounts for rapid categorization. Publishing Authors By Initials

T Serre,A Oliva,T Poggio,

For similar psychological phenomena and processes: mental processes: perception: visual perception research abstracts see: psychological phenomena and processes: mental processes: perception: visual perception research

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A feedforward architecture accounts for rapid categorization. Journal Published:

PUBLICATION TYPE: Research Support, U.S. Gov't,

Journal: Proceedings of the National Academy of Sciences of

VOLUME: 104

Page Numbers: 6424-9

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ISSN: 0027-8424

DAY: 2

MONTH: 04

YEAR: 2007

A feedforward architecture accounts for rapid categorization. Information

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LANGUAGE: eng

NlmUniqueID: 7505876

A feedforward architecture accounts for rapid categorization. Keywords Mesh Terms:

KEYWORDS: Visual Perception

MESH TERMS: physiology

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Grant and Affiliation Information for A feedforward architecture accounts for rapid categorization.

AFFILIATION: Center for Biological and Computational Learning, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. serre@mit.edu

Country: United States

AGENCY: United States NIMH

GRANT: 1 P20MH66239-01A1

ACRONYM: MH

MEDLINETA: Proc Natl Acad Sci U S A

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