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Vision: are models of object recognition catching up with the brain?
Author(s) -
Poggio Tomaso,
Ullman Shimon
Publication year - 2013
Publication title -
annals of the new york academy of sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.712
H-Index - 248
eISSN - 1749-6632
pISSN - 0077-8923
DOI - 10.1111/nyas.12148
Subject(s) - cognitive neuroscience of visual object recognition , categorization , computer science , visual cortex , computational model , perspective (graphical) , artificial intelligence , identification (biology) , domain (mathematical analysis) , human–computer interaction , cognitive science , object (grammar) , vision science , visual perception , machine learning , perception , neuroscience , psychology , mathematical analysis , botany , mathematics , biology
Object recognition has been a central yet elusive goal of computational vision. For many years, computer performance seemed highly deficient and unable to emulate the basic capabilities of the human recognition system. Over the past decade or so, computer scientists and neuroscientists have developed algorithms and systems—and models of visual cortex—that have come much closer to human performance in visual identification and categorization. In this personal perspective, we discuss the ongoing struggle of visual models to catch up with the visual cortex, identify key reasons for the relatively rapid improvement of artificial systems and models, and identify open problems for computational vision in this domain.