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Research article summary:
An integrated network for invariant visual detection and recognition.
Abstract Extract:
We describe an architecture for invariant visual detection and recognition. Learning is performed in a single central module. The architecture makes use of a replica module consisting of copies of retinotopic layers of local features, with a particular ... (Full abstract text below) Published 2003Sep
in Journal: Vision Res
(Language : eng)
Full Pubmed Extract
This information was retrieved, real-time, on your behalf from the public area of the Pubmed website:
1. Vision Res.
2003 Sep;43(19):2073-88
An integrated network for invariant visual detection and recognition.
Amit Y, Mascaro M
Department of Statistics, University of Chicago, Chicago, IL 60637, USA. amit@galton.uchicago.edu
We describe an architecture for invariant visual detection and recognition. Learning is performed in a single central module. The architecture makes use of a replica module consisting of copies of retinotopic layers of local features, with a particular design of inputs and outputs, that allows them to be primed either to attend to a particular location, or to attend to a particular object representation. In the former case the data at a selected location can be classified in the central module. In the latter case all instances of the selected object are detected in the field of view. The architecture is used to explain a number of psychophysical and physiological observations: object based attention, the different response time slopes of target detection among distractors, and observed attentional modulation of neuronal responses. We hypothesize that the organization of visual cortex in columns of neurons responding to the same feature at the same location may provide the copying architecture needed for translation invariance.
PMID : 12842160 [PubMed - Indexed for MEDLINE]
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Full Author Information
| First Name | LastName | Initials |
| Yali | Amit | Y |
| Massimo | Mascaro | M |
Affiliation: Department of Statistics, University of Chicago, Chicago, IL 60637, USA. amit@galton.uchicago.edu
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