Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Last Update2007: administrator
Metadata Last Update2020: administrator
Citation KeyDonattiWürt:2007:MeOrIn
TitleMemory Organization for Invariant Object Recognition and Categorization
DateOct. 7-10, 2007
Access Date2021, Jan. 16
Number of Files1
Size59 KiB
Context area
Author1 Donatti, Guillermo S.
2 Würtz, Rolf P.
Affiliation1 Institut für Neuroinformatik, International Graduate School of Neuroscience, Ruhr-Universität Bochum
2 Institut für Neuroinformatik, International Graduate School of Neuroscience, Ruhr-Universität Bochum
EditorGonçalves, Luiz
Wu, Shin Ting
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 20 (SIBGRAPI)
Conference LocationBelo Horizonte
Book TitleProceedings
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Tertiary TypeTechnical Poster
History2008-07-17 14:03:08 :: -> administrator ::
2008-07-17 14:05:11 :: administrator -> banon ::
2008-07-17 14:07:07 :: banon -> administrator ::
2009-08-13 20:38:49 :: administrator -> banon ::
2010-08-28 20:02:33 :: banon -> administrator ::
2020-02-19 03:06:19 :: administrator -> :: 2007
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
KeywordsComputer Vision, Theoretical Neuroscience, Neuroscience.
AbstractThe integration of bottom-up with top-down object processing has always been a topic of major concern in computer vision. However, while a lot is known about feature extraction, the knowledge-driven aspect of perception has been recognized as important, but hard to probe experimentally and difficult to implement in computer vision systems. How object knowledge must be organized so that it supports scene perception and can be acquired automatically is a research problem of outstanding significance for the biological, the psychological, and the computational approach to understand perception. The present work aims to develop an object memory model which can provide fast retrieval and robust recognition and categorization. The underlying data structure is inspired by the neural network structure of the human brain, connecting similar object views with excitatory synapses and using inhibitory synapses to separate different ones. The insights derived from building such a computational theory and the properties of the resulting model have implications for strategies and experimental paradigms to analyze human object memory as well as technical applications for robotics and computer vision.
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