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International Workshop on Brain-Inspired Computing

BrainComp 2013: Brain-Inspired Computing pp 51–62Cite as

Models of the Visual Cortex for Object Representation: Learning and Wired Approaches

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8603))

Abstract

Computational modeling now spans more than three decades. Biologically-plausible models are usually organized into a hierarchy that models the brain in primates after carefully examining neurophysiological and psychophysical studies. Currently, these models extract some values (corners, edges, textures, contours) from images and then apply machine learning algorithms to learn objects or shapes. Are they really that different from classical, non-biologically-inspired, computer vision methods? What facts can we learn from the primate visual system other than the extensively used edge extraction by means of Gabor filters? Should we work more on the representation along this hierarchy before applying a learning strategy? We review the status of computational modeling for object recognition and propose what can be the next challenges to solve.

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Author information

Authors and Affiliations

  1. Intelligent and Interactive Systems, University of Innsbruck, Technikerstr. 21A, Innsbruck, 6020, Austria

    Antonio J. Rodríguez-Sánchez & Justus Piater

Authors
  1. Antonio J. Rodríguez-Sánchez
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  2. Justus Piater
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Correspondence to Antonio J. Rodríguez-Sánchez .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Calabria, Arcavacada di Rende, Italy

    Lucio Grandinetti

  2. Jülich Supercomputing Centre, Jülich, Germany

    Thomas Lippert

  3. Computing Science, University of Groningen Institute for Mathematics &, Groningen, The Netherlands

    Nicolai Petkov

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Rodríguez-Sánchez, A.J., Piater, J. (2014). Models of the Visual Cortex for Object Representation: Learning and Wired Approaches. In: Grandinetti, L., Lippert, T., Petkov, N. (eds) Brain-Inspired Computing. BrainComp 2013. Lecture Notes in Computer Science(), vol 8603. Springer, Cham. https://doi.org/10.1007/978-3-319-12084-3_5

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  • DOI: https://doi.org/10.1007/978-3-319-12084-3_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12083-6

  • Online ISBN: 978-3-319-12084-3

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