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Rotation Invariant Categorization of Visual Objects Using Radon Transform and Self-Organizing Modules

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Neural Information Processing. Models and Applications (ICONIP 2010)

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Abstract

The Radon transform in combination with self-organizing maps is used to build the rotation invariant systems for categorization of visual objects. The first system has one SOM per the Radon transform direction. The outputs from these directional SOMs that represent positions of the winners and related post-synaptic activities, form the input to the final categorizing SOM. Such a network delivers robust rotation invariant categorization of images rotated by angles up to around 12o. In the second network the angular Radon transform vectors are combined together and form the input to the categorizing SOM. This network can correctly categorized visual stimuli rotated by up to 30o. The rotation invariance can be improved by increasing the number of Radon transform angle, which has been equal to six in our initial experiments.

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Authors and Affiliations

  1. Clayton School of IT, Monash University, Australia

    Andrew P. Papliński

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  1. Andrew P. Papliński
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Editors and Affiliations

  1. School of Information Technology, Murdoch University, 6150, Murdoch, WA, Australia

    Kok Wai Wong

  2. The Australian National University, 0200, Canberra, ACT, Australia

    B. Sumudu U. Mendis

  3. School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Northfields Avenue, 2522, P.O. Box, Wollongong, NSW, Australia

    Abdesselam Bouzerdoum

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Papliński, A.P. (2010). Rotation Invariant Categorization of Visual Objects Using Radon Transform and Self-Organizing Modules. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Models and Applications. ICONIP 2010. Lecture Notes in Computer Science, vol 6444. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17534-3_44

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  • DOI: https://doi.org/10.1007/978-3-642-17534-3_44

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17533-6

  • Online ISBN: 978-3-642-17534-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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