Rotation-invariant categorization of colour images using the Radon transform

Andrew Peter Paplinski

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    4 Citations (Scopus)


    We have derived a novel rotation-invariant feature vector, or signature, for two-variable functions like images. The feature vector is calculated as an angular integral of the Radon transform of the function. Three such feature vectors are calculated for each colour image. Subsequently, these feature vectors are used to categorize colour images in a network of 3+1 self-organizing modules. The 3-D ‘labels’ produced by the first level modules are used by the second level modules and can be thought of as a “universal neuronal code”. The network is trained for un-rotated images and then tested for rotated images. It has been demonstrated that rotation of images by the angles included in calculation of the Radon transform results
    in the perfect categorization. For the angles in between, that is, those not included in the Radon transform, a small shift in
    categorization might occur, keeping, however, the objects well inside their clusters. Since calculation of the rotation-invariant feature vectors is very simple and involves only summations of signals (pixel values), hence very fast, it is postulated that such
    a mechanism might be included in the biological vision systems.
    Original languageEnglish
    Title of host publication2012 International Joint Conference on Neural Networks
    EditorsKate Smith-Miles
    Place of PublicationPiscataway NJ USA
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages1408 - 1413
    Number of pages6
    ISBN (Print)9781467314909
    Publication statusPublished - 2012
    EventIEEE International Joint Conference on Neural Networks 2012 - Brisbane Convention & Exhibition Centre, Brisbane, Australia
    Duration: 10 Jun 201215 Jun 2012 (Proceedings)


    ConferenceIEEE International Joint Conference on Neural Networks 2012
    Abbreviated titleIJCNN 2012
    Internet address

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