The angular integral of the Radon transform (aniRT) as a feature vector in categorization of visual objects

Andrew P. Paplinski

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

    Abstract

    The recently introduced angular integral of the Radon transform (aniRT) seems to be a good candidate as a feature vector used in categorization of visual objects in a rotation invariant fashion. We investigate application of aniRT in situations when the number of objects is significant, for example, Chinese characters. Typically, the aniRT feature vector spans the diagonal of the visual object. We show that a subset of the full aniRT vector delivers a good categorization results in a timely manner.
    Original languageEnglish
    Title of host publicationAdvances in Neural Networks (ISNN 2013)
    Subtitle of host publication10th International Symposium on Neural Networks, ISNN 2013, Dalian, China, July 4-6, 2013, Proceedings, Part I
    EditorsChengan Guo, Zeng-Guang Hou, Zhigang Zeng
    Place of PublicationHeidelberg [Germany]
    PublisherSpringer
    Pages523 - 531
    Number of pages9
    ISBN (Electronic)9783642390654
    ISBN (Print)9783642390647
    DOIs
    Publication statusPublished - 2013
    EventInternational Symposium on Neural Networks 2013 - Dalian, China
    Duration: 4 Jul 20136 Jul 2013
    Conference number: 10th
    https://link.springer.com/book/10.1007/978-3-642-39065-4 (Conference Proceedings)

    Publication series

    NameLecture Notes in Compute Science
    PublisherSpringer
    Volume7951
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    ConferenceInternational Symposium on Neural Networks 2013
    Abbreviated titleISNN 2013
    CountryChina
    CityDalian
    Period4/07/136/07/13
    OtherProceedings
    Part of the Lecture Notes in Computer Science book series (LNCS, volume 7951)
    Internet address

    Keywords

    • Radon transform
    • Categorization of visual objects
    • Chinese characters
    • Self-organizing maps
    • Incremental learning

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