Full-Angle Quaternions for Robustly Matching Vectors of 3D Rotations

Stephan Liwicki, Minh-Tri Pham, Stefanos Zafeiriou, Maja Pantic, Björn Stenger

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    3 Citations (Scopus)
    48 Downloads (Pure)

    Abstract

    In this paper we introduce a new distance for robustly matching vectors of 3D rotations. A special representation of 3D rotations, which we coin full-angle quaternion (FAQ), allows us to express this distance as Euclidean. We apply the distance to the problems of 3D shape recognition from point clouds and 2D object tracking in color video. For the former, we introduce a hashing scheme for scale and translation which outperforms the previous state-of-the-art approach on a public dataset. For the latter, we incorporate online subspace learning with the proposed FAQ representation to highlight the benefits of the new representation.
    Original languageUndefined
    Title of host publicationProceedings of IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014
    Place of PublicationUSA
    PublisherIEEE Computer Society
    Pages105-112
    Number of pages8
    ISBN (Print)978-1-4799-5117-8
    DOIs
    Publication statusPublished - Jun 2014
    Event27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014 - Columbus, OH, USA, Columbus, United States
    Duration: 23 Jun 201428 Jun 2014
    Conference number: 27

    Publication series

    Name
    PublisherIEEE Computer Society
    ISSN (Print)1063-6919

    Conference

    Conference27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014
    Abbreviated titleCVPR 2014
    CountryUnited States
    CityColumbus
    Period23/06/1428/06/14
    Other23-28 June 2014

    Keywords

    • HMI-HF: Human Factors
    • METIS-309945
    • IR-95226
    • EWI-25819

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