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Embedded Double Matching of Local Descriptors for a Fast Automatic Recognition of Real-World Objects

Alqaisi, T., Gledhill, Duke and Olszewska, Joanna Isabelle (2012) Embedded Double Matching of Local Descriptors for a Fast Automatic Recognition of Real-World Objects. In: Proceedings of the19th IEEE International Conference on Image Processing (ICIP). IEEE, Orlando, Florida, USA, pp. 2385-2388. ISBN 978-1-4673-2534-9

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    Abstract

    In this paper, we present a new approach for matching local descriptors such as Scale Invariant Feature Transform (SIFT)ones in order to recognize image objects quickly and reliably. The proposed method first computes the Hausdorff distance combined with the City-Block distance to match the two sets of the extracted keypoints from the goal and data images, respectively. Then, the matched points are involved into an embedded pairing process, leading to a double matching which is more discriminant for the object recognition purpose as demonstrated on real-world standard databases.

    Item Type: Book Chapter
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Q Science > QA Mathematics > QA76 Computer software
    Schools: School of Computing and Engineering
    School of Computing and Engineering > Informatics Research Group > Knowledge Engineering and Intelligent Interfaces
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    Depositing User: Joanna Olszewska
    Date Deposited: 26 Jul 2012 14:19
    Last Modified: 09 Jul 2013 14:44
    URI: http://eprints.hud.ac.uk/id/eprint/14122

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