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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 > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge
School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge
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Depositing User: Joanna Olszewska
Date Deposited: 26 Jul 2012 13:19
Last Modified: 09 Jul 2013 13:44
URI: http://eprints.hud.ac.uk/id/eprint/14122

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