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Fast Matching of Binary Descriptors for Large-scale Applications in Robot Vision
Örebro University, School of Science and Technology.
Örebro University, School of Science and Technology.ORCID iD: 0000-0002-3122-693X
2016 (English)In: International Journal of Advanced Robotic Systems, ISSN 1729-8806, E-ISSN 1729-8814, Vol. 13, article id 58Article in journal (Refereed) Published
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Abstract [en]

The introduction of computationally efficient binary feature descriptors has raised new opportunities for real-world robot vision applications. However, brute force feature matching of binary descriptors is only practical for smaller datasets. In the literature, there has therefore been an increasing interest in representing and matching binary descriptors more efficiently. In this article, we follow this trend and present a method for efficiently and dynamically quantizing binary descriptors through a summarized frequency count into compact representations (called fsum) for improved feature matching of binary pointfeatures. With the motivation that real-world robot applications must adapt to a changing environment, we further present an overview of the field of algorithms, which concerns the efficient matching of binary descriptors and which are able to incorporate changes over time, such as clustered search trees and bag-of-features improved by vocabulary adaptation. The focus for this article is on evaluation, particularly large scale evaluation, compared to alternatives that exist within the field. Throughout this evaluation it is shown that the fsum approach is both efficient in terms of computational cost and memory requirements, while retaining adequate retrieval accuracy. It is further shown that the presented algorithm is equally suited to binary descriptors of arbitrary type and that the algorithm is therefore a valid option for several types of vision applications.

Place, publisher, year, edition, pages
Rijeka, Croatia: InTech, 2016. Vol. 13, article id 58
Keywords [en]
Binary Descriptors, Efficient Feature Matching, Real-world Robotic Vision Applications
National Category
Robotics
Identifiers
URN: urn:nbn:se:oru:diva-49931DOI: 10.5772/62162ISI: 000372839300002OAI: oai:DiVA.org:oru-49931DiVA, id: diva2:923401
Funder
Swedish Research Council, 2011-6104Available from: 2016-04-26 Created: 2016-04-26 Last updated: 2017-11-30Bibliographically approved

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Persson, AndreasLoutfi, Amy

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CiteExportLink to record
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Citation style
  • apa
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  • Other style
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  • de-DE
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