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Image matching using generalized scale-space interest points
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.ORCID iD: 0000-0002-9081-2170
2013 (English)In: Scale Space and Variational Methods in Computer Vision: 4th International Conference, SSVM 2013, Schloss Seggau, Leibnitz, Austria, , June 2-6, 2013, Proceedings / [ed] A. Kuijper et al, Springer Berlin/Heidelberg, 2013, Vol. 7893, 355-367 p.Conference paper, Published paper (Refereed)
Abstract [en]

The performance of matching and object recognition methods based on interest points depends on both the properties of the underlying interest points and the associated image descriptors. This paper demonstrates the advantages of using generalized scale-space interest point detectors when computing image descriptors for image-based matching. These generalized scale-space interest points are based on linking of image features over scale and scale selection by weighted averaging along feature trajectories over scale and allow for a higher ratio of correct matches and a lower ratio of false matches compared to previously known interest point detectors within the same class. Specifically, it is shown how a significant increase in matching performance can be obtained in relation to the underlying interest point detectors in the SIFT and the SURF operators. We propose that these generalized scale-space interest points when accompanied by associated scale-invariant image descriptors should allow for better performance of interest point based methods for image-based matching, object recognition and related vision tasks.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2013. Vol. 7893, 355-367 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743
Keyword [en]
Feature detection, Interest point, Blob detection, Corner detection, Scale, Scale selection, Scale linking, Feature trajectory, Matching, Object recognition, Scale invariance, Affine invariance, Differential invariant, Image descriptor, Scale space, Computer vision
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-118694DOI: 10.1007/978-3-642-38267-3_30Scopus ID: 2-s2.0-84884406110OAI: oai:DiVA.org:kth-118694DiVA: diva2:607455
Conference
SSVM 2013: Fourth International Conference on Scale Space and Variational Methods in Computer Vision, June 2-6, 2013, Schloss Seggau, Graz region, Austria
Funder
Swedish Research Council, 2010-4766
Note

QC 20130702

Available from: 2013-02-23 Created: 2013-02-23 Last updated: 2015-12-07Bibliographically approved

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Lindeberg, Tony

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Citation style
  • apa
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  • de-DE
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