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Frequency map by Structure Tensor in Logarithmic Scale Space and Forensic Fingerprints
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-4929-1262
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
2016 (English)In: PROCEEDINGS OF 29TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, (CVPRW 2016), Piscataway, NJ: IEEE, 2016, p. 204-213, article id 7789522Conference paper, Published paper (Refereed)
Abstract [en]

Increasingly, absolute frequency and orientation maps are needed, e.g. for forensics. We introduce a non-linear scale space via the logarithm of trace of the Structure Tensor. Therein, frequency estimation becomes an orientation estimation problem. We show that this offers significant advantages, including construction of efficient isotropic estimations of dense maps of frequency. In fingerprints, both maps are shown to improve each other in an enhancement scheme via Gabor filtering. We suggest a novel continuous ridge counting method, relying only on dense absolute frequency and orientation maps, without ridge detection, thinning, etc. Furthermore, we present new evidence that frequency maps are useful attributes of minutiae. We verify that the suggested method compares favorably with state of the art using forensic fingerprints as test bed, and test images where the ground truth is known. In evaluations, we use public data sets and published methods only.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2016. p. 204-213, article id 7789522
Series
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, ISSN 2160-7508
Keywords [en]
Absolute frequency, Gabor filtering, Logarithmic scale, Orientation estimation, Orientation maps, Ridge detections, State of the art, Structure tensors
National Category
Signal Processing Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:hh:diva-35669DOI: 10.1109/CVPRW.2016.32ISI: 000391572100025Scopus ID: 2-s2.0-85010210669ISBN: 978-1-5090-1437-8 (electronic)OAI: oai:DiVA.org:hh-35669DiVA, id: diva2:1161901
Conference
29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016; Las Vegas; United States; 26 June 2016 through 1 July 2016.
Available from: 2017-12-01 Created: 2017-12-01 Last updated: 2017-12-01Bibliographically approved

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Bigun, JosefMikaelyan, Anna

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf