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Frequency map by Structure Tensor in Logarithmic Scale Space and Forensic Fingerprints
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0002-4929-1262
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
2016 (Engelska)Ingår i: PROCEEDINGS OF 29TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, (CVPRW 2016), Piscataway, NJ: IEEE, 2016, s. 204-213, artikel-id 7789522Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
Piscataway, NJ: IEEE, 2016. s. 204-213, artikel-id 7789522
Serie
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, ISSN 2160-7508
Nyckelord [en]
Absolute frequency, Gabor filtering, Logarithmic scale, Orientation estimation, Orientation maps, Ridge detections, State of the art, Structure tensors
Nationell ämneskategori
Signalbehandling Sannolikhetsteori och statistik
Identifikatorer
URN: urn:nbn:se:hh:diva-35669DOI: 10.1109/CVPRW.2016.32ISI: 000391572100025Scopus ID: 2-s2.0-85010210669ISBN: 978-1-5090-1437-8 (digital)OAI: oai:DiVA.org:hh-35669DiVA, id: diva2:1161901
Konferens
29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016; Las Vegas; United States; 26 June 2016 through 1 July 2016.
Tillgänglig från: 2017-12-01 Skapad: 2017-12-01 Senast uppdaterad: 2017-12-01Bibliografiskt granskad

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Bigun, JosefMikaelyan, Anna

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SignalbehandlingSannolikhetsteori och statistik

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