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An Evaluation of Local Feature Detectors and Descriptors for Infrared Images
KTH, Skolan för datavetenskap och kommunikation (CSC).
FLIR Systems AB.
KTH, Skolan för datavetenskap och kommunikation (CSC), Robotik, perception och lärande, RPL.ORCID-id: 0000-0002-4266-6746
2016 (engelsk)Inngår i: Lecture Notes in Computer Science, Volume 9915, 2016, s. 711-723Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

This paper provides a comparative performance evaluation of local features for infrared (IR) images across different combinations of common detectors and descriptors. Although numerous studies report comparisons of local features designed for ordinary visual images, their performance on IR images is far less charted. We perform a systematic investigation, thoroughly exploiting the established benchmark while also introducing a new IR image data set. The contribution is two-fold: we (i) evaluate the performance of both local float type and more recent binary type detectors and descriptors in their combinations under a variety (6 kinds) of image transformations, and (ii) make a new IR image data set publicly available. Through our investigation we gain novel and useful insights for applying state-of-the art local features to IR images with different properties.

sted, utgiver, år, opplag, sider
2016. s. 711-723
Emneord [en]
Infrared images, Local features, Detectors, Descriptors
HSV kategori
Forskningsprogram
Datalogi
Identifikatorer
URN: urn:nbn:se:kth:diva-199391DOI: 10.1007/978-3-319-49409-8_59ISI: 000389501100059Scopus ID: 2-s2.0-85006004155OAI: oai:DiVA.org:kth-199391DiVA, id: diva2:1062250
Konferanse
European Conference on Computer Vision (ECCV) Workshop
Merknad

QC 20170118

Tilgjengelig fra: 2017-01-05 Laget: 2017-01-05 Sist oppdatert: 2020-03-05bibliografisk kontrollert

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Totalt: 111 treff
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