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An Evaluation of Local Feature Detectors and Descriptors for Infrared Images
KTH, School of Computer Science and Communication (CSC).
FLIR Systems AB.
KTH, School of Computer Science and Communication (CSC).
2016 (English)In: Volume 9915 of the series Lecture Notes in Computer Science, Springer, 2016Conference paper (Refereed)
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.

Place, publisher, year, edition, pages
Springer, 2016.
Keyword [en]
Infrared images, Local features, Detectors, Descriptors
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-199391OAI: oai:DiVA.org:kth-199391DiVA: diva2:1062250
Conference
European Conference on Computer Vision (ECCV) Workshop
Available from: 2017-01-05 Created: 2017-01-05 Last updated: 2017-01-05

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