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Interest Point Detectors and Descriptors for IR Images: An Evaluation of Common Detectors and Descriptors on IR images
KTH, School of Computer Science and Communication (CSC).
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Detektorer och deskriptorer för extrempunkter i IR-bilder (Swedish)
Abstract [en]

Interest point detectors and descriptors are the basis of many applications within computer vision. In the selection of which methods to use in an application, it is of great interest to know their performance against possible changes to the appearance of the content in an image. Many studies have been completed in the field on visual images while the performance on infrared images is not as charted.

This degree project, conducted at FLIR Systems, provides a performance evaluation of detectors and descriptors on infrared images. Three evaluations steps are performed. The first evaluates the performance of detectors; the second descriptors; and the third combinations of detectors and descriptors.

We find that best performance is obtained by Hessian-Affine with LIOP and the binary combination of ORB detector and BRISK descriptor to be a good alternative with comparable results but with increased computational efficiency by two orders of magnitude. 

Abstract [sv]

Detektorer och deskriptorer är grundpelare till många applikationer inom datorseende. Vid valet av metod till en specifik tillämpning är det av stort intresse att veta hur de presterar mot möjliga förändringar i hur innehållet i en bild framträder. Grundlig forskning är utförd på visuella bilder medan det fortfarande saknas en lika grundläggande kartläggning av deras prestation på infraröda bilder.

Det här examensarbetet utvärderar, på uppdrag av FLIR Systems, hur detektorer och deskriptorer presterar i infraröda bilder. Arbetet är uppdelat i tre utvärderingar varav den första utvärderar detektorer, den andra deskriptorer och den tredje kombinationen av detektor och deskriptor.

Vi finner att bäst resultat uppnås av Hessian-Affine tillsammans med LIOP men att den binära kombinationen av ORB detektor och BRISK deskriptor är ett bra alternativ som har jämförbart resultat men en ökad effektivitet av två storlekordningar. 

Place, publisher, year, edition, pages
2015.
Keyword [en]
Detector, descriptor, evaluation, IR-image
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-175801OAI: oai:DiVA.org:kth-175801DiVA: diva2:862394
External cooperation
FLIR Systems
Educational program
Master of Science in Engineering - Electrical Engineering
Supervisors
Examiners
Available from: 2015-10-27 Created: 2015-10-21 Last updated: 2015-10-27Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
  • ieee
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  • vancouver
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Language
  • de-DE
  • en-GB
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
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