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Information Theoretic Similarity Measures for Robust Image Matching: Multimodal Imaging - Infrared and Visible light
KTH, School of Computer Science and Communication (CSC), Theoretical Computer Science, TCS.
KTH, School of Computer Science and Communication (CSC), Theoretical Computer Science, TCS.
2016 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Informationsteoretiska Likhetsmått för Robust Matchning av Bilder : Multimodal Bildbehandling - Infraröd och Synligt ljus (Swedish)
Abstract [en]

Abstract

This study aimed to investigate the applicability of three different information theoretic similarity measures in image matching, mutual information (MI), cross-cumulative residual entropy (CCRE) and sum of conditional variances (SCV). An experiment was conducted to assess the impact on the performances of the similarity measures when dealing with multimodality, in this case in the context of infrared and visible light. This was achieved by running simulations of four different scenarios using images taken in infrared and visible light, and additionally with variations in amount of details to create different experimental setups. Namely experimental setup A: unimodal data sets with more and less details and experimental setup B: multimodal datasets with more and less details.

The result showed that the concept of multimodality gives a statistically significant effect on the performances of all similarity measures. Observations were made that the similarity measures performances also, when trying to match images with different amount of details, differed from each other. This provided a basis for judgement on what measure to use as to give as clear and sound results as possible depending on the variation of detail amount in the data. With this study, it was concluded that the similarity measure CCRE gave the most clear and sound results in the context of multimodality concerning infrared and visible light for both cases of more or less details. Even though the other similarity measures performed well in some cases, CCRE would be to recommend as observed by this study.

Keywords : Image matching, image registration, information theoretic similarity measures, multimodal imaging, similarity measures, MI, CCRE, SCV, infrared, visible light.

Abstract [sv]

Denna studie syftade till att undersöka tillämpligheten av tre olika informationsteoretiska likhetsmått vid matchning av bilder, mutual information (MI), cross cumulative residual entropy (CCRE) och sum of conditional variances (SCV). Ett experiment genomfördes för att bedöma hur de olika likhetsmåtten påverkades i kontexten av multimodalitet, i detta fall i samband med infrarött och synligt ljus. Detta uppnåddes genom att köra simuleringar av fyra olika scenarier med hjälp av bilder tagna i infrarött och synligt ljus, och dessutom med variationer i mängden detaljer för att skapa olika experimentella uppsättningar. Nämligen experimentuppsättning A: unimodala datamängder med mer / mindre detaljer och experimentuppsättning B: multimodala datamängder med mer / mindre detaljer.

 

Resultatet visade att multimodalitet har en statistiskt signifikant påverkan på alla likhetsmått. Observationer gjordes att likhetsmåttens beteenden, när man försöker matcha bilder med olika mängd detaljer, skilde sig från varandra. Detta gav en grund för bedömning av vilken av dessa likhetsmått som då kunde användas för att ge de mest tydliga och stabila resultaten som möjligt beroende på variationen av mängden detaljer i datat. Med denna studie drogs slutsatsen att likhetsmåttet CCRE gav mest de tydliga och stabila resultaten i samband med multimodalitet gällande infrarött och synligt ljus för båda fallen av mer eller mindre detaljer. Även om de andra likhetsmåtten också gav goda resultat i vissa fall, skulle CCRE vara att rekommendera, som observerat i denna studie.

Place, publisher, year, edition, pages
2016.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-186450OAI: oai:DiVA.org:kth-186450DiVA: diva2:927339
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Available from: 2016-05-12 Created: 2016-05-11 Last updated: 2016-06-15Bibliographically approved

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