Performance analysis of interest point detection/matching on shiny and non-textured surfaces

Bachelor Thesis (2021)
Author(s)

R.M. Huizer (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

J.C. Gemert – Mentor (TU Delft - Pattern Recognition and Bioinformatics)

Burak Yildiz – Mentor (TU Delft - Pattern Recognition and Bioinformatics)

Luciano C. Siebert – Graduation committee member (TU Delft - Interactive Intelligence)

Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright
© 2021 Rick Huizer
More Info
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Publication Year
2021
Language
English
Copyright
© 2021 Rick Huizer
Graduation Date
01-07-2021
Awarding Institution
Delft University of Technology
Project
['CSE3000 Research Project']
Programme
['Computer Science and Engineering']
Faculty
Electrical Engineering, Mathematics and Computer Science
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Abstract

3D modeling techniques can be used to automate processes such as damage assessment in aircraft engines. Aircraft engines often have shiny and non-textured surfaces, where these modeling techniques often have poor performance. This paper gives more insight into the performance of interest detection/matching algorithms on shiny and non-textured surfaces as found in aircraft engine borescope inspection videos. These algorithms are often used in 3D modeling techniques. Three interest point detection/matching algorithms are executed on different test videos, and various metrics are calculated for each algorithm. This paper is the first paper that compares both recent and traditional computer vision interest point detection/matching algorithms in these specific settings, and contributes to a better understanding of the usability of feature-based 3D reconstruction techniques. The results show that more recent neural network-based approaches outperform traditional approaches.

Files

Final_paper_Huizer.pdf
(pdf | 8.55 Mb)
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