Improving SIFT for Image Feature Extraction

  • Renata Deak Department of Computer Science, Faculty of Mathematics and Computer Science, Babeș-Bolyai University, Cluj-Napoca, Romania
  • Adrian Sterca Department of Computer Science, Faculty of Mathematics and Computer Science, Babeș-Bolyai University, Cluj-Napoca, Romania
  • Ioan Bădărînză Department of Computer Science, Faculty of Mathematics and Computer Science, Babeș-Bolyai University, Cluj-Napoca, Romania

Abstract

This paper reviews a classical image feature extraction algorithm, namely SIFT (i.e. Scale Invariant Feature Transform) and modifies it in order to increase its repeatability score. We are using an approach that is inspired from another computer vision algorithm, namely FAST. The tests presented in the evaluation section show that our approach (i.e. SIFT-FAST) obtains better repeatability scores over classical SIFT.

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Published
2017-12-15
How to Cite
DEAK, Renata; STERCA, Adrian; BĂDĂRÎNZĂ, Ioan. Improving SIFT for Image Feature Extraction. Studia Universitatis Babeș-Bolyai Informatica, [S.l.], v. 62, n. 2, p. 17-31, dec. 2017. ISSN 2065-9601. Available at: <http://www.cs.ubbcluj.ro/~studia-i/journal/journal/article/view/11>. Date accessed: 29 nov. 2020. doi: https://doi.org/10.24193/subbi.2017.2.02.
Section
Articles