Publication Details

Fast corner point detection through machine learning

SCHAFFROTH Patrick and SVOBODA Pavel. Fast corner point detection through machine learning. In: Proceedings of the 17th Conference STUDENT EEICT 2011. Volume 3. Brno: Brno University of Technology, 2011, pp. 537-541. ISBN 978-80-214-4273-3.
Czech title
Rychlá detekce rohových bodů pomocí strojového učení
Type
conference paper
Language
english
Authors
Schaffroth Patrick (DCGM FIT BUT)
Svoboda Pavel, Ing., Ph.D. (DITS FIT BUT)
URL
Keywords

Corner point detection, Machine learning, Comparison of corner point detection methods

Abstract
The subject of this paper is the procedure of corner point detection using Machine learning algorithms.
The paper compares from many points of view the success of the classical corner point detector and the detector obtained by WaldBoost algorithm.
Annotation
Traditionally, corner point detection is performed through evaluation of some corner amplifying function and thresholding its results. Recently, an alternative machine learning-based approach was introduced. This contribution focuses on corner point detection through machine learning and proposes an approach that has good performance, low resource requirements, and is well implementable in parallel environments and programmable hardware. The paper also introduces the achieved results and discusses them.
Published
2011
Pages
537-541
Proceedings
Proceedings of the 17th Conference STUDENT EEICT 2011
Series
Volume 3
Conference
Student EEICT 2011, Brno, CZ
ISBN
978-80-214-4273-3
Publisher
Brno University of Technology
Place
Brno, CZ
BibTeX 
@INPROCEEDINGS{FITPUB9618,
   author = "Patrick Schaffroth and Pavel Svoboda",
   title = "Fast corner point detection through machine learning",
   pages = "537--541",
   booktitle = "Proceedings of the 17th Conference STUDENT EEICT 2011",
   series = "Volume 3",
   year = 2011,
   location = "Brno, CZ",
   publisher = "Brno University of Technology",
   ISBN = "978-80-214-4273-3",
   language = "english",
   url = "https://www.fit.vut.cz/research/publication/9618"
}
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