Conference paper

SCHAFFROTH Patrick and SVOBODA Pavel. Fast corner point detection through machine learning. In: Proceedings of the 17th Conference STUDENT EEICT 2011. Brno: Brno University of Technology, 2011, pp. 537-541. ISBN 978-80-214-4273-3.
Publication language:english
Original title:Fast corner point detection through machine learning
Title (cs):Rychlá detekce rohových bodů pomocí strojového učení
Pages:537-541
Proceedings:Proceedings of the 17th Conference STUDENT EEICT 2011
Conference:Student EEICT 2011
Series:Volume 3
Place:Brno, CZ
Year:2011
ISBN:978-80-214-4273-3
Publisher:Brno University of Technology
URL:http://www.feec.vutbr.cz/EEICT/2011/sbornik/03-Doktorske%20projekty/09-Grafika%20a%20multimedia/04-xsvobo59.pdf [PDF]
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Keywords
Corner point detection, Machine learning, Comparison of corner point detection methods
Annotation
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.
Abstract
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.
BibTeX:
@INPROCEEDINGS{
   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 = {http://www.fit.vutbr.cz/research/view_pub.php?id=9618}
}

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