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A Systematic Approach for the Parameterisation of the Kernel-Based Hough Transform Using a Human-Generated Ground Truth

  • Lines are one of the basic features that are used to characterise the content of an image and to detect objects. Unlike edges or segmented blobs, lines are not only an accumulation of certain feature pixels but can also be described in an easy and exact mathematical way. Besides a lot of different detection methods, the Hough transform has gained much attention in recent years. With increasing processing power and continuous development, computer vision algorithms get more powerful with respect to speed, robustness and accuracy. But there still arise problems when searching for the best parameters for an algorithm or when characterising and evaluating the results of feature detection tasks. It is often difficult to estimate the accuracy of an algorithm and the influences of the parameter selection. Highly interdependent parameters and preprocessing steps continually lead to only hardly comprehensible results. Therefore, instead of pure trial and error and subjective ratings, a systematic assessment with a hard, numerical evaluation criterion is suggested. The paper at hand deals with the latter ones by using a human-generated ground truth to approach the problem. Thereby, the accuracy of the surveyed Kernel-based Hough transform algorithm was improved by a factor of three. These results are used for the tracking of cylindrical markers and to reconstruct their spatial arrangement for a biomedical research application.

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Metadaten
Author:Jonas Lang, Mark Becke, Thomas SchleglORCiD
DOI:https://doi.org/10.1007/978-3-319-22879-2_44
ISBN:978-3-319-22878-5
Parent Title (English):Intelligent Robotics and Applications
Publisher:Springer
Place of publication:Cham
Editor:Honghai Liu, Naoyuki Kubota, Xiangyang Zhu, Rüdiger Dillmann, Dalin Zhou
Document Type:conference proceeding (article)
Language:English
Year of first Publication:2015
Release Date:2022/06/05
Tag:Feature detection; Hough transform; Human-generated ground truth; Image processing; Line detection; Systematic parameterisation
Volume:9244
First Page:473
Last Page:486
Konferenzangabe:International Conference on Intelligent Robotics and Applications (ICIRA 2015)
Institutes:Fakultät Maschinenbau
research focus:Produktion und Systeme
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG