TY - CONF A1 - Lang, Jonas A1 - Becke, Mark A1 - Schlegl, Thomas A2 - Liu, Honghai A2 - Kubota, Naoyuki A2 - Zhu, Xiangyang A2 - Dillmann, RĂ¼diger A2 - Zhou, Dalin T1 - A Systematic Approach for the Parameterisation of the Kernel-Based Hough Transform Using a Human-Generated Ground Truth T2 - Intelligent Robotics and Applications N2 - 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. KW - Feature detection KW - Hough transform KW - Human-generated ground truth KW - Image processing KW - Line detection KW - Systematic parameterisation Y1 - 2015 UR - https://opus4.kobv.de/opus4-oth-regensburg/frontdoor/index/index/docId/4002 SN - 978-3-319-22878-5 VL - 9244 SP - 473 EP - 486 PB - Springer CY - Cham ER -