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A fast robust geometric fitting method for parabolic curves.
dc.contributor.author | López-Rubio, Ezequiel | |
dc.contributor.author | Thurnhofer-Hemsi, Karl | |
dc.contributor.author | Blázquez-Parra, Elidia Beatriz | |
dc.contributor.author | De-Cózar-Macías, Óscar | |
dc.contributor.author | Ladrón-de-Guevara-Muñoz, María del Carmen | |
dc.date.accessioned | 2024-01-23T10:19:11Z | |
dc.date.available | 2024-01-23T10:19:11Z | |
dc.date.created | 2024 | |
dc.date.issued | 2018-07-18 | |
dc.identifier.citation | Ezequiel López-Rubio, Karl Thurnhofer-Hemsi, Elidia Beatriz Blázquez-Parra, Óscar David de Cózar-Macías, M. Carmen Ladrón-de-Guevara-Muñoz, A fast robust geometric fitting method for parabolic curves, Pattern Recognition, Volume 84, 2018, Pages 301-316 | es_ES |
dc.identifier.issn | 0031-3203 | |
dc.identifier.uri | https://hdl.handle.net/10630/29017 | |
dc.description.abstract | Fitting discrete data obtained by image acquisition devices to a curve is a common task in many fields of science and engineering. In particular, the parabola is some of the most employed shape features in electrical engineering and telecommunication applications. Standard curve fitting techniques to solve this problem involve the minimization of squared errors. However, most of these procedures are sensitive to noise. Here, we propose an algorithm based on the minimization of absolute errors accompanied by a normalization of the directrix vector that leads to an improved stability of the method. This way, our proposal is substantially resilient to noisy samples in the input dataset. Experimental results demonstrate the good performance of the algorithm in terms of speed and accuracy when compared to previous approaches, both for synthetic and real data. | es_ES |
dc.description.sponsorship | This work is partially supported by the Ministry of Economy and Competitiveness of Spain [grant number TIN2014-53465-R], project name Video surveillance by active search of anomalous events. It is also partially supported by the Autonomous Government of Andalusia (Spain) [grant number TIC-6213], project name Development of Self-Organizing Neural Networks for Information Technologies; and [grant number TIC-657], project name Self-organizing systems and robust estimators for video surveillance. All of them include funds from the European Regional Development Fund (ERDF). The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the SCBI (Supercomputing and Bioinformatics) center of the University of Málaga. They have also been supported by the Biomedic Research Institute of Málaga (IBIMA). They also gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X GPU. Karl Thurnhofer-Hemsi is funded by a Ph.D. scholarship from the Spanish Ministry of Education, Culture and Sport under the FPU program [grant number FPU15/06512]. | es_ES |
dc.language.iso | spa | es_ES |
dc.publisher | Elsevier | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.subject | Ajuste de curvas | es_ES |
dc.subject | Geometría analítica | es_ES |
dc.subject.other | Parabolic fitting | es_ES |
dc.subject.other | Geometric curve fitting | es_ES |
dc.subject.other | Noise | es_ES |
dc.subject.other | Minimization of absolute errors | es_ES |
dc.subject.other | Robust estimation | es_ES |
dc.title | A fast robust geometric fitting method for parabolic curves. | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.centro | E.T.S.I. Informática | es_ES |
dc.identifier.doi | 10.1016/j.patcog.2018.07.019 | |
dc.rights.cc | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | |
dc.type.hasVersion | info:eu-repo/semantics/acceptedVersion | es_ES |