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dc.contributor.authorLópez-Rubio, Ezequiel 
dc.contributor.authorThurnhofer-Hemsi, Karl
dc.contributor.authorBlázquez-Parra, Elidia Beatriz 
dc.contributor.authorDe-Cózar-Macías, Óscar 
dc.contributor.authorLadrón-de-Guevara-Muñoz, María del Carmen 
dc.date.accessioned2024-01-23T10:19:11Z
dc.date.available2024-01-23T10:19:11Z
dc.date.created2024
dc.date.issued2018-07-18
dc.identifier.citationEzequiel 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 301-316es_ES
dc.identifier.issn0031-3203
dc.identifier.urihttps://hdl.handle.net/10630/29017
dc.description.abstractFitting 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.sponsorshipThis 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.isospaes_ES
dc.publisherElsevieres_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectAjuste de curvases_ES
dc.subjectGeometría analíticaes_ES
dc.subject.otherParabolic fittinges_ES
dc.subject.otherGeometric curve fittinges_ES
dc.subject.otherNoisees_ES
dc.subject.otherMinimization of absolute errorses_ES
dc.subject.otherRobust estimationes_ES
dc.titleA fast robust geometric fitting method for parabolic curves.es_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroE.T.S.I. Informáticaes_ES
dc.identifier.doi10.1016/j.patcog.2018.07.019
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersiones_ES


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