A multi-threshold approach and a realistic error measure for vanishing point detection in natural landscapes

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dc.contributor.author García-Faura, Álvaro
dc.contributor.author Fernández Martínez, Fernando
dc.contributor.author Kleinlein, Ricardo
dc.contributor.author San Segundo, Rubén
dc.contributor.author Díaz de María, Fernando
dc.date.accessioned 2020-09-16T16:29:40Z
dc.date.available 2021-10-01T23:00:05Z
dc.date.issued 2019-10-01
dc.identifier.bibliographicCitation Engineering Applications of Artificial Intelligence, (2019), 85, pp.: 713-726.
dc.identifier.issn 0952-1976
dc.identifier.uri http://hdl.handle.net/10016/30822
dc.description.abstract Vanishing Point (VP) detection is a computer vision task that can be useful in many different fields of application. In this work, we present a VP detection algorithm for natural landscape images based on an multi-threshold edge extraction process that combines several representations of an image, and on novel clustering and cluster refinement procedures. Our algorithm identifies a VP candidate in images with single-point perspective and improves detection results on two datasets that have already been tested for this task. Furthermore, we study how VP detection results have been reported in literature, pointing out the main drawbacks of previous approaches. To overcome these drawbacks, we present a novel error measure that is based on a probabilistic consistency measure between edges and VP hypothesis, and that can be tuned to vary the strictness on the results. Our reasoning on how our measure is more correct is supported by an intuitive analysis, simulations and an experimental validation.
dc.description.sponsorship The work leading to these results has been supported by the Span-ish Ministry of Economy and Competitiveness and the Ministry of Science, Innovation and Universities through the ESITUR (MINECO,RTC-2016-5305- 7), CAVIAR (MICINN, TEC2017-84593-C2-1-R), and AMIC (MICINN, TIN2017-85854-C4-4-R) projects (AEI/FEDER, UE).We also gratefully acknowledge the support of NVIDIA Corporation.
dc.format.extent 13
dc.language.iso eng
dc.publisher Elsevier
dc.rights © 2019 Elsevier Ltd. All rights reserved.
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Landscapes
dc.subject.other Multi-threshold
dc.subject.other Perspective
dc.subject.other Vanishing point
dc.title A multi-threshold approach and a realistic error measure for vanishing point detection in natural landscapes
dc.type article
dc.description.status Publicado
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.1016/j.engappai.2019.08.001
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TEC2017-84593-C2-1-R/CAVIAR
dc.relation.projectID Gobierno de España. RTC-2016-5305- 7/ESITUR
dc.relation.projectID Gobierno de España. TIN2017-85854-C4-4-R/AMIC
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 713
dc.identifier.publicationlastpage 726
dc.identifier.publicationtitle Engineering Applications of Artificial Intelligence
dc.identifier.publicationvolume 85
dc.identifier.uxxi AR/0000024534
dc.affiliation.dpto UC3M. Departamento de Teoría de la Señal y Comunicaciones
dc.affiliation.grupoinv UC3M. Grupo de Investigación: Procesado Multimedia
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