Publication: A multi-threshold approach and a realistic error measure for vanishing point detection in natural landscapes
dc.affiliation.dpto | UC3M. Departamento de Teoría de la Señal y Comunicaciones | es |
dc.affiliation.grupoinv | UC3M. Grupo de Investigación: Procesado Multimedia | es |
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.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. | en |
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. | en |
dc.description.status | Publicado | es |
dc.format.extent | 13 | |
dc.identifier.bibliographicCitation | Engineering Applications of Artificial Intelligence, (2019), 85, pp.: 713-726. | en |
dc.identifier.doi | https://doi.org/10.1016/j.engappai.2019.08.001 | |
dc.identifier.issn | 0952-1976 | |
dc.identifier.publicationfirstpage | 713 | |
dc.identifier.publicationlastpage | 726 | |
dc.identifier.publicationtitle | Engineering Applications of Artificial Intelligence | en |
dc.identifier.publicationvolume | 85 | |
dc.identifier.uri | https://hdl.handle.net/10016/30822 | |
dc.identifier.uxxi | AR/0000024534 | |
dc.language.iso | eng | en |
dc.publisher | Elsevier | es |
dc.relation.projectID | Gobierno de España. TEC2017-84593-C2-1-R/CAVIAR | es |
dc.relation.projectID | Gobierno de España. RTC-2016-5305- 7/ESITUR | es |
dc.relation.projectID | Gobierno de España. TIN2017-85854-C4-4-R/AMIC | es |
dc.rights | © 2019 Elsevier Ltd. All rights reserved. | en |
dc.rights | Atribución-NoComercial-SinDerivadas 3.0 España | * |
dc.rights.accessRights | open access | en |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject.eciencia | Telecomunicaciones | es |
dc.subject.other | Landscapes | en |
dc.subject.other | Multi-threshold | en |
dc.subject.other | Perspective | en |
dc.subject.other | Vanishing point | en |
dc.title | A multi-threshold approach and a realistic error measure for vanishing point detection in natural landscapes | en |
dc.type | research article | * |
dc.type.hasVersion | AM | * |
dspace.entity.type | Publication |
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