A New Edge Detection Method Based on Global Evaluation Using Supervised Classification Algorithms

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dc.contributor.authorFlores Vidal, Pablo Arcadio
dc.contributor.authorVillarino, Guillermo
dc.contributor.authorGómez, Daniel
dc.contributor.authorMontero, Javier
dc.date.accessioned2023-06-17T12:32:15Z
dc.date.available2023-06-17T12:32:15Z
dc.date.issued2019
dc.descriptionP. A. Flores-Vidal, G. Villarino, D. Gómez, and J. Montero, “A New Edge Detection Method Based on Global Evaluation Using Supervised Classification Algorithms,” vol. 12, no. 1, pp. 367–378, 2019, doi: https://doi.org/10.2991/ijcis.2019.125905653.
dc.description.abstractTraditionally, the last step of edge detection algorithms, which is called scaling-evaluation, produces the final output classifying each pixel as edge or nonedge. This last step is usually done based on local evaluation methods. The local evaluation makes this classification based on measures obtained for every pixel. By contrast, in this work, we propose a global evaluation approach based on the idea of edge list to produce a solution that suits more with the human perception. In particular, we propose a new evaluation method that can be combined with any classical edge detection algorithm in an easy way to produce a novel edge detection algorithm. The new global evaluation method is divided in four steps: in first place we build the edge lists, that we have called edge segments. In second place we extract the characteristics associated to each segment: length, intensity, location, and so on. In the third step we learn the characteristics that make a segment good enough to become an edge. At the fourth step, we apply the classification task. In this work we have built the ground truth of edge list necessary for the supervised classification. Finally, we test the effectiveness of this algorithm against other classical algorithms based on local evaluation approach.
dc.description.departmentDepto. de Estadística e Investigación Operativa
dc.description.facultyFac. de Ciencias Matemáticas
dc.description.refereedTRUE
dc.description.sponsorshipMinisterio de Economía, Industria y Competitividad (MINECO)
dc.description.sponsorshipComunidad de Madrid
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/63017
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dc.identifier.doi10.2991/ijcis.2019.125905653
dc.identifier.issn1875-6883
dc.identifier.officialurlhttps://doi.org/10.2991/ijcis.2019.125905653
dc.identifier.relatedurlhttps://www.atlantis-press.com/
dc.identifier.urihttps://hdl.handle.net/20.500.14352/12426
dc.issue.number1
dc.journal.titleInternational Journal of Computational Intelligence Systems
dc.language.isoeng
dc.page.final378
dc.page.initial367
dc.publisherAtlantis Press
dc.relation.projectIDTIN2015-66471-P
dc.relation.projectIDCASI-CAM-CM (S2013/ICE-2845)
dc.rightsAtribución-NoComercial 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttps://creativecommons.org/licenses/by-nc/3.0/es/
dc.subject.cdu51:004
dc.subject.keywordImage processing
dc.subject.keywordEdge detection
dc.subject.keywordGlobal evaluation
dc.subject.keywordEdge segments Supervised classification
dc.subject.ucmCibernética matemática
dc.subject.unesco1207.03 Cibernética
dc.titleA New Edge Detection Method Based on Global Evaluation Using Supervised Classification Algorithms
dc.typejournal article
dc.volume.number12
dspace.entity.typePublication
relation.isAuthorOfPublication881ba82f-e783-4e7e-a1b6-dded36681497
relation.isAuthorOfPublication.latestForDiscovery881ba82f-e783-4e7e-a1b6-dded36681497
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