Automated testing of aluminum castings using classifier fusion strategies

Simple item page
dc.contributor.authorMery, D
dc.contributor.authorChacon, M
dc.contributor.authorGonzalez, L
dc.contributor.authorMunoz, L
dc.date.accessioned2024-01-10T13:46:16Z
dc.date.available2024-01-10T13:46:16Z
dc.date.issued2005
dc.description.abstractGenerally, discontinuity detection in automated visual testing consists of two steps: identification of potential discontinuities using image processing techniques and classification of potential discontinuities into discontinuities and regular structures (false alarms) using a pattern recognition methodology. In the second step, since several features cyan be extracted from the potential discontinuities, a feature selection must be performed. In this paper, several known classifiers are studied in automated visual testing: threshold, euclidean, mahalanobis, polynomial, support vector machine and neural network classifiers. First, the performance of the classifiers is assessed individually. Second, the classifiers are combined in order to improve their performance. Seven fusion strategies in the combination were tested: and, or, majority vote, product, sum, maximum and median. The proposed methodology was tested on real data acquired from 50 noisy radiographic images of aluminum wheels, where 23 000 potential discontinuities (with only 60 real discontinuities) were segmented and 405 features were extracted for each potential discontinuity. Using fusion of classifiers, a very good performance was achieved, yielding a sensitivity of 100% and specificity of 99.97%.
dc.format.extent6 páginas
dc.fuente.origenWOS
dc.identifier.issn0025-5327
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/79141
dc.identifier.wosidWOS:000226762100005
dc.information.autorucIngeniería;Mery D;S/I;102382
dc.issue.numero2
dc.language.isoen
dc.nota.accesoSin adjunto
dc.pagina.final153
dc.pagina.inicio148
dc.publisherAMER SOC NONDESTRUCTIVE TEST
dc.revistaMATERIALS EVALUATION
dc.rightsregistro bibliográfico
dc.subjectautomated visual testing
dc.subjectfusion of classifiers
dc.subjectaluminum castings
dc.subjectradiography
dc.subjectPATTERN-RECOGNITION
dc.subjectINSPECTION
dc.subject.ods11 Sustainable Cities and Communities
dc.subject.odspa11 Ciudades y comunidades sostenibles
dc.titleAutomated testing of aluminum castings using classifier fusion strategies
dc.typeartículo
dc.volumen63
sipa.codpersvinculados102382
sipa.indexWOS
sipa.indexScopus
sipa.trazabilidadCarga SIPA;09-01-2024
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
2024-06-12. Automated testing of aluminum castings using classifier fusion strategies.pdf
Size:
2.73 KB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Artículos de revistas