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dc.date.accessioned 2019-03-15T14:10:48Z
dc.date.available 2019-03-15T14:10:48Z
dc.date.issued 2018
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/73213
dc.description.abstract Roads are composed of various sorts of materials and with the constant use they expose different kinds of cracks or potholes. The aim of the current research is to present a novel automated classification method to be applied on these faults, which can be located on rigid pavement type. In order to collect proper representation of faults, a Kinect device was used, leading to three-dimensional point cloud structures. Images descriptors were used in order to establish the type of pothole and to get information regarding fault dimensions. en
dc.format.extent 401-411 es
dc.language en es
dc.subject roads en
dc.subject automated classification method en
dc.subject Kinect device en
dc.title Recognition of Surface Irregularities on Roads: a machine learning approach on 3D models en
dc.type Objeto de conferencia es
sedici.identifier.isbn 978-950-658-472-6 es
sedici.creator.person Huincalef, Rodrigo es
sedici.creator.person Urrutia, Guillermo es
sedici.creator.person Ingravallo, Gabriel es
sedici.creator.person Martínez, Diego C. es
sedici.description.note XVI Workshop Computación Gráfica, Imágenes y Visualización (WCGIV) es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Red de Universidades con Carreras en Informática (RedUNCI) es
sedici.subtype Objeto de conferencia es
sedici.rights.license Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
sedici.rights.uri http://creativecommons.org/licenses/by-nc-sa/4.0/
sedici.date.exposure 2018-10
sedici.relation.event XXIV Congreso Argentino de Ciencias de la Computación (La Plata, 2018). es
sedici.description.peerReview peer-review es

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Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) Except where otherwise noted, this item's license is described as Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)