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dc.contributor.authorDíaz, R.
dc.contributor.authorGil, L.
dc.contributor.authorSerrano, C.
dc.contributor.authorBlasco, María A.
dc.contributor.authorMoltó, Enrique
dc.contributor.authorBlasco, José
dc.date.accessioned2017-06-01T10:11:46Z
dc.date.available2017-06-01T10:11:46Z
dc.date.issued2004
dc.identifier.citationDiaz, R., Gil, L., Serrano, C., Blasco, M., Moltó, E., Blasco, J. (2004). Comparison of three algorithms in the classification of table olives by means of computer vision. Journal of Food Engineering, 61(1), 101-107.
dc.identifier.issn0260-8774
dc.identifier.urihttp://hdl.handle.net/20.500.11939/5138
dc.description.abstractThe classification of table olive in different quality categories is performed depending on the defects in the surface of the fruits. However, the characteristics of every category are not defined. Then, it is necessary to apply learning algorithms that allow the extraction of quality information from batches previously classified by expert workers. In this research, a colorimetric characterisation of the more common defects has been carried out. An image analysis system has been used to segment the parameter set with the information from the olives quality. Three different algorithms have been applied to classify the olives in four quality categories. The results show that a neural network with a hidden layer is able to classify the olives with an accuracy of over 90%, while partial least squares discriminant and Mahalanobis distance are over 70%. (C) 2003 Elsevier Ltd. All rights reserved.
dc.language.isoen
dc.titleComparison of three algorithms in the classification of table olives by means of computer vision
dc.typearticle
dc.authorAddressInstituto Valenciano de Investigaciones Agrarias (IVIA), Carretera CV-315, Km. 10’7, 46113 Moncada (Valencia), Españaes
dc.date.issuedFreeFormJAN 2004
dc.entidadIVIACentro de Agroingeniería
dc.identifier.doi10.1016/S0260-8774(03)00191-2
dc.journal.abbreviatedTitleJ.Food Eng.
dc.journal.issueNumber1
dc.journal.titleJournal of Food Engineering
dc.journal.volumeNumber61
dc.page.final107
dc.page.initial101
dc.rights.accessRightsopenAccess
dc.source.typeImpreso


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