Unsupervised learning for ripeness estimation from grape seeds images

dc.creatorHernández-Alvarez, Sergio
dc.creatorMorales, L.
dc.creatorUrrutia-Sepúlveda, Angélica
dc.date2017-11-21T19:28:52Z
dc.date2017-11-21T19:28:52Z
dc.date2017
dc.date.accessioned2019-11-20T15:10:04Z
dc.date.available2019-11-20T15:10:04Z
dc.identifierhttp://repositorio.ucm.cl:8080/handle/ucm/1301
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3033144
dc.descriptionEstimating the current stage of grape ripeness is a crucial step in wine making and becomes especially important during harvesting. Visual inspection of grape seeds is one method to achieve this goal without performing chemical analysis, however this method is prone to failure. In this paper, we propose an unsupervised visual inspection system for grape ripeness estimation using the Dirichlet Mixture Model (DMM). Experimental analysis using real world data demonstrates that our approach can be used to estimate different ripeness stages from unlabeled grape seeds catalogs.
dc.languageen
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/
dc.sourceInternational Journal on Smart Sensing and Intelligent Systems, 10(3), 594-612
dc.subjectMixture model
dc.subjectGrape ripening
dc.subjectComputer vision
dc.titleUnsupervised learning for ripeness estimation from grape seeds images
dc.typeArtículos de revistas


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