Unsupervised learning for ripeness estimation from grape seeds images

dc.contributor.author	Hernández-Alvarez, Sergio
dc.contributor.author	Morales, L.
dc.contributor.author	Urrutia-Sepúlveda, Angélica
dc.date.accessioned	2017-11-21T19:28:52Z
dc.date.available	2017-11-21T19:28:52Z
dc.date.issued	2017
dc.identifier.uri	http://repositorio.ucm.cl/handle/ucm/1301
dc.description.abstract	Estimating 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.	es_CL
dc.language.iso	en	es_CL
dc.rights	Atribución-NoComercial-SinDerivadas 3.0 Chile	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/cl/	*
dc.source	International Journal on Smart Sensing and Intelligent Systems, 10(3), 594-612	es_CL
dc.subject	Mixture model	es_CL
dc.subject	Grape ripening	es_CL
dc.subject	Computer vision	es_CL
dc.title	Unsupervised learning for ripeness estimation from grape seeds images	es_CL
dc.type	Article	es_CL
dc.ucm.facultad	Facultad de Ciencias de la Ingeniería	es_CL
dc.ucm.indexacion	Scopus	es_CL