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dc.contributor.author | Prats-Montalbán, José Manuel | es_ES |
dc.contributor.author | De Juan, A. | es_ES |
dc.contributor.author | Ferrer Riquelme, Alberto José | es_ES |
dc.date.accessioned | 2014-10-14T11:30:28Z | |
dc.date.available | 2014-10-14T11:30:28Z | |
dc.date.issued | 2011-05 | |
dc.identifier.issn | 0169-7439 | |
dc.identifier.uri | http://hdl.handle.net/10251/43242 | |
dc.description.abstract | [EN] Nowadays, image analysis is becoming more important because of its ability to perform fast and non-invasive low-cost analysis on products and processes. Image analysis is a wide denomination that encloses classical studies on gray scale or RGB images, analysis of images collected using few spectral channels (sometimes called multispectral images) or, most recently, data treatments to deal with hyperspectral images, where the spectral direction is exploited in its full extension. Pioneering data treatments in image analysis were applied to simple images mainly for defect detection, segmentation and classification by the Computer Science community. From the late 80s, the chemometric community joined this field introducing powerful tools for image analysis, which were already in use for the study of classical spectroscopic data sets and were appropriately modified to fit the particular characteristics of image structures. These chemometric approaches adapt to images of all kinds, from the simplest to the hyperspectral images, and have provided new insights on the spatial and spectroscopic information of this kind of data sets. New fields open by the introduction of chemometrics on image analysis are exploratory image analysis, multivariate statistical process control (monitoring), multivariate image regression or image resolution. This paper reviews the different techniques developed in image analysis and shows the evolution in the information provided by the different methodologies, which has been heavily pushed by the increasing complexity of the image measurements in the spatial and, particularly, in the spectral direction. © 2011 Elsevier B.V. | es_ES |
dc.description.sponsorship | We would like to thank the Chemolab Editorial Board for inviting us to prepare this paper. This research was supported by the Spanish Government (Science and Innovation Ministry) under projects DPI2008-06880-C03-03 and CTQ2009-11572. | |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Chemometrics and Intelligent Laboratory Systems | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Multivariate image analysis | es_ES |
dc.subject | MIA | es_ES |
dc.subject | Multivariate image regression (MIR) | es_ES |
dc.subject | Texture | es_ES |
dc.subject | RGB | es_ES |
dc.subject | Multispectral images | es_ES |
dc.subject | Hyperspectral images | es_ES |
dc.subject | Image resolution | es_ES |
dc.subject | Calibration | es_ES |
dc.subject.classification | ESTADISTICA E INVESTIGACION OPERATIVA | es_ES |
dc.title | Multivariate image analysis: a review with applications | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.chemolab.2011.03.002 | |
dc.relation.projectID | info:eu-repo/grantAgreement/MICINN//DPI2008-06880-C03-03/ES/TECNICAS ESTADISTICAS MULTIVARIANTES PARA EL CONOCIMIENTO, MONITORIZACION Y OPTIMIZACION DE BIOPROCESOS/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MICINN//CTQ2009-11572/ | |
dc.rights.accessRights | Cerrado | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat | es_ES |
dc.description.bibliographicCitation | Prats-Montalbán, JM.; De Juan, A.; Ferrer Riquelme, AJ. (2011). Multivariate image analysis: a review with applications. Chemometrics and Intelligent Laboratory Systems. 107(1):1-23. https://doi.org/10.1016/j.chemolab.2011.03.002 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | http://dx.doi.org/10.1016/j.chemolab.2011.03.002 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 23 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 107 | es_ES |
dc.description.issue | 1 | es_ES |
dc.relation.senia | 41323 | |
dc.subject.asignatura | Análisis multivariante de imágenes 34479 / X - Máster universitario en ingeniería de análisis de datos, mejora de procesos y toma de decisiones 2138 | es_ES |
dc.contributor.funder | Ministerio de Ciencia e Innovación |