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Multivariate image analysis: a review with applications

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Multivariate image analysis: a review with applications

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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 MICINN/DPI2008-06880-C03-03 es_ES
dc.relation MICINN/CTQ2009-11572
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.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. doi: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.contributor.funder Ministerio de Ciencia e Innovación


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