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Segmentation techniques in image analysis: A comparative study

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Segmentation techniques in image analysis: A comparative study

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dc.contributor.author Vitale, Raffaele es_ES
dc.contributor.author Prats-Montalbán, José Manuel es_ES
dc.contributor.author López García, Fernando es_ES
dc.contributor.author BLASCO IVARS, JOSE es_ES
dc.contributor.author Ferrer, Alberto es_ES
dc.date.accessioned 2020-05-30T03:31:16Z
dc.date.available 2020-05-30T03:31:16Z
dc.date.issued 2016-12 es_ES
dc.identifier.issn 0886-9383 es_ES
dc.identifier.uri http://hdl.handle.net/10251/144680
dc.description.abstract [EN] Nowadays, the detection, localization, and quantification of different kinds of features in an RGB image (segmentation) is extremely helpful for, e.g., process monitoring or customer product acceptance. In this article, some of the most commonly used RGB image segmentation approaches are compared in an orange quality control case study. Analysis of variance and correspondence analysis are combined for determining their most relevant differences and highlighting their pros and cons. es_ES
dc.description.sponsorship Spanish Ministry of Economy and Competitiveness, Grant/Award Number: DPI2014-55276-C5-1R; Spanish National Institute for Agricultural and Food Research and Technology (INIA), Grant/Award Number: RTA2012-00062-C04-01; European Regional Development Fund (FEDER); Shell Global Solutions International B.V. es_ES
dc.language Inglés es_ES
dc.publisher John Wiley & Sons es_ES
dc.relation.ispartof Journal of Chemometrics es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Color information es_ES
dc.subject Graphs es_ES
dc.subject Multivariate image analysis (MIA) es_ES
dc.subject Segmentation es_ES
dc.subject Textural information es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.subject.classification ESTADISTICA E INVESTIGACION OPERATIVA es_ES
dc.title Segmentation techniques in image analysis: A comparative study es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1002/cem.2854 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//RTA2012-00062-C04-01/ES/Nuevas técnicas de inspección basadas en espectrometría para la estimación de propiedades y determinación automática de la calidad interna y sanidad de productos agroalimentarios aplicadas a líneas de inspección y manipulación (SPEC-DACSA)/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//DPI2014-55276-C5-1-R/ES/BIOLOGIA SINTETICA PARA LA MEJORA EN BIOPRODUCCION: DISEÑO, OPTIMIZACION, MONITORIZACION Y CONTROL/ es_ES
dc.rights.accessRights Abierto 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.contributor.affiliation Universitat Politècnica de València. Departamento de Mecanización y Tecnología Agraria - Departament de Mecanització i Tecnologia Agrària es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors es_ES
dc.description.bibliographicCitation Vitale, R.; Prats-Montalbán, JM.; López García, F.; Blasco Ivars, J.; Ferrer, A. (2016). Segmentation techniques in image analysis: A comparative study. Journal of Chemometrics. 30(12):749-758. https://doi.org/10.1002/cem.2854 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1002/cem.2854 es_ES
dc.description.upvformatpinicio 749 es_ES
dc.description.upvformatpfin 758 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 30 es_ES
dc.description.issue 12 es_ES
dc.relation.pasarela S\321987 es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder Shell Global Solutions International B.V. es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
dc.description.references Prats-Montalbán, J. M., de Juan, A., & Ferrer, A. (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.references Bevilacqua, M., Bucci, R., Magrì, A. D., Magrì, A. L., Nescatelli, R., & Marini, F. (2013). Classification and Class-Modelling. Chemometrics in Food Chemistry, 171-233. doi:10.1016/b978-0-444-59528-7.00005-3 es_ES
dc.description.references Manning, C. D., Raghavan, P., & Schutze, H. (2008). Introduction to Information Retrieval. doi:10.1017/cbo9780511809071 es_ES
dc.description.references MacQueen J Some methods for classification and analysis of multivariate observations Proceedings of the Berkeley Symposium on Mathematical Statistics and Probability Berkeley, CA University of California Press 1967 281 297 es_ES
dc.description.references Haralick, R. M. (1979). Statistical and structural approaches to texture. Proceedings of the IEEE, 67(5), 786-804. doi:10.1109/proc.1979.11328 es_ES
dc.description.references Felzenszwalb, P. F., & Huttenlocher, D. P. (2004). Efficient Graph-Based Image Segmentation. International Journal of Computer Vision, 59(2), 167-181. doi:10.1023/b:visi.0000022288.19776.77 es_ES
dc.description.references Barker, M., & Rayens, W. (2003). Partial least squares for discrimination. Journal of Chemometrics, 17(3), 166-173. doi:10.1002/cem.785 es_ES
dc.description.references Postma, G. J., Krooshof, P. W. T., & Buydens, L. M. C. (2011). Opening the kernel of kernel partial least squares and support vector machines. Analytica Chimica Acta, 705(1-2), 123-134. doi:10.1016/j.aca.2011.04.025 es_ES
dc.description.references Vitale, R., de Noord, O. E., & Ferrer, A. (2014). A kernel-based approach for fault diagnosis in batch processes. Journal of Chemometrics, 28(8), S697-S707. doi:10.1002/cem.2629 es_ES
dc.description.references Prats-Montalbán, J. M., & Ferrer, A. (2007). Integration of colour and textural information in multivariate image analysis: defect detection and classification issues. Journal of Chemometrics, 21(1-2), 10-23. doi:10.1002/cem.1026 es_ES
dc.description.references Prats-Montalbán J Control estadístico de procesos mediante análisis multivariante de imágenes Ph.D. Thesis 2005 es_ES
dc.description.references López, F., Prats, J. M., Ferrer, A., & Valiente, J. M. (2006). Defect Detection in Random Colour Textures Using the MIA T2 Defect Maps. Image Analysis and Recognition, 752-763. doi:10.1007/11867661_68 es_ES
dc.description.references Ho, P.-G. (Ed.). (2011). Image Segmentation. doi:10.5772/628 es_ES
dc.description.references Pal, N. R., & Pal, S. K. (1993). A review on image segmentation techniques. Pattern Recognition, 26(9), 1277-1294. doi:10.1016/0031-3203(93)90135-j es_ES
dc.description.references MATLAB R2012b (8.0.0.783), Natick, USA: The Mathworks Inc es_ES
dc.description.references Wold, S., Esbensen, K., & Geladi, P. (1987). Principal component analysis. Chemometrics and Intelligent Laboratory Systems, 2(1-3), 37-52. doi:10.1016/0169-7439(87)80084-9 es_ES
dc.description.references Geladi, P., & Kowalski, B. R. (1986). Partial least-squares regression: a tutorial. Analytica Chimica Acta, 185, 1-17. doi:10.1016/0003-2670(86)80028-9 es_ES
dc.description.references Cao, D.-S., Liang, Y.-Z., Xu, Q.-S., Hu, Q.-N., Zhang, L.-X., & Fu, G.-H. (2011). Exploring nonlinear relationships in chemical data using kernel-based methods. Chemometrics and Intelligent Laboratory Systems, 107(1), 106-115. doi:10.1016/j.chemolab.2011.02.004 es_ES
dc.description.references Vitale, R., de Noord, O. E., & Ferrer, A. (2015). Pseudo-sample based contribution plots: innovative tools for fault diagnosis in kernel-based batch process monitoring. Chemometrics and Intelligent Laboratory Systems, 149, 40-52. doi:10.1016/j.chemolab.2015.09.013 es_ES
dc.description.references Hirschfeld, H. O. (1935). A Connection between Correlation and Contingency. Mathematical Proceedings of the Cambridge Philosophical Society, 31(4), 520-524. doi:10.1017/s0305004100013517 es_ES


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