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Spectral Reflectance Reconstruction Using Fuzzy Logic System Training: Color Science Application

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Spectral Reflectance Reconstruction Using Fuzzy Logic System Training: Color Science Application

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dc.contributor.author Amiri, Morteza Maali es_ES
dc.contributor.author Garcia-Nieto, Sergio es_ES
dc.contributor.author Morillas, Samuel es_ES
dc.contributor.author Fairchild, Mark D. es_ES
dc.date.accessioned 2021-11-05T14:11:16Z
dc.date.available 2021-11-05T14:11:16Z
dc.date.issued 2020-09 es_ES
dc.identifier.uri http://hdl.handle.net/10251/176450
dc.description.abstract [EN] In this work, we address the problem of spectral reflectance recovery from both CIEXYZ and RGB values by means of a machine learning approach within the fuzzy logic framework, which constitutes the first application of fuzzy logic in these tasks. We train a fuzzy logic inference system using the Macbeth ColorChecker DC and we test its performance with a 130 sample target set made out of Artist's paints. As a result, we obtain a fuzzy logic inference system (FIS) that performs quite accurately. We have studied different parameter settings within the training to achieve a meaningful overfitting-free system. We compare the system performance against previous successful methods and we observe that both spectrally and colorimetrically our approach substantially outperforms these classical methods. In addition, from the FIS trained we extract the fuzzy rules that the system has learned, which provide insightful information about how the RGB/XYZ inputs are related to the outputs. That is to say that, once the system is trained, we extract the codified knowledge used to relate inputs and outputs. Thus, we are able to assign a physical and/or conceptual meaning to its performance that allows not only to understand the procedure applied by the system but also to acquire insight that in turn might lead to further improvements. In particular, we find that both trained systems use four reference spectral curves, with some similarities, that are combined in a non-linear way to predict spectral curves for other inputs. Notice that the possibility of being able to understand the method applied in the trained system is an interesting difference with respect to other 'black box' machine learning approaches such as the currently fashionable convolutional neural networks in which the downside is the impossibility to understand their ways of procedure. Another contribution of this work is to serve as an example of how, through the construction of a FIS, some knowledge relating inputs and outputs in ground truth datasets can be extracted so that an analogous strategy could be followed for other problems in color and spectral science. es_ES
dc.description.sponsorship Samuel Morillas acknowledges the support of the Spanish Ministry of Science under grants PRX17/00384, PRX16/00050 and PID2019-107790RB-C22. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Sensors es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Spectral recovery es_ES
dc.subject CIEXYZ es_ES
dc.subject RGB es_ES
dc.subject Fuzzy logic es_ES
dc.subject Fuzzy logic inference systems es_ES
dc.subject.classification MATEMATICA APLICADA es_ES
dc.subject.classification INGENIERIA DE SISTEMAS Y AUTOMATICA es_ES
dc.title Spectral Reflectance Reconstruction Using Fuzzy Logic System Training: Color Science Application es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/s20174726 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/Ministerio de Educación Cultura y Deporte//PRX16%2F00050//Medidas de similitud perceptual para imágenes en color/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/Ministerio de Educación Cultura y Deporte//PRX17%2F00384//Medidas de calidad perceptual para imágenes en color/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI//PID2019-107790RB-C22//DESARROLLO DEL SOFTWARE PARA UN SISTEMA PET DE CRISTAL CONTINUO APLICADO AL CANCER DE MAMA/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería de Sistemas y Automática - Departament d'Enginyeria de Sistemes i Automàtica es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada es_ES
dc.description.bibliographicCitation Amiri, MM.; Garcia-Nieto, S.; Morillas, S.; Fairchild, MD. (2020). Spectral Reflectance Reconstruction Using Fuzzy Logic System Training: Color Science Application. Sensors. 20(17):1-18. https://doi.org/10.3390/s20174726 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/s20174726 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 18 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 20 es_ES
dc.description.issue 17 es_ES
dc.identifier.eissn 1424-8220 es_ES
dc.identifier.pmid 32825676 es_ES
dc.identifier.pmcid PMC7506793 es_ES
dc.relation.pasarela S\417216 es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder Ministerio de Educación, Cultura y Deporte es_ES


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