<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>2215-3470</journal-id>
<journal-title><![CDATA[Uniciencia]]></journal-title>
<abbrev-journal-title><![CDATA[Uniciencia]]></abbrev-journal-title>
<issn>2215-3470</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional, Costa Rica]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2215-34702022000100559</article-id>
<article-id pub-id-type="doi">10.15359/ru.36-1.36</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[The transformation of RGB images to Munsell Soil-Color charts]]></article-title>
<article-title xml:lang="en"><![CDATA[Transformación de imágenes en RGB a tarjetas de color de suelo Munsell]]></article-title>
<article-title xml:lang="pt"><![CDATA[Conversão de imagens em RGB em cartas de cores do solo de Munsell]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Solís]]></surname>
<given-names><![CDATA[Martín]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Muñoz-Alvarado]]></surname>
<given-names><![CDATA[Erick]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pegalajar]]></surname>
<given-names><![CDATA[María Carmen]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Tecnológico de Costa Rica  ]]></institution>
<addr-line><![CDATA[Cartago ]]></addr-line>
<country>Costa Rica</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Tecnológico de Costa Rica Escuela de Ingeniería en Computadores ]]></institution>
<addr-line><![CDATA[Cartago ]]></addr-line>
<country>Costa Rica</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad de Granada Department of Computer Science and Artificial Intelligence ]]></institution>
<addr-line><![CDATA[Granada ]]></addr-line>
<country>Spain</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<volume>36</volume>
<numero>1</numero>
<fpage>559</fpage>
<lpage>568</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_arttext&amp;pid=S2215-34702022000100559&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_abstract&amp;pid=S2215-34702022000100559&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_pdf&amp;pid=S2215-34702022000100559&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  (Objetivo):  La transformación del espacio de color RGB al de color Munsell es un tema relevante para diferentes tareas como la identificación de: la taxonomía del suelo, materiales orgánicos, materiales rocosos. tipo de piel entre otros. Esta investigación tiene como objetivo desarrollar alternativas basadas en las redes feedforward y las Redes Neuronales Convolucionales para predecir el tono, el valor y el croma en las cartas de color del suelo de Munsell (MSCC) a partir de imágenes RGB.  (Metodología):  Con el fin de entrenar y probar los modelos, usamos imágenes de los gráficos de colores de suelo de Munsell de las versiones 2000 y 2009 tomadas de Millota et al. (2018). Se utilizó una división de 2856 imágenes en 10% para pruebas, 20% para validación y 70% para entrenamiento con miras a construir los modelos.  (Resultados):  El mejor enfoque fueron las redes neuronales convolucionales para la clasificación con un 93% de precisión total de la combinación de tono, valor y croma (consta de tres CNN, uno para la predicción de tono, otra para la de valor y la última para la de croma), aunque los tres mejores modelos muestran cercanía entre la predicción y los valores reales según la distancia CIEDE2000. Los casos clasificados incorrectamente con este enfoque tuvieron un promedio CIEDE2000 de 0.27 y una desviación estándar de 1.06.  (Conclusiones):  Los modelos demostraron un mejor reconocimiento de color en entornos no controlados que la transformación de Centore, la cual es el método clásico para transformar de RGB a HVC. Los resultados fueron prometedores, pero el modelo debe evaluarse ampliamente con imágenes reales del suelo para clasificar su color.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  (Objective):  The transformation from RGB to Munsell color space is a relevant issue for different tasks, such as the identification of soil taxonomy, organic materials, rock materials, skin types, among others. This research aims to develop alternatives based on feedforward networks and the convolutional neural networks to predict the hue, value, and chroma in the Munsell soil-color charts (MSCCs) from RGB images.  (Methodology):  We used images of Munsell soil-color charts from 2000 and 2009 versions taken from Millota et al. (2018) to train and test the models. A division of 2856 images in 10% for testing, 20% for validation, and 70% for training was used to build the models.  (Results):  The best approach was the convolutional neural networks for classification with 93% of total accuracy of hue, value, and chroma combination; it comprises three CNN, one for the hue prediction, another for value prediction, and the last one for chroma prediction. However, the three best models show closeness between the prediction and real values according to the CIEDE2000 distance. The cases classified incorrectly with this approach had a CIEDE2000 average of 0.27 and a standard deviation of 1.06.  (Conclusions):  The models demonstrated better color recognition in uncontrolled environments than the Transformation of Centore, which is the classical method to transform from RGB to HVC. The results were promising, but the model should be tested with real images at different applications, such as soil real images, to classify the soil color.]]></p></abstract>
<abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo  (Objetivo):  A conversão do espaço de cor RGB para o espaço de cores Munsell é um tema relevante para diferentes tarefas como a identificação: da taxonomia do solo, dos materiais orgânicos, dos materiais rochosos, do tipo de pele, dentre outros. Esta pesquisa tem como objetivo desenvolver alternativas baseadas nas redes feed-forward e nas Redes Neurais Convolucionais (CNN) para prever o matiz, o valor e o croma nas cartas de cores do solo de Munsell (MSCC) a partir de imagens RGB.  (Metodologia):  Para treinar e testar os modelos, usamos imagens dos gráficos de cores do solo de Munsell das versões 2000 e 2009 tomadas de Millota et al. (2018). Foi usada uma divisão de 2856 imagens em 10% para testes, 20% para validação e 70% para treinamento com o intuito de construir os modelos.  (Resultados):  O melhor enfoque foram as redes neurais convolucionais para a classificação com 93% de precisão total da combinação de matiz, valor e croma (consta de três CNN, um para a previsão de matiz, outra para a previsão de valor e a última para a previsão de croma), embora três melhores modelos tenham mostrado proximidade entre a previsão e os valores reais dependendo da distância CIEDE2000. Os casos classificados incorretamente com este enfoque tiveram uma média CIEDE2000 de 0,27 e um desvio padrão de 1,06.  (Conclusões):  Os modelos demonstraram um melhor reconhecimento de cor em ambientes não controlados que a conversão de Centore, que é o método clássico para converter de RGB a HVC. Os resultados foram prometedores, más o modelo deve ser amplamente avaliado com imagens reais de solo para classificar sua cor.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Munsell color space]]></kwd>
<kwd lng="en"><![CDATA[RGB color space]]></kwd>
<kwd lng="en"><![CDATA[transformation]]></kwd>
<kwd lng="en"><![CDATA[Munsell soil color charts]]></kwd>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
<kwd lng="en"><![CDATA[neuronal networks.]]></kwd>
<kwd lng="es"><![CDATA[espacio de color Munsell]]></kwd>
<kwd lng="es"><![CDATA[espacio de color RGB]]></kwd>
<kwd lng="es"><![CDATA[transformación]]></kwd>
<kwd lng="es"><![CDATA[cartas de color del suelo Munsell]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje automático]]></kwd>
<kwd lng="es"><![CDATA[redes neuronales]]></kwd>
<kwd lng="pt"><![CDATA[espaço de cor Munsell]]></kwd>
<kwd lng="pt"><![CDATA[espaço de cor RGB]]></kwd>
<kwd lng="pt"><![CDATA[conversão]]></kwd>
<kwd lng="pt"><![CDATA[cartas de cores do solo de Munsell]]></kwd>
<kwd lng="pt"><![CDATA[aprendizagem automática]]></kwd>
<kwd lng="pt"><![CDATA[redes neuronais]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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