<?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>1794-1237</journal-id>
<journal-title><![CDATA[Revista EIA]]></journal-title>
<abbrev-journal-title><![CDATA[Rev.EIA.Esc.Ing.Antioq]]></abbrev-journal-title>
<issn>1794-1237</issn>
<publisher>
<publisher-name><![CDATA[Escuela de ingenieria de Antioquia]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1794-12372018000200057</article-id>
<article-id pub-id-type="doi">10.24050/reia.v15i30.1150</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Visión por Computador aplicada al control de calidad en procesos de manufactura: seguimiento en tiempo real de refrigeradores]]></article-title>
<article-title xml:lang="en"><![CDATA[Computer Vision applied to quality control in manufacturing processes: real-time tracking of refrigerators]]></article-title>
<article-title xml:lang="pt"><![CDATA[Visão computacional aplicada ao controle de qualidade em processos de fabricação: monitoramento em tempo real de refrigeradores]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[López-Castaño]]></surname>
<given-names><![CDATA[Carlos Alberto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ferrin]]></surname>
<given-names><![CDATA[Carlos Diego]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Castillo]]></surname>
<given-names><![CDATA[Luis Fernando]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Nacional de Colombia  ]]></institution>
<addr-line><![CDATA[Manizales ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad del Valle  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Nacional de Colombia  ]]></institution>
<addr-line><![CDATA[Manizales ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2018</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2018</year>
</pub-date>
<volume>15</volume>
<numero>30</numero>
<fpage>57</fpage>
<lpage>71</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S1794-12372018000200057&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S1794-12372018000200057&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S1794-12372018000200057&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen En este artículo se presenta una propuesta de visión por computador para el problema de detección y seguimiento de accesorios internos en unidades de refrigeración sobre una línea de producción. La propuesta de visión por computador contempla una etapa de adquisición de imágenes multimodales (visible, infrarroja cercana y profundidad) provenientes de cuatro sensores, seguida de una etapa de segmentación para la extracción de contornos tanto de puertas como gabinete; estos contornos son seguidos mediante con ayuda de una etapa de seguimiento que permite que el sistema centre su atención en una sola nevera para pruebas individuales; una etapa final permite estimar las regiones de interés de los accesorios, en la nevera bajo prueba mediante una técnica de mapeo homográfico y la ayuda de un conjunto de accesorios registrados para cada modelo de unidad de refrigeración en una base de datos. El sistema de visión por computador fue evaluado sobre una arquitectura distribuida de alto desempeño con tres modelos diferentes de neveras en condiciones reales de producción. El desempeño y la efectividad de los algoritmos desarrollados demostraron ser adecuados para su aplicación en entornos reales de producción industrial.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract In this paper a computer vision based system is conceived and implemented for quality assurance of inner accessories in fridge-freezer during assembly process. The computer vision system includes a stage of acquisition of multimodal images (visible, near infrared and depth) from four sensors, followed by a segmentation stage for the extraction of contours both doors and cabinet; these contours are tracked by means of a tracking technique that allows the system to focus its attention on a single refrigerator for individual tests; a final stage allows to estimate the regions of interest of the accessories in the refrigerator under evaluation by means of a homographic mapping technique and the aid of a set of previously registered accessories for each refrigerator model in a database. The computer vision system was tested on a distributed and high performance computing architecture for three different models in a real scenario. Performance and effectiveness of the developed system demonstrated suitability for real time applications.]]></p></abstract>
<abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo Este artigo apresenta uma proposta de visão computacional para o problema de detecção e monitoramento de acessórios internos em unidades de refrigeração em uma linha de produção. A proposta de visão por computador contempla um estágio de aquisição de imagens multimodais (visível, infravermelho próximo e profundo) provenientes de quatro sensores, seguido de um estágio de segmentação para a extração de contornos e também de portas como gabinete; esses conteúdos foram seguidos com a ajuda de um estágio de acompanhamento que permite que o sistema concentre sua atenção em um único refrigerador para testes individuais; uma etapa final permite estimar as regiões de interesse dos acessórios, no frigorífico em teste por meio de uma técnica de mapeamento homográfico e a ajuda de um conjunto de acessórios para cada modelo da taxa de resfriamento em um banco de dados. O sistema de visão computacional foi avaliado em uma arquitetura distribuída de alto desempenho, nunca com modelos em condições reais de produção. O processo e a eficácia dos algoritmos mostraram-se adequados para aplicação em ambientes reais de produção industrial.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Control de Calidad]]></kwd>
<kwd lng="es"><![CDATA[Unidades de Refrigeración]]></kwd>
<kwd lng="es"><![CDATA[Visión por Computador]]></kwd>
<kwd lng="es"><![CDATA[Sistemas Distribuidos]]></kwd>
<kwd lng="es"><![CDATA[Computación de Alto Desempeño]]></kwd>
<kwd lng="en"><![CDATA[Quality Assurance]]></kwd>
<kwd lng="en"><![CDATA[Fridge-Freezer]]></kwd>
<kwd lng="en"><![CDATA[Computer Vision]]></kwd>
<kwd lng="en"><![CDATA[Distributed System]]></kwd>
<kwd lng="en"><![CDATA[High Performance Computing]]></kwd>
<kwd lng="pt"><![CDATA[Controle de Qualidade]]></kwd>
<kwd lng="pt"><![CDATA[Unidades de Refrigeração]]></kwd>
<kwd lng="pt"><![CDATA[Visão Computacional]]></kwd>
<kwd lng="pt"><![CDATA[Sistemas Distribuídos]]></kwd>
<kwd lng="pt"><![CDATA[Computação de Alto Desempenho]]></kwd>
</kwd-group>
</article-meta>
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