<?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>2007-0705</journal-id>
<journal-title><![CDATA[Nova scientia]]></journal-title>
<abbrev-journal-title><![CDATA[Nova scientia]]></abbrev-journal-title>
<issn>2007-0705</issn>
<publisher>
<publisher-name><![CDATA[Universidad de La Salle Bajío A. C., Coordinación de Investigación]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2007-07052020000100007</article-id>
<article-id pub-id-type="doi">10.21640/ns.v12i24.2152</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Crop Images Segmentation using Adaptive Morphologic Descriptors]]></article-title>
<article-title xml:lang="es"><![CDATA[Segmentación de imágenes de cultivos utilizando descriptores morfológicos adaptativos]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gil Rios]]></surname>
<given-names><![CDATA[Miguel Ángel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[López Hernández]]></surname>
<given-names><![CDATA[Juan Manuel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Juárez Ramírez]]></surname>
<given-names><![CDATA[Dolores]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ruiz Robledo]]></surname>
<given-names><![CDATA[María del Carmen]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Badillo Canchola]]></surname>
<given-names><![CDATA[Laura Paulina]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Aranda López]]></surname>
<given-names><![CDATA[Ariana]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Tecnológica de León Tecnologías de la Información y Comunicación ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad de Guanajuato División de Ciencias e Ingenierías ]]></institution>
<addr-line><![CDATA[Yuriria ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2020</year>
</pub-date>
<volume>12</volume>
<numero>24</numero>
<fpage>0</fpage>
<lpage>0</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-07052020000100007&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-07052020000100007&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-07052020000100007&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract This research is focused on the segmentation improvement of crop images by using adaptive morphologic descriptors instead of classic algorithms like K-means and the top-hat operator using predefined shapes like disk or diamond. Obtained results shows that using an adaptive morphologic descriptor improves the segmentation performance against the classic shapes like disc and diamond. In order to measure the process a set of 60 crop images was used including their respective ground-truth images. The images were segmented using the K-Means algorithm and the top-hat operator with the disk and diamond shapes at different sizes into a range to validate their performance. In order to generate the adaptive morphologic descriptor, the Univariated Marginal Distribution Algorithm was used with no constraints by exploring a range of different sizes. Also, performance metrics like receiver operating characteristic and accuracy rate were applied to the generated data in order to assess the results.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Esta investigación está enfocada en mejorar los resultados de segmentación de imágenes de cultivos mediante el uso de descriptores morfológicos adaptativos, en lugar de algoritmos clásicos o el uso del operador top-hat utilizando descriptores morfológicos predefinidos como el disco y el diamante. Una de las técnicas más utilizadas para la segmentación de imágenes de cultivos es el algoritmo K-Means. Sin embargo, este algoritmo tiene el inconveniente de llegar a diferentes resultados de segmentación en la misma imagen, dada su inicialización aleatoria. Adicionalmente, el tiempo requerido para segmentar imágenes de tamaño considerable, como lo son las fotografías de cultivos adquiridas principalmente mediante drones, es significativo. Los resultados obtenidos con la técnica propuesta son comparados contra los obtenidos por el algoritmo K-Means así como los resultados que se obtuvieron al utilizar descriptores morfológicos con formas predeterminadas de la literatura.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[image segmentation]]></kwd>
<kwd lng="en"><![CDATA[morphologic descriptor]]></kwd>
<kwd lng="en"><![CDATA[crop detection]]></kwd>
<kwd lng="en"><![CDATA[UMDA]]></kwd>
<kwd lng="es"><![CDATA[segmentación de imagen]]></kwd>
<kwd lng="es"><![CDATA[descriptor morfológico]]></kwd>
<kwd lng="es"><![CDATA[detección de cultivos]]></kwd>
<kwd lng="es"><![CDATA[UMDA]]></kwd>
</kwd-group>
</article-meta>
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