A Machine Vision Approach for Recognizing Coastal Fish

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<dc:date>2022-12-05</dc:date>
<dc:description xml:lang="es">Sumario: Coastal fish is one of the prominent marine resources, which takes a necessary role in the economic growth of a country. Because of environmental issues along with other reasons, not only most of the marine resources are diminishing but also many coastal fishes are getting extinct gradually. As a result, the young peoples have insufficient knowledge of coastal fish. This issue can be solved with the use of vision-based technologies. To deal with this situation, a coastal fish recognition system based on machine vision is conceived, which can be approached by the images of coastal fish that are captured with a portable device and identify the fish to recognize fish. Numerous experimental analyses are executed to exhibit the benefit of this proposed expert system. In the beginning, conversion of a color image into a gray-scale image occurs and the gray-scale histogram is developed. Using the histogram-based method, image segmentation is conducted. After that, a set of thirteen features comprising of four classes is extracted to be fed to a classifier. For reducing the number of features, PCA is applied. To recognize coastal fish, three cutting-edge classifiers are performed, where k-NN provides a potential accuracy of up to 98.7%.

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<dc:identifier>https://documentacion.fundacionmapfre.org/documentacion/publico/es/bib/181723.do</dc:identifier>
<dc:language>eng</dc:language>
<dc:rights xml:lang="es">https://creativecommons.org/licenses/by-nc/4.0</dc:rights>
<dc:subject xml:lang="es">Inteligencia artificial</dc:subject>
<dc:subject xml:lang="es">Pesca</dc:subject>
<dc:type xml:lang="es">Artículos y capítulos</dc:type>
<dc:title xml:lang="es">A Machine Vision Approach for Recognizing Coastal Fish</dc:title>
<dc:relation xml:lang="es">En: Revista Iberoamericana de Inteligencia Artificial. -  : IBERAMIA, Sociedad Iberoamericana de Inteligencia Artificial , 2018- = ISSN 1988-3064. - 05/12/2022 Volumen 25 Número 70 - diciembre 2022 , p. 13-32</dc:relation>
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