A Machine Vision Approach for Recognizing Coastal Fish
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 r...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Asociación Española para la Inteligencia Artificial
2022-09-01
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| Series: | Inteligencia Artificial |
| Subjects: | |
| Online Access: | http://journal.iberamia.org/index.php/intartif/article/view/785 |
| _version_ | 1811342596692246528 |
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| author | Afiq Raihan Israt Sharmin B M Marjan Khan Md. Ismail Jabiullah Md. Tarek Habib |
| author_facet | Afiq Raihan Israt Sharmin B M Marjan Khan Md. Ismail Jabiullah Md. Tarek Habib |
| author_sort | Afiq Raihan |
| collection | DOAJ |
| description |
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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| first_indexed | 2024-04-13T19:13:50Z |
| format | Article |
| id | doaj.art-73252b24b6ec4ee58513dce3f4721791 |
| institution | Directory Open Access Journal |
| issn | 1137-3601 1988-3064 |
| language | English |
| last_indexed | 2024-04-13T19:13:50Z |
| publishDate | 2022-09-01 |
| publisher | Asociación Española para la Inteligencia Artificial |
| record_format | Article |
| series | Inteligencia Artificial |
| spelling | doaj.art-73252b24b6ec4ee58513dce3f47217912022-12-22T02:33:45ZengAsociación Española para la Inteligencia ArtificialInteligencia Artificial1137-36011988-30642022-09-012570A Machine Vision Approach for Recognizing Coastal Fish Afiq Raihan0Israt Sharmin1B M Marjan Khan2Md. Ismail Jabiullah3Md. Tarek Habib4Daffodil International UniversityDaffodil International UniversityDaffodil International UniversityDaffodil International UniversityDaffodil International University 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%. http://journal.iberamia.org/index.php/intartif/article/view/785Fish species recognitionMachine visionFeature extractionPrincipal component analysisk-nearest neighborPerformance metric |
| spellingShingle | Afiq Raihan Israt Sharmin B M Marjan Khan Md. Ismail Jabiullah Md. Tarek Habib A Machine Vision Approach for Recognizing Coastal Fish Inteligencia Artificial Fish species recognition Machine vision Feature extraction Principal component analysis k-nearest neighbor Performance metric |
| title | A Machine Vision Approach for Recognizing Coastal Fish |
| title_full | A Machine Vision Approach for Recognizing Coastal Fish |
| title_fullStr | A Machine Vision Approach for Recognizing Coastal Fish |
| title_full_unstemmed | A Machine Vision Approach for Recognizing Coastal Fish |
| title_short | A Machine Vision Approach for Recognizing Coastal Fish |
| title_sort | machine vision approach for recognizing coastal fish |
| topic | Fish species recognition Machine vision Feature extraction Principal component analysis k-nearest neighbor Performance metric |
| url | http://journal.iberamia.org/index.php/intartif/article/view/785 |
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