Shape- and texture-based fish image recognition system
This research developed a computer system capable of recognizing some fish images. The system known as the "shape- and texture-based fish image recognition system" (FIRS) consists of five subsystems-namely: 1) image acquisition, 2) image preprocessing 3) feature extraction, 4) image recogn...
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th-mahidol.309512018-10-19T11:28:36Z Shape- and texture-based fish image recognition system Chomtip Pornpanomchai Benjamaporn Lurstwut Pimprapai Leerasakultham Waranat Kitiyanan Mahidol University Agricultural and Biological Sciences This research developed a computer system capable of recognizing some fish images. The system known as the "shape- and texture-based fish image recognition system" (FIRS) consists of five subsystems-namely: 1) image acquisition, 2) image preprocessing 3) feature extraction, 4) image recognition and 5) result presentation. The experiment was conducted on 30 fish species, which consisted of 600 fish images as the training dataset and 300 fish images for testing. The system compared two recognition techniques-a Euclidean distance method (EDM) and artificial neural networks (ANN). The system was able to recognize all 30 species of the training fish images with a precision of 99.00 and 81.67% for the ANN and the EDM techniques, respectively. The average access times were 24.4 and 154.43 sec per image for the EDM and ANN techniques, respectively. 2018-10-19T04:28:36Z 2018-10-19T04:28:36Z 2013-11-12 Article Kasetsart Journal - Natural Science. Vol.47, No.4 (2013), 624-634 00755192 2-s2.0-84887179880 https://repository.li.mahidol.ac.th/handle/123456789/30951 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84887179880&origin=inward |
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Agricultural and Biological Sciences Chomtip Pornpanomchai Benjamaporn Lurstwut Pimprapai Leerasakultham Waranat Kitiyanan Shape- and texture-based fish image recognition system |
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This research developed a computer system capable of recognizing some fish images. The system known as the "shape- and texture-based fish image recognition system" (FIRS) consists of five subsystems-namely: 1) image acquisition, 2) image preprocessing 3) feature extraction, 4) image recognition and 5) result presentation. The experiment was conducted on 30 fish species, which consisted of 600 fish images as the training dataset and 300 fish images for testing. The system compared two recognition techniques-a Euclidean distance method (EDM) and artificial neural networks (ANN). The system was able to recognize all 30 species of the training fish images with a precision of 99.00 and 81.67% for the ANN and the EDM techniques, respectively. The average access times were 24.4 and 154.43 sec per image for the EDM and ANN techniques, respectively. |
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Mahidol University |
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Mahidol University Chomtip Pornpanomchai Benjamaporn Lurstwut Pimprapai Leerasakultham Waranat Kitiyanan |
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Article |
author |
Chomtip Pornpanomchai Benjamaporn Lurstwut Pimprapai Leerasakultham Waranat Kitiyanan |
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Chomtip Pornpanomchai |
title |
Shape- and texture-based fish image recognition system |
title_short |
Shape- and texture-based fish image recognition system |
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Shape- and texture-based fish image recognition system |
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Shape- and texture-based fish image recognition system |
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Shape- and texture-based fish image recognition system |
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shape- and texture-based fish image recognition system |
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2018 |
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https://repository.li.mahidol.ac.th/handle/123456789/30951 |
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1763495999184044032 |