Automated fish fry counting and schooling behavior analysis using computer vision
This paper presents an automated fish fry counting by detecting the pixel area occupied by each fish silhouette using image processing. A photo of the fish fry in a specially designed container undergoes binarization and edge detection. For every image frame, the total fish count is the sum of the a...
| Main Authors: | , , , , , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2013
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| Online Access: | https://hdl.handle.net/10356/100952 http://hdl.handle.net/10220/16738 |
| _version_ | 1826113250189639680 |
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| author | Labuguen, R. T. Volante, E. J. P. Bayot, R. Peren, G. Macaraig, R. M. Libatique, N. J. C. Tangonan, G. L. Causo, Albert |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Labuguen, R. T. Volante, E. J. P. Bayot, R. Peren, G. Macaraig, R. M. Libatique, N. J. C. Tangonan, G. L. Causo, Albert |
| author_sort | Labuguen, R. T. |
| collection | NTU |
| description | This paper presents an automated fish fry counting by detecting the pixel area occupied by each fish silhouette using image processing. A photo of the fish fry in a specially designed container undergoes binarization and edge detection. For every image frame, the total fish count is the sum of the area inside every contour. Then the average number of fishes for every frame is summed up. Experimental data shows that the accuracy rate of the method reaches above 95 percent for a school of 200, 400, 500, and 700 fish fry. To minimize errors due to crowding in the container, schooling behavior analysis is considered. The behavioral effects of different colored lights on milkfish and tilapia are thoroughly investigated. The system's effectiveness, efficiency, possible improvements, and other potential applications are discussed. |
| first_indexed | 2024-10-01T03:20:07Z |
| format | Conference Paper |
| id | ntu-10356/100952 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:20:07Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/1009522020-03-07T13:26:33Z Automated fish fry counting and schooling behavior analysis using computer vision Labuguen, R. T. Volante, E. J. P. Bayot, R. Peren, G. Macaraig, R. M. Libatique, N. J. C. Tangonan, G. L. Causo, Albert School of Mechanical and Aerospace Engineering International Colloquium on Signal Processing and its Applications (8th : 2012 : Melaka, Malaysia) This paper presents an automated fish fry counting by detecting the pixel area occupied by each fish silhouette using image processing. A photo of the fish fry in a specially designed container undergoes binarization and edge detection. For every image frame, the total fish count is the sum of the area inside every contour. Then the average number of fishes for every frame is summed up. Experimental data shows that the accuracy rate of the method reaches above 95 percent for a school of 200, 400, 500, and 700 fish fry. To minimize errors due to crowding in the container, schooling behavior analysis is considered. The behavioral effects of different colored lights on milkfish and tilapia are thoroughly investigated. The system's effectiveness, efficiency, possible improvements, and other potential applications are discussed. 2013-10-23T07:20:59Z 2019-12-06T20:31:23Z 2013-10-23T07:20:59Z 2019-12-06T20:31:23Z 2012 2012 Conference Paper Labuguen, R. T., Volante, E. J. P., Causo, A., Bayot, R., Peren, G., Macaraig, R. M., et al. (2012). Automated fish fry counting and schooling behavior analysis using computer vision. 2012 IEEE 8th International Colloquium on Signal Processing and its Applications (CSPA), 255-260. https://hdl.handle.net/10356/100952 http://hdl.handle.net/10220/16738 10.1109/CSPA.2012.6194729 en © 2012 IEEE |
| spellingShingle | Labuguen, R. T. Volante, E. J. P. Bayot, R. Peren, G. Macaraig, R. M. Libatique, N. J. C. Tangonan, G. L. Causo, Albert Automated fish fry counting and schooling behavior analysis using computer vision |
| title | Automated fish fry counting and schooling behavior analysis using computer vision |
| title_full | Automated fish fry counting and schooling behavior analysis using computer vision |
| title_fullStr | Automated fish fry counting and schooling behavior analysis using computer vision |
| title_full_unstemmed | Automated fish fry counting and schooling behavior analysis using computer vision |
| title_short | Automated fish fry counting and schooling behavior analysis using computer vision |
| title_sort | automated fish fry counting and schooling behavior analysis using computer vision |
| url | https://hdl.handle.net/10356/100952 http://hdl.handle.net/10220/16738 |
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