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קהילה:
אסיף מאגר המחקר החקלאי
פותח על ידי קלירמאש פתרונות בע"מ -
Real-time underwater sorting of edible fish species
Year:
2007
Authors :
אוסטרובסקי, ויאצ'סלב
;
.
אלחנתי, ויקטור
;
.
ברקי, אסף
;
.
ציון, בועז
;
.
קרפלוס, אילן
;
.
Volume :
56
Co-Authors:
Zion, B., Institute of Agricultural Engineering, Agricultural Research Organization, The Volcani Center, Bet Dagan, 50250, Israel
Alchanatis, V., Institute of Agricultural Engineering, Agricultural Research Organization, The Volcani Center, Bet Dagan, 50250, Israel
Ostrovsky, V., Institute of Agricultural Engineering, Agricultural Research Organization, The Volcani Center, Bet Dagan, 50250, Israel
Barki, A., Institute of Animal Science, Agricultural Research Organization, The Volcani Center, Bet Dagan, 50250, Israel
Karplus, I., Institute of Animal Science, Agricultural Research Organization, The Volcani Center, Bet Dagan, 50250, Israel
Facilitators :
From page:
34
To page:
45
(
Total pages:
12
)
Abstract:
Common carp (Cyprinus carpio), St. Peter's fish (Oreochromis sp.) and grey mullet (Mugil cephalus), were sorted according to species while swimming in pond water containing algae and suspended sediments. Fish images were acquired by a computer vision system while swimming through a narrow channel with their sides to the camera so that distance from the camera was relatively constant. Background illumination was used to overcome water opaqueness and to generate high image contrast. An algorithm extracted size- and orientation-invariant features from the fish silhouettes. Classification of the grey mullet, St. Peter's fish and carp images was achieved with a Bayes classifier, to accuracies of 98.9%, 94.2% and 97.7%, respectively. A real-time underwater computer vision system was tested in a pool in which fish swim through a narrow transparent unidirectional channel. Two sets, of 1701 and 2164 images, respectively, were analyzed with overall species recognition accuracy of 97.8% and 98.9%. © 2007 Elsevier B.V. All rights reserved.
Note:
Related Files :
algae
biodiversity
cichlid
computer vision
cyprinid
Cyprinidae
Cyprinus carpio
edible species
Fish species
עוד תגיות
תוכן קשור
More details
DOI :
10.1016/j.compag.2006.12.007
Article number:
0
Affiliations:
Database:
סקופוס
Publication Type:
מאמר
;
.
Language:
אנגלית
Editors' remarks:
ID:
21820
Last updated date:
02/03/2022 17:27
Creation date:
16/04/2018 23:47
Scientific Publication
Real-time underwater sorting of edible fish species
56
Zion, B., Institute of Agricultural Engineering, Agricultural Research Organization, The Volcani Center, Bet Dagan, 50250, Israel
Alchanatis, V., Institute of Agricultural Engineering, Agricultural Research Organization, The Volcani Center, Bet Dagan, 50250, Israel
Ostrovsky, V., Institute of Agricultural Engineering, Agricultural Research Organization, The Volcani Center, Bet Dagan, 50250, Israel
Barki, A., Institute of Animal Science, Agricultural Research Organization, The Volcani Center, Bet Dagan, 50250, Israel
Karplus, I., Institute of Animal Science, Agricultural Research Organization, The Volcani Center, Bet Dagan, 50250, Israel
Real-time underwater sorting of edible fish species
Common carp (Cyprinus carpio), St. Peter's fish (Oreochromis sp.) and grey mullet (Mugil cephalus), were sorted according to species while swimming in pond water containing algae and suspended sediments. Fish images were acquired by a computer vision system while swimming through a narrow channel with their sides to the camera so that distance from the camera was relatively constant. Background illumination was used to overcome water opaqueness and to generate high image contrast. An algorithm extracted size- and orientation-invariant features from the fish silhouettes. Classification of the grey mullet, St. Peter's fish and carp images was achieved with a Bayes classifier, to accuracies of 98.9%, 94.2% and 97.7%, respectively. A real-time underwater computer vision system was tested in a pool in which fish swim through a narrow transparent unidirectional channel. Two sets, of 1701 and 2164 images, respectively, were analyzed with overall species recognition accuracy of 97.8% and 98.9%. © 2007 Elsevier B.V. All rights reserved.
Scientific Publication
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