Automated Fish Bone Detection using X-ray Testing. Mery, D., Lillo, I., Loebel, H., V. Riffo, A. S., Cipriano, A., & Aguilera, J. Journal of Food Engineering, 105(3):485-492, 2011.
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Paper abstract bibtex
Paper abstract bibtex In countries where fish is often consumed, fish bones are some of the most frequently ingested foreign bodies encountered in foods. In the production of fish fillets, fish bone detection is performed by human inspection using their sense of touch and vision which can lead to misclassification. Effective detection of fish bones in the quality control process would help avoid this problem. For this reason, an X-ray machine vision approach to automatically detect fish bones in fish fillets was developed. This paper describes our approach and the corresponding experiments with salmon and trout fillets. In the experiments, salmon X-ray images using 10×10 pixels detection windows and 24 intensity features (selected from 279 features) were analyzed. The methodology was validated using representative fish bones and trouts provided by a salmon industry and yielded a detection performance of 99%. We believe that the proposed approach opens new possibilities in the field of automated visual inspection of salmon, trout and other similar fish.
@Article{ mery:etal:2011,
author = {D. Mery and I. Lillo and H. Loebel and V. Riffo, A. Soto
and A. Cipriano and JM. Aguilera},
title = {Automated Fish Bone Detection using X-ray Testing},
journal = {Journal of Food Engineering},
volume = {105},
number = {3},
pages = {485-492},
year = {2011},
abstract = {In countries where fish is often consumed, fish bones are
some of the most frequently ingested foreign bodies
encountered in foods. In the production of fish fillets,
fish bone detection is performed by human inspection using
their sense of touch and vision which can lead to
misclassification. Effective detection of fish bones in the
quality control process would help avoid this problem. For
this reason, an X-ray machine vision approach to
automatically detect fish bones in fish fillets was
developed. This paper describes our approach and the
corresponding experiments with salmon and trout fillets. In
the experiments, salmon X-ray images using 10×10 pixels
detection windows and 24 intensity features (selected from
279 features) were analyzed. The methodology was validated
using representative fish bones and trouts provided by a
salmon industry and yielded a detection performance of 99%.
We believe that the proposed approach opens new
possibilities in the field of automated visual inspection
of salmon, trout and other similar fish. },
url = {http://saturno.ing.puc.cl/media/papers_alvaro/2011-JFoodEng-SalmonX.pdf}
}Downloads: 0
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In the production of fish fillets, fish bone detection is performed by human inspection using their sense of touch and vision which can lead to misclassification. Effective detection of fish bones in the quality control process would help avoid this problem. For this reason, an X-ray machine vision approach to automatically detect fish bones in fish fillets was developed. This paper describes our approach and the corresponding experiments with salmon and trout fillets. In the experiments, salmon X-ray images using 10×10 pixels detection windows and 24 intensity features (selected from 279 features) were analyzed. The methodology was validated using representative fish bones and trouts provided by a salmon industry and yielded a detection performance of 99%. We believe that the proposed approach opens new possibilities in the field of automated visual inspection of salmon, trout and other similar fish. 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This paper describes our approach and the\n\t\t corresponding experiments with salmon and trout fillets. In\n\t\t the experiments, salmon X-ray images using 10×10 pixels\n\t\t detection windows and 24 intensity features (selected from\n\t\t 279 features) were analyzed. The methodology was validated\n\t\t using representative fish bones and trouts provided by a\n\t\t salmon industry and yielded a detection performance of 99%.\n\t\t We believe that the proposed approach opens new\n\t\t possibilities in the field of automated visual inspection\n\t\t of salmon, trout and other similar fish. },\n url\t\t= {http://saturno.ing.puc.cl/media/papers_alvaro/2011-JFoodEng-SalmonX.pdf}\n}\n\n","author_short":["Mery, D.","Lillo, I.","Loebel, H.","V. Riffo, A. 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