Vision Applications in the Fishing and Fish Product Industries

Arnarson, H.; Bengoetxea, Kepa; Pau, L. F.

doi:10.1142/s0218001488000418

Cited by 17 publications

(7 citation statements)

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“…Automation of fish processing with computer vision, apart from savings in labor costs, can bring also an overall improvement in the product quality (Arnarson and others 1988). In food industry, there has been a rapid growth over the past decade in development and use of noninvasive methods to evaluate quality (Lin and others 2003).…”

Section: Introductionmentioning

confidence: 99%

Computer Vision‐Based Sorting of Atlantic Salmon (Salmo salar) Fillets According to Their Color Level

Misimi¹,

Mathiassen²,

Erikson³

2007

Journal of Food Science

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Computer vision method was used to evaluate the color of Atlantic salmon (Salmo salar) fillets. Computer vision-based sorting of fillets according to their color was studied on 2 separate groups of salmon fillets. The images of fillets were captured using a digital camera of high resolution. Images of salmon fillets were then segmented in the regions of interest and analyzed in red, green, and blue (RGB) and CIE Lightness, redness, and yellowness (Lab) color spaces, and classified according to the Roche color card industrial standard. Comparisons of fillet color between visual evaluations were made by a panel of human inspectors, according to the Roche SalmoFan lineal standard, and the color scores generated from computer vision algorithm showed that there were no significant differences between the methods. Overall, computer vision can be used as a powerful tool to sort fillets by color in a fast and nondestructive manner. The low cost of implementing computer vision solutions creates the potential to replace manual labor in fish processing plants with automation.

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Section: Introductionmentioning

confidence: 99%

Computer Vision‐Based Sorting of Atlantic Salmon (Salmo salar) Fillets According to Their Color Level

Misimi¹,

Mathiassen²,

Erikson³

2007

Journal of Food Science

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“…Automation of fish processing with MV, apart from savings in labor costs, can also bring an overall improvement in the product quality (Arnarson et al, 1988). Although a large variety of examples of using CV in food industry have been reported (Panigrahi and Gunasekaran, 2001), the use of MV in automation of fish processing industry is still limited.…”

Section: Industrial Applications In Aquatic Foodsmentioning

confidence: 99%

Untitled

Gümüş¹,

Balaban²,

Ünlüsayın³

2011

Turk. J. Fish. Aquat. Sci.

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Machine vision (MV) is a rapid, economic, consistent and objective inspection and evaluation technique. This nondestructive method has applications in the aquatic food industry. MV can perform many functions at once in an aquatic food processing line: sorting by species, by size, and by visual quality attributes, as well as automated portioning. In this review, the mode of operation and the components of a MV system are introduced, its applications to foods are briefly discussed, and the advantages and disadvantages listed. The literature in the MV applications to aquatic foods is grouped under the following topics: determination of composition, measurement and evaluation of size and volume, measurement of shape parameters, quantification of the outside or meat color of aquatic foods, and detection of defects during quality evaluation. Finally, brief examples from the industrial applications of this promising technology are given. Extensive bibliography is cited in this field. ÖzetBilgisayarlı resim analizi (BRA); hızlı, ekonomik, tutarlı ve objektif olarak kontrol etme ve değerlendirme metodudur. Ürüne zarar vermeyen bu metodun, su ürünleri endüstrisine uygulamaları bulunmaktadır. BRA"nın otomatik porsiyonlama gibi, çoğu fonksiyonu veya ürünün türe, ağırlığa ve görsel kalite özelliklerine göre sınıflandırması su ürünleri işlemesinde, hızlı bir şekilde uygulanabilir. Bu derlemede, BRA sisteminin çalışma biçimi ve parçaları, kısaca gıdalara uygulanması, avantajları ve dezavantajları açıklanmaktadır. Su ürünlerine BRA uygulamalarının kaynakçaları; su ürünleri kompozisyonun belirlenmesi, ağırlık ve hacimin değerlendirmesi, şekil özelliklerinin ölçülmesi, su ürünlerinin et ya da yüzey renginin tanımlanması ve kalite değerlendirmesi sırasında istenmeyen kusurların belirlenmesi şeklindeki başlıklar altında gruplandırılmıştır. Sonuç olarak; gelecek için umut verici bu teknolojinin, endüstriyel uygulamalardaki bazı örnekleri verilmektedir. Bu konular derlemede kapsamlı kaynakça ile belirtilmektedir.Anahtar Kelimeler: Bilgisayarlı resim analizi, resim değerlendirme, su ürünleri.

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“…Codex standards (1981) exist for acceptable surface defects; in terms of the absolute area of such defects, they are, typically, 3, 5, and 10 cm 2 intervals. Surface defects can be detected using computer vision (Arnarson et al, 1988) but this requires a two face inspection.…”

Section: Vision Systemsmentioning

confidence: 99%

Robotics in Meat, Fish and Poultry Processing

Khodabandehloo¹

1993

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Vision Applications in the Fishing and Fish Product Industries

Cited by 17 publications

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Computer Vision‐Based Sorting of Atlantic Salmon (Salmo salar) Fillets According to Their Color Level

Computer Vision‐Based Sorting of Atlantic Salmon (Salmo salar) Fillets According to Their Color Level

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Robotics in Meat, Fish and Poultry Processing

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