Determination of Volume of Alaska Pollock (<i>Theragra chalcogramma</i>) by Image Analysis

Balaban, Murat Ö.; Chombeau, Melanie; Gümüş, Bahar; Cırban, Dilşat

doi:10.1080/10498850.2010.531996

Cited by 8 publications

(2 citation statements)

References 12 publications

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“…Good correlations between predicted and measured volumes were found (Damar et al, 2006). Balaban et al (2011) used the cubic splines method to predict the volume of whole Alaskan pollock with good accuracy (r 2 =0.99). They took top view and side view images of whole fish.…”

Section: Size/volumementioning

confidence: 96%

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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Section: Size/volumementioning

confidence: 96%

Untitled

Gümüş¹,

Balaban²,

Ünlüsayın³

2011

Turk. J. Fish. Aquat. Sci.

Self Cite

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“…Balaban et al measured Alaska pollock (Theragra chalcogramma) by taking side and top view images and then estimating the body contour as a b-spline. This was then used to calculate the volume [27]. In contrast, Rantung et al measured the length, height, and width through side and top views of the fish and used these measurements as inputs that divide the fish into discrete elliptical discs [14].…”

Section: The Cost Of Transport Metricmentioning

confidence: 99%

Computer Vision Estimation of Physical Parameters and Its Application to Power Requirements of Natural and Artificial Swimmers

Coe

Gutschmidt

2021

Designs

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A useful measure of efficiency of transport in aquatic animals and autonomous underwater vehicles is cost of transport. Often, cost of transport data on specific animals or platforms is not readily available or does not fit specific use cases, but images are readily available. In this work, we present a methodology to synthesize such data without the need for a specimen or laboratory tests. We propose a computer vision in a methodology called Ika-Fit to determine important physical characteristics, such as surface area, slenderness ratio, and mass, that are used for a cost of transport model. The Ika-Fit method provides a good estimation of parameters when compared to biological data and robotic platforms. These parameters are estimated for existing engineered systems, and the model is compared to published data; the model is found to demonstrate higher accuracy using fewer parameters in estimating cost of transport over existing methods.

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