System for Estimation of Pin Bone Positions in Pre-rigor Salmon

Thielemann, Jens T.; Kirkhus, Trine; Kavli, Tom; Schumann-Olsen, Henrik; Haugland, Oddmund; Westavik, Harry

doi:10.1007/978-3-540-74607-2_81

Cited by 14 publications

(5 citation statements)

References 2 publications

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“…Han and Shi [12] developed a method combining particle swarm clustering and morphological operations with high effectiveness in fi sh bone detection for radiographic images. Thielemann et al [13] presented an approach using texture analysis of surface image to forecast the locations where fi sh bones could be existent in the fi sh fi llet. Sivertsen et al [14] described the use of a ridge detection method to fi nd the centerline on cod fi llets for quality inspection.…”

Section: Literature Reviewmentioning

confidence: 99%

“…They evaluated the quality of fi sh fi llet is dependent on percent of fi sh fi llet and fi shbone region. From the above reviews of literatures, almost all of the proposed fi shbone detection methods were developed for fi sh fi llets [11][12][13][14][15][16] and through radiographic images [11][12]15]. Those developed systems are expensive for the X-ray related devices.…”

Section: Literature Reviewmentioning

confidence: 99%

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Creation of image models for inspecting defects in commercial dried fish floss

Lin¹,

Lin²,

Lin³

2020

J Appl Eng Science

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Fish fl oss is a chopped fi nely or mashed fi sh meat boiled in seasonings, then stir fry until the fi sh meat is arid and pulverous. In the making of commercial fi sh fl osses, defect inspection is conducted by expertise inspectors using their feeling of contact and sight which could cause misjudgments. When consumers eat fi sh fl oss with defects, it may cause harm to the health of consumers. Therefore, this study proposes an automated defect detection method and develop an optical inspection system for commercial dried fi sh fl oss. The proposed method applies the curvelet transform with low-pass energy fi ltering to remove the random patterns of background and delete the angle direction of background texture. The approximated and partial detailed components regarding defects and uniform background are preserved in the low and medium frequency bands. In the reconstructed image, the background random texture is attenuated and the defect areas are enhanced. Finally, the restored image can be easily segmented by an estimated threshold value into two categories namely dark defects, and white background. The experimental results show that the proposed method well balances the trade-off between the recall rate (82.11%) and precision rate (87.62%), and reaches an F-score of 84.78%, outperforming the traditional defect detection techniques in inspection of dried fi sh fl oss.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Creation of image models for inspecting defects in commercial dried fish floss

Lin¹,

Lin²,

Lin³

2020

J Appl Eng Science

View full text Add to dashboard Cite

show abstract

“…Lorencs et al (2009) elaborated on a general algorithm based on statistical features that can be used to detect fish bones, in which small vertical fragments are segmented in gray value images containing a varying undefined background, however, validation experiments are not reported. Thielemann et al (2007) presented an interesting method based on texture analysis of the surface image (not an X-ray image) to predict the positions where fish bones could be present in the fillet. In the literature review, we observed the lack of an approach that could automatically detect fish bones effectively.…”

Section: Introductionmentioning

confidence: 99%

Automated fish bone detection using X-ray imaging

Mery

Lillo

Loebel

et al. 2011

Journal of Food Engineering

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“…Deboning takes place 4–6 days after the fish has been slaughtered; this is due to rigor mortis phase, after death in which the fillet becomes inflexible (Thielemann et al 2007 ; Morison 2004 ). The pin bones cannot be pulled out during rigor mortis without breaking or damaging the fillet due to it being attached to tendons in the muscle (Mathiassen et al 2012 ).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Salmon (Salmo salar) and Rainbow Trout (Oncorhynchus mykiss) pin bones using textural analysis and micro X-ray computational tomography

et al. 2019

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Industrially, common problems arise with the deboning pin bone process, where Atlantic Salmon ( Salmo salar ) and Rainbow Trout ( Oncorhynchus mykiss ) fillets, post rigor , are subjected to a pulling process to remove the pin bones from the fillet. This study measured the length of pin bones from two species of fish and two different industrial graded weights, and then used a texture analyser and µCT X-ray to measure the pulling force, break point and volume of the pin bones of both species of fish. Results showed that salmon pin bones required significantly higher pulling force to remove pin bones from the fish fillet when compared with Trout pin bones. Interestingly Trout pin bones were significantly longer and stronger than Salmon pin bones, but had significantly lower volume. This research has progressed the issues surrounding pin boning industrially, however, more studies are required in order to understand if these differences affect the overall deboning pin bone process. Electronic supplementary material The online version of this article (10.1007/s13197-019-03803-9) contains supplementary material, which is available to authorized users.

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System for Estimation of Pin Bone Positions in Pre-rigor Salmon

Cited by 14 publications

References 2 publications

Creation of image models for inspecting defects in commercial dried fish floss

Creation of image models for inspecting defects in commercial dried fish floss

Automated fish bone detection using X-ray imaging

Evaluation of Salmon (Salmo salar) and Rainbow Trout (Oncorhynchus mykiss) pin bones using textural analysis and micro X-ray computational tomography

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