System Development for Quality Evaluation of Fish Fillet Using Image Processing

Rerkratn, Apinai; Kaewpoonsuk, Anucha

doi:10.2991/ccit-14.2014.52

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…Mery et al [15] proposed an X-ray computer vision method to inspect fi sh bones in fi sh fi llets of salmon and trout. Rerkratn and Kaewpoonsuk [16] developed a system for quality assessment of fi sh fi llet employing K-means clustering and thresholding skills.…”

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

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“…About another specific quality evaluation of fish, Rekratn and Kaewpoonsuk proposed a new machine vision system to calculate area of fish filet and fish bone. The major steps of this system was using Ultraviolet lamp [8].…”

Section: Introductionmentioning

confidence: 99%

Individual Weight Estimation of Cynoglossus-gracilis Based on Measurement of Irregular Image Area

Ma¹,

Tian²,

Lin³

2016

Proceedings of the International Symposium on Mechanical Engineering and Material Science

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For aquaculture, the classification of live fish and the deconcentrition of fish into more ponds are very important process. Non-contact and no-harm are always the target of this process. Using the technology of digital image processing, the binary irregular image of Cynoglossus Gracilis is got and measurement; we analyze the relationship between the weight of fish and flat surface area by a linear regression model. The experimental results show that two parameters has a certain linear relationship, the correlation coefficient is 0.3807(R 2 =0.1450). Further research is needed to account for the effects of different growth stages of Cynoglossus gracilis body thickness and other factors, a more accurate model to identify the relationship between the two parameters, to provide support for the non-contact measurement of weight and classification.

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