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首页> 外文期刊>Journal of Aquatic Food Product Technology >Quality Evaluation of Alaska Pollock (Theragra chalcogramma) Roe by Image Analysis. Part I: Weight Prediction
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Quality Evaluation of Alaska Pollock (Theragra chalcogramma) Roe by Image Analysis. Part I: Weight Prediction

机译:通过图像分析对阿拉斯加鳕鱼子的质量进行评估。第一部分:体重预测

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Roe is an important product of the Alaska pollock (Theragra chalcogramma) industry. About 31% of the value for all pollock products comes from roe, yet roe is 5% of the weight of the fish. Currently, the size (weight), color, and maturity of the roe are subjectively evaluated. The objective of this study was to develop methods to predict the weight of Alaska pollock roe based on its view area from a camera and to differentiate between single and double roes. One hundred and forty-two pollock roes were picked from a processing line in a Kodiak, AK plant. Each roe was weighed, placed in a light box equipped with a digital video camera, images were taken at two different angles from one side, then turned over and presented at two different angles again (four images for each roe). A reference square of known surface area was placed by the roe. The following equations were used to fit the view area (X) versus weight (Y) data: linear, power, and second-order polynomial. Error rates for the classification of roes by weight decreased significantly when weight prediction equations for single and double roes were developed separately. A “turn angle” method, a “box” method, and a “modified box” method were tested to differentiate single and double roes by image analysis. Machine vision can accurately determine the weight of pollock roe.Practical Application Abstract: An image analysis method to accurately determine if pollock roe is a single or a double was developed. Then view area versus weight correlations were found for single and double roes that reduced incorrect weight classification rates to half that of human graders.View full textDownload full textKeywordsimage processing, pollock roe, single, double, weight classificationRelated var addthis_config = { ui_cobrand: "Taylor & Francis Online", services_compact: "citeulike,netvibes,twitter,technorati,delicious,linkedin,facebook,stumbleupon,digg,google,more", pubid: "ra-4dff56cd6bb1830b" }; Add to shortlist Link Permalink http://dx.doi.org/10.1080/10498850.2011.583377
机译:鱼卵是阿拉斯加狭鳕(Theragra chalcogramma)行业的重要产品。所有鳕鱼产品的价值中约31%来自鱼卵,而鱼卵占鱼重量的5%。目前,对卵的大小(重量),颜色和成熟度进行主观评估。这项研究的目的是开发一种方法,根据相机的视野来预测阿拉斯加鳕鱼子的重量,并区分单卵子和双卵子。从位于AK的科迪亚克工厂的一条生产线中挑出了142条鳕鱼子。将每个鱼籽称重,放置在配备有数字摄像机的灯箱中,从一侧以两个不同角度拍摄图像,然后翻转并以两个不同角度再次呈现(每个鱼籽四个图像)。鱼卵放置已知表面积的参考方格。以下方程式用于拟合视图区域(X)与权重(Y)数据:线性,幂和二阶多项式。当分别制定单双双鱼体重预测方程时,按重量对双鱼卵分类的错误率显着降低。测试了“转角”方法,“盒”方法和“修改后的盒”方法,以通过图像分析区分单卵和双卵。机器视觉可以准确地确定鳕鱼子的重量。实用应用摘要:提出了一种图像分析方法,可以准确地确定鳕鱼子是单卵还是双卵。然后,发现单卵和双卵的视野面积与体重的相关性将不正确的体重分类率降低到了平地机的一半。查看全文下载关键词图像处理,鳕鱼卵,单卵,双卵,体重分类相关var addthis_config = {ui_cobrand:“ Taylor &Francis Online”,services_compact:“ citeulike,netvibes,twitter,technorati,delicious,linkedin,facebook,stumbleupon,digg,google,更多”,发布号:“ ra-4dff56cd6bb1830b”};添加到候选列表链接永久链接http://dx.doi.org/10.1080/10498850.2011.583377

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