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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 II: Color Defects and Length Evaluation
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Quality Evaluation of Alaska Pollock (Theragra chalcogramma) Roe by Image Analysis. Part II: Color Defects and Length Evaluation

机译:通过图像分析对阿拉斯加鳕鱼子的质量进行评估。第二部分:颜色缺陷和长度评估

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摘要

In the second part of the study of the quality evaluation of pollock roe by image analysis, methods to quantify the color defects (green spots, dark strips, dark color, and uneven coloring due to “freezer burn”) were developed. Dark roes can be detected by their average L* value. Dark strips can be detected by quantifying the percentage of pixels that have an L* value below an L*threshold. Since there is wide variation among the average colors of the roes, this L*threshold value must be auto-adjusting to the color of the individual roe. Green spots can be detected by their darker color and by ignoring red blood vessels by setting an upper a*threshold. In this study, identifying pixels with L* values less than the L*threshold = 66% of the L*average of the roe, and a* values less than an a*threshold = 20 successfully detected dark strips and green spots. Detection and quantification of uneven color and “freezer burn” required a “smoothing” of the roe colors to reduce details. The “color primitives” method was used, with a setting of a color threshold (CT) = 75. The resulting images were analyzed by setting L*threshold values of 60, 65, 70, 75, 80, and 85% of L*average of individual roes. More surface area of the roe was judged as defective with increasing L*threshold. With proper selection of L*threshold, a*threshold, and CT value, image analysis can accurately quantify the color defects of pollock roe.Practical Application Abstract: Automation of pollock roe sorting by color would streamline the operation, reduce error rates, and help with standardization of quality. Combined with other capabilities of machine vision such as sorting by weight, this technology can be used for multiple purposes simultaneously.View full textDownload full textKeywordsimage processing, pollock roe, green spot, color defect, freezer burnRelated 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/10.1080/10498850.2011.583378
机译:在通过图像分析对鳕鱼子质量进行评估的研究的第二部分中,开发了量化颜色缺陷(绿色斑点,深色条纹,深色和“冰柜灼伤”导致的颜色不均匀)的方法。暗ro鱼可以通过其平均L *值进行检测。可以通过量化L *值低于L * threshold 的像素百分比来检测暗条。由于鱼卵的平均颜色之间存在很大差异,因此该L * threshold 值必须自动调整为单个鱼卵的颜色。可以通过设置较深的a * threshold 阈值来检测绿色斑点,使其颜色更深,而忽略红色血管。在这项研究中,确定L *值小于L * 阈值 =卵子L * 平均值的66%且a *值小于a的像素* threshold = 20个成功检测到的黑条和绿点。对不均匀颜色和“冰柜烧伤”的检测和量化要求鱼卵颜色“平滑”,以减少细节。使用“颜色原语”方法,并将颜色阈值(CT)设置为75。通过将L * threshold 值设置为60、65、70,单个子代L * 平均值的75、80和85%。随着L * 阈值的增加,鱼籽的更大表面积被认为是次品。通过正确选择L * 阈值,a * 阈值和CT值,图像分析可以准确地量化花粉的颜色缺陷。按颜色排序将简化操作,降低错误率,并有助于质量标准化。结合机器视觉的其他功能(例如按重量排序),该技术可以同时用于多种目的。查看全文下载全文关键词图像处理,鳕鱼子,绿点,颜色缺陷,冷冻柜燃烧相关的var addthis_config = {ui_cobrand:“ Taylor&弗朗西斯在线”,services_compact:“ citeulike,netvibes,twitter,technorati,美味,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/10.1080/10498850.2011.583378

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