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Image processing based method to assess fish quality and freshness

机译:基于图像处理的评估鱼类质量和新鲜度的方法

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The quality of a fish may be affected primarily by handling, processing and storage procedures from the catch to consumers. Retention time and storage temperature of post-harvested fish are key factors for sustaining the final quality of this product. This paper proposes an image processing method which is completely automatic, efficient and non-destructive for segmentation of tissues and prediction of freshness of the fish sample. The gill tissues of the fish sample are automatically segmented using a clustering based method and its features are strategically extracted in the wavelet transformation domain using Haar filter. First, second and third level decomposition in the wavelet domain is performed and the coefficients obtained at each level have been analyzed to predict the freshness of the fish sample. The experimental results indicate a monotonic variation pattern of the coefficients at the third level of decomposition and these coefficients gives an indication of the quality of the fish. This discriminatory variation in the image features with the duration of retention time provides a strategic framework for assessment of fish freshness. (C) 2015 Elsevier Ltd. All rights reserved.
机译:鱼的质量可能主要受到从渔获物到消费者的处理,加工和储存程序的影响。捕捞后鱼的保留时间和储存温度是维持该产品最终质量的关键因素。本文提出了一种全自动,高效且无损的图像处理方法,用于组织分割和鱼样品新鲜度的预测。使用基于聚类的方法自动分割鱼样品的ill组织,并使用Haar滤波器在小波变换域中策略性地提取其特征。首先,在小波域中执行第二,第三和第三级分解,并分析了在每个级别获得的系数,以预测鱼样品的新鲜度。实验结果表明,在第三级分解中系数的单调变化模式,这些系数表明了鱼的质量。图像特征随保留时间长短的这种差异性变化为评估鱼类新鲜度提供了战略框架。 (C)2015 Elsevier Ltd.保留所有权利。

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