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Image Processing Based Automatic Identification of Freshness in Fish Gill Tissues

机译:基于图像处理鱼鳃组织的自动识别

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Fish is one of the healthiest foods on the earth. The quality of the fish gets changed by the process involved in preserving and handling from buyer to seller. The degradation in the fish can be detected on the most vulnerable focal tissue like gill. This focal tissue has been automatically segmented from whole fish using image processing based technique. The statistical features were extracted from the segmented gill tissues. This paper presents a nondestructive image processing based technique for extracting the properties of stored fish gill tissues using Wavelet Transform. The imperative selection of the methodology makes the algorithm computationally fast, efficient, and automatic. The strategic selection of the discriminatory features from the fish image makes this a novel approach which is accurate for the detection of freshness in the sample under test. Experimental results show discriminatory variation pattern of statistical wavelet feature versus freshness of the fish. This prominent monotonic dissimilarity in the parameters provides a strategic framework for the identification of freshness in stored fish gill tissues. The computation speed is fast which makes this method efficient for real time application.
机译:鱼是地球上最健康的食物之一。通过从买方到卖方的过程所涉及和处理的过程来改变鱼类的质量。可以在像鳃上的最脆弱的焦点组织上检测鱼中的降解。使用基于图像处理的技术从整个鱼类自动分割了该焦组织。从分段的鳃组织中提取统计特征。本文介绍了基于非破坏性图像处理,用于使用小波变换提取储存的鱼鳃组织的性质。该方法的命令选择使算法能够计算快速,高效,自动。来自鱼类图像的歧视性特征的战略选择使得这种新的方法可以准确地检测正在测试的样品中的新鲜度。实验结果表明了统计小波特征与鱼类新鲜度的鉴别变异模式。参数中突出的单调异调提供了一种战略框架,用于鉴定储存的鱼鳃组织中的新鲜度。计算速度快速,这使得该方法有效地应用于实时应用。

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