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Fuzzy c-means clustering algorithm for quality inspection of fruits based on image sensors data

机译:基于图像传感器数据的水果质量检测的模糊C均值聚类算法

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摘要

Use of FCM for inspection of fruits is proposed in this paper. In this method, an image of fruits is firstly taken in RGB color model. The output of imaging sensors is preprocessed in order to get proper image for evaluation purpose. An algorithm based on fuzzy c-means theory was developed for quality inspection of fruits. Discrete Wavelet Transform (DWT) is applied in order to extract the features. The DWT features are used as input data to FCM algorithm to get clusters and segment the image. An evaluation method based on image processing techniques was developed for the purpose of evaluation quality of fruits. The experimental result of proposed method shows that fuzzy evaluation is a viable way for quality inspection of fruits.
机译:本文提出了使用FCM检验水果的方法。在这种方法中,首先以RGB颜色模型拍摄水果图像。成像传感器的输出经过预处理,以便获得适当的图像以进行评估。提出了一种基于模糊c均值理论的水果质量检测算法。应用离散小波变换(DWT)来提取特征。 DWT功能用作FCM算法的输入数据,以获取聚类并分割图像。为了评估水果的质量,开发了一种基于图像处理技术的评估方法。所提方法的实验结果表明,模糊评价是检测水果质量的可行方法。

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