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

Ebrahim Aghajari, D. Gharpure
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引用次数: 4

Abstract

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.
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基于图像传感器数据的水果质量检测模糊c均值聚类算法
本文提出了用流式细胞仪检测水果的方法。该方法首先采用RGB颜色模型提取水果图像。对成像传感器的输出进行预处理,得到适合评价的图像。提出了一种基于模糊c均值理论的水果质量检测算法。采用离散小波变换(DWT)提取特征。将DWT特征作为FCM算法的输入数据进行聚类和分割。提出了一种基于图像处理技术的水果品质评价方法。实验结果表明,模糊评价是一种可行的水果质量检验方法。
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