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首页> 外文期刊>Nonlinear dynamics >Segmentation of nutrient deficiency in incomplete crop images using intuitionistic fuzzy C-means clustering algorithm
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Segmentation of nutrient deficiency in incomplete crop images using intuitionistic fuzzy C-means clustering algorithm

机译：直觉模糊C-均值聚类算法对不完整作物图像中营养成分的分割

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This paper introduces a new segmentation technique to segment incomplete nutrient-deficient crop images by imputing missing pixels. Usually, each image contains pixels holding information about intensity, but sometimes image can miss some pixels (that is, the pixel without an appropriate intensity value). An image with missing pixels is called an incomplete image. Intuitionistic fuzzy clustering algorithm is a useful tool for clustering images, but it is not directly applicable for incomplete images. For example, segmentation of nutrient deficiency portions in the presence of missing pixels leads to error in segmentation. Crop images with nutrient deficiency might have missing pixels due to inherent defects in imaging equipment or due to environmental conditions. In this paper, nutrient deficiency in crop images is segmented after imputation of missing pixels based on intuitionistic fuzzy C-means color clustering algorithm. Experiments are performed on various incomplete crop images. Through the derived results and evaluated comparisons with other methods, namely K-means, fuzzy K-means, principal component analysis, regularized expectation maximization and fuzzy C-means algorithms, it has been proven that the proposed method performs well.
机译：本文介绍了一种新的分割技术，通过估算缺失像素来分割不完整的营养缺乏作物图像。通常，每个图像都包含保存有关强度的信息的像素，但是有时图像可能会丢失一些像素（即没有适当强度值的像素）。像素缺失的图像称为不完整图像。直觉模糊聚类算法是对图像进行聚类的有用工具，但不适用于不完整的图像。例如，在缺少像素的情况下分割营养缺乏部分会导致分割错误。营养不足的作物图像可能由于成像设备的固有缺陷或环境条件而缺少像素。本文基于直觉模糊C均值颜色聚类算法，对缺失像素进行插补后，对作物图像中的营养缺乏进行了分割。对各种不完整的作物图像进行了实验。通过得出的结果和与其他方法（即K均值，模糊K均值，主成分分析，正则期望最大化和模糊C均值算法）的比较，证明了该方法的有效性。

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