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:2019,28(5):35-41
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基于改进模糊C均值聚类的光伏面板红外图像分割
(南昌大学 信息工程学院, 南昌 330031)
Infrared Image Segmentation of Photovoltaic Panel Based on Improved Fuzzy C-Means Clustering
(Information Engineering School, Nanchang University, Nanchang 330031, China)
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投稿时间:2018-11-27    修订日期:2018-12-18
中文摘要: 红外图像具有对比度低和信噪比低等特点,这对红外光伏面板图像的分割始终是一个巨大的挑战.为了解决传统的模糊C均值(FCM)聚类算法易受到初始聚类中心不确定的影响和不考虑空间信息的问题,提出了一种基于模糊C均值改进的聚类分割算法,该算法利用直方图的特点确定初始聚类中心,同时在传统的模糊C均值(FCM)和模糊核C均值算法(KFCM)的基础上,利用像素之间的空间信息和邻域像素之间的关系改进传统FCM聚类目标函数,从而推导出新的目标函数.实验结果表明,该算法在分割质量和效果上与Otsu算法、文献[20]的自适应k-means算法及模糊核C均值算法(KFCM)相比,过分割和错分割率明显降低,且分割效果非常接近手动分割图.
Abstract:Infrared images have low contrast and low signal-to-noise ratio, which is always a huge challenge for the segmentation of infrared photovoltaic panel images. In order to solve the problem that the traditional Fuzzy C-Means (FCM) clustering algorithm is susceptible to the uncertainty of the initial clustering center and does not consider the spatial information, a clustering algorithm based on FCM is proposed. The algorithm uses the histogram, meanwhile, the characteristics of the graph determine the initial clustering center, and based on the traditional FCM and Fuzzy Kernel C-Means (KFCM) algorithm, the traditional FCM is improved by the relationship between the spatial information among pixels and the neighboring pixels. The objective function is clustered to derive a new objective function. The experimental results show that the proposed algorithm has significantly lower over-segmentation and mis-segmentation rate than the Otsu algorithm, the adaptive k-means algorithm, and KFCM algorithm. The effect is very close to the manual segmentation map.
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基金项目:2017年第二批产学合作协同育人项目(201702109002)
引用文本:
洪向共,周世芬.基于改进模糊C均值聚类的光伏面板红外图像分割.计算机系统应用,2019,28(5):35-41
HONG Xiang-Gong,ZHOU Shi-Fen.Infrared Image Segmentation of Photovoltaic Panel Based on Improved Fuzzy C-Means Clustering.COMPUTER SYSTEMS APPLICATIONS,2019,28(5):35-41

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