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基于Gabor小波的浮选泡沫图像纹理特征提取 预览 被引量:31

Flotation froth image texture feature extraction based on Gabor wavelets
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摘要 矿物浮选泡沫表面纹理是浮选工艺指标高低的重要指示器,为了获取泡沫图像纹理的细微差别进而对浮选生产状态进行机器识别,提出一种基于Gabor滤波的泡沫图像纹理特征提取方法。首先利用Gabor小波提取了泡沫图像多尺度与多方向上的泡沫纹理幅度谱(GMTR)与相位谱(GPTR);然后根据GMTR与GPTR的分布特征,通过估计GMTR的Gamma分布参数和计算GPTR的熵作为泡沫纹理特征参量;最后,利用所提取的泡沫纹理特征对浮选工业生产状态进行无监督的模糊聚类分析与有监督的生产状态识别。实验结果表明,该纹理特征提取方法能有效地获取各种浮选状态下泡沫表面纹理的细微差别,基于该纹理特征参量的浮选状态识别准确率高于90%。 The surface texture of flotation froth is an important indicator of flotation production performance,a method for froth image texture extraction based on Gabor filter is presented to obtain the distinctive representation of flotation froth images texture and then try to identify the flotation production states using the extracted texture parameters.First,two Gabor convolution features,i.e.Gabor magnitude texture representation (GMTR) and Gabor phase-based texture representation (GPTR) are captured using the convolution between multi-scale and multidirectional Gabor wavelet function and the froth images And then the distribution parameter of GMTR is characterized using Gamma density function and the entropy of GPTR is calculated;the distributions of GMTR and GPTR in all subbands are considered,and both of them are treated as texture feature parameters.The froth images captured from industrial field were used in experiments;the texture variables were extracted using this method and the industrial production states were clustered using unsupervised fuzzy clustering analysis and recognized using artificial neural network.Experiment results demonstrate that this method can extract the distinctive froth texture in various flotation states and the flotation production states recognition rate is grater than 90% using these texture parameters.
作者 刘金平 桂卫华 牟学民 唐朝晖 李建奇 Liu Jinping,Gui Weihua,Mu Xuemin,Tang Zhaohui,Li Jianqi(School of Information Science and Engineering,Central South University,Changsha 410083,China)
出处 《仪器仪表学报》 EI CAS CSCD 北大核心 2010年第8期 1769-1775,共7页 Chinese Journal of Scientific Instrument
基金 国家自然科学基金重点项目(60634020)资助
关键词 浮选泡沫图像 纹理特征 GABOR小波 GAMMA分布 聚类分析 flotation froth image texture feature Gabor wavelet Gamma distribution clustering analysis
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