泡沫图像统计建模及其在矿物浮选过程监控中的应用
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摘要
泡沫浮选是以表面化学为基础,根据矿物粒子表面疏水性的不同来选别有用矿物,是最重要的矿物分选方法。尽管泡沫浮选已有百年应用历史,但由于实际工业浮选流程长、过程影响因素多、内部机理不明确,矿物浮选过程自动监控仍然难以有效实施,造成矿物资源回收率低且指标波动频繁。鉴于工业过程视觉监控具有检测速度快、结果客观和不干扰浮选生产的优点,近年来,基于机器视觉的矿物浮选过程优化控制是进一步提高选矿自动化水平和矿产资源回收率的发展趋势。在基于机器视觉的矿物浮选过程监控中,研究合适的泡沫图像处理和分析方法以获取与生产工况密切相关的泡沫表面视觉特征参量是进行后续浮选过程建模与自动控制的基础。然而,由于浮选泡沫表面表现出来的随机堆积性、无背景性、形状不规则性等特点,常用的图像处理、分析方法难以有效实现泡沫表面视觉特征的准确测量与分析。为了能对这些形状各异、大小不一的、随机堆积的矿化气泡进行特征量化描述,本文将概率论、统计学习、模式识别等方法应用到图像信号处理和分析中,根据特定变换域的泡沫图像随机场所表现出来的统计分布特性建立合理的图像统计分布模型,为后续图像的分析和理解提供有效的先验知识；然后,基于所建立的统计模型,有效解决泡沫图像表面颜色、气泡大小和表面随机纹理等视觉特征难以准确表征问题,并成功应用于矿物浮选过程的泡沫状态分类与生产工况智能识别中,实现浮选生产工况的机器鉴别与自动评价。论文主要研究工作及创新点如下：
(1)针对浮选泡沫图像噪声大,泡沫表面视觉特征不能准确提取的难题,提出一种时空信息联合的图像序列多尺度几何变换去噪方法。该方法首先通过收集大量未受噪声干扰的泡沫图像样本进行图像统计分布建模,建立了泡沫图像多尺度几何变换域系数的统计分布模型；然后,以所建立的图像统计模型为先验知识,采用贝叶斯最小二乘估计方法获得基于帧内信息的泡沫图像去噪结果；最后,根据图像序列时空相关的信息,通过加权处理帧间图像去噪信号,实现了时空信息融合的图像序列最优无噪图像信号估计,解决了常规图像去噪方法中经常遇到的图像细节系数与图像噪声难以区分的难题。该方法在提高图像去噪效果的同时,极大限度地保持了图像边缘和表面纹理细节,为泡沫图像特征分析与理解提供了高质量的处理信号。
(2)针对泡沫图像因色偏严重而难以实现泡沫表面真实颜色的准确测量问题,提出一种基于图像空间结构统计分布的最优泡沫图像颜色自动校正方法。基于已有的图像光照估计方法,首先深入分析了图像边缘响应统计分布特征与图像最优光照估计方法间的关系,建立了基于图像边缘响应统计分布的图像入射光照最优估计模型；然后,根据入射光照的估计结果将发生色偏的泡沫图像自动校正到标准参考光照下的颜色表示。在进行泡沫图像光照估计时,以Ciurea和Funt建立的包含11346帧图像的Gray-ball标准光照数据库为训练样本,通过统计学习获得基于图像边缘响应分布的图像最优光照估计方法选择的混合高斯(MoG)模型,实现了图像最优光照估计方法的自动选取。实验表明,该方法能自动根据泡沫图像边缘响应的统计分布特点实现图像最优光照估计,进而有效地对泡沫图像颜色进行颜色校正,为泡沫表面颜色特征的准确提取与浮选生产工况的客观鉴别奠定了基础。
(3)针对矿物浮选过程加药操作缺乏有效评价方法的问题,根据气泡大小分布随药剂操作的改变而动态变化的特点,提出一种基于泡沫大小动态分布特征自适应学习的浮选过程药剂操作健康状态统计模式识别方法。首先根据泡沫图像局部区域像素的统计分布特点和矿化气泡的几何边缘特性,提出一种改进的泡沫图像分割方法,解决了因泡沫表面矿物粒子随机粘附造成矿化气泡上表面高亮点分散而引起泡沫图像的严重过分割问题；然后通过核密度估计获得浮选泡沫大小的累积分布函数(CDF)；再采用无监督的最远邻聚类(FNC)学习方法获得各典型药剂操作状态下气泡尺寸统计分布特征集；最后,根据测试时间段的浮选气泡大小分布的动态变化特点,采用贝叶斯推理获得对应的药剂操作健康状态识别结果,并自动根据浮选生产工况的波动情况对各典型药剂状态下的气泡统计分布特征集进行在线修正。所提出浮选药剂操作健康状态的统计识别方法,能实时跟踪泡沫大小分布的动态变化,进而根据气泡大小分布的变化情况实现浮选药剂操作健康状态的自动识别与客观评价,为实现浮选生产过程的加药量优化控制奠定基础。
(4)为了能进一步根据浮选泡沫表面纹理的细微差别实现浮选生产工况的自动鉴别与评价,提出一种基于泡沫图像多尺度多方向纹理表征的浮选生产工况综合分类与识别方法。鉴于二维Gabor基函数具有与绝大多数哺乳动物的视觉皮层简单细胞的感知域模型相似的性质,将Gabor小波变换应用到泡沫图像的多尺度多方向视觉特征提取中。分别分析了各分解子带上的图像实部谱(RGFR)、虚部谱(IGFR)、幅度谱(AGFR)和相位谱(PGFR)的统计分布特征,建立了RGFR、IGFR、AGFR和PGFR的边缘分布和图像相邻像素值的联合统计分布模型；分别采用t Location-Scale分布和Gamma分布模型来拟合RGFR, IGFR与AGFR的边缘分布,并计算各Gabor小波卷积谱的联合分布特征参量共同作为泡沫图像的表面纹理特征参量集；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。实验效果表明,该纹理特征提取方法有效地获取了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。
(5)以中国铝业有限公司中州分公司的铝土矿浮选过程监控为例,将所提出的图像建模、分析和识别方法应用到矿物浮选过程监控中。在铝土矿浮选现场设计并搭建了浮选泡沫图像采集、处理软硬件平台,实时提取了包括泡沫颜色、气泡大小及其分布、表面纹理等浮选泡沫图像表面视觉特征,根据泡沫表面视觉特征与生产工况的关系,获得了最佳精选泡沫图像的局部纹理特征区间,并最终实现了整个浮选流程中关键浮选槽泡沫状态的机器鉴别与客观评价。应用表明,监控系统所获得的泡沫特征曲线为生产工人提供了明确的工况信息,并给出具体的操作建议,避免了工人操作的盲目性,有效提高了矿物回收率并稳定了选矿生产指标,为浮选过程优化控制奠定了基础。图89幅,表10个,参考文献211篇。
Froth flotation is the most important mineral separation technology, which is used to concentrate the valuable minerals from the source ores according to the different surface hydrophobicities of the mineral particles based on the principle of surface hydrophobicities. Though one hundred years have elapsed, the automatic monitoring of the flotation process is still hard to be put into effect, resulting in low recovery of the mineral resource with fluctuating product indexes, sine the practical industrial flotation process usually consist of long process circuits and great amount of influence factors with unclear inner mechanism. In view of the merits of the industrial vision monitoring system, which responses fast and provides objective and non-intrusive monitoring of the froth states, machine vision based flotation process monitoring is recently expected as a promising tool to improve the online detection, measurement and control means and ultimately to achieve the optimal control the flotation process by both the scientific researchers and industrial engineers. In the machine vision based flotation process monitoring, researching the proper froth image processing methods and extracting the effective bubble features closely relating to the production condition of the flotation process are the prerequisites for subsequent flotation process modeling and optimization. However, the traditional image processing and analysis methods are difficult to be used in the froth image analysis and feature extraction due to the random accumulation of the mineralized bubbles, which have diverse morphological structures without background between each other. In order to quantitatively delineate the randomly accumulated bubbles with various shapes and sizes and obtain their comprehensive visual features, which do not depend on the single bubble in the froth surface, the methods such as probability theory, statistical learning and pattern recognition are applied to the froth image analysis and processing. The statistical distribution models of the froth image are established in the image transform domain according to the statistical distribution profile of the random signal characteristics for the subsequent image analysis and understanding to provide effective prior knowledge. The statistical characteristics of the froth image are studied by establishing reasonable mathematical models for image distribution feature description, which effectively solve the problems of accurate extraction of the forth surface color, bubble size and bubble surface texture. The extracted froth image statistics are successfully applied in the froth state classification and the intelligent production condition recognition in the mineral flotation process. The main researches and contributions are as follows:
(1) A spatio-temporal information fused froth image denoising method based on multi-scale geometric transformation of the froth image sequences is proposed, aiming at solving the problem of serious noises on the froth image, which may lead to inaccurate extraction of the froth surface features. Firstly, a great many of froth images are collected for statistically modeling the froth image coefficients in the multi-scale transform domain. Then, the recovery of the clean froth image signals is obtained by Bayesian least square estimation based on the intra-frame information with the established statistical model of the froth images as the prior knowledge. At last, by weighting the inter-frame recovery signal, it restores the uncontaminated image signal optimally based on temporal and spatial fused image sequence information. It solves the problem of the confusion of the image details and image noises, which is frequently existed in the commonly used image denoising methods. This method can remove the image noise while effectively keeps the edge curves and surface textures of froth image simultaneously, which provides high-quality image signals for the subsequent froth feature extraction.
(2) An adaptive froth image color correction method is presented based on the statistical distribution of the spatial structures of the images, since the froth images are prone to have the color cast. The correlationship of the edge response distribution features of the froth images and the corresponding optimal illumination estimation method is analysed in advance, which results in the establishment of the optimal scence illumination estimation model based on the statistical distribution of the edge responses of the froth images. Then, the forh image color will be corrected to the comparative color space under a canonical illumination according to the estimated results of the incident illumination color. During the illumination estimation, a well known image database for color constancy research including11346images with the known illumination built by Ciurea and Funt is used as the training samples to establish the distribution model of the image edge responses. After building the Mixture of Gasussian classification model based on the relation of the statistical distribution features of the images with their corresponding optimal illumination estimation method, it achieves the automatic selection of the optimal illumination color estimation methods. The experimental results demonstrate that this method is capable of achieving the optimal estimation of the illumination of the image and effectively correcting the froth image color, which paves the way of accurate color feature extraction of froth images and automatic identification of the flotation production conditions.
(3) An intelligent recognition method of the health states of the reagents operation is presented based on the adaptive learning of the dynamic distribution features of the froth bubble size distribution according to the fact of the bubble size distribution of flotation froth varies with the dynamic changes of the reagent operation, for lack of the effective method to monitor and assess the health states of the reagent operation. Firstly, an improved froth image segmentation algorithm is proposed based on the local pixel gray-level distribution of the froth images in combination with geometric characteristics of the bubble edge curves. It solves the problem of image over-segmentation resulting from the dispersed highlights caused by the light reflections of the mineral particles on the bubble surface. Then, the cumulative density functions (CDF) of the froth images are fitted through the statistics of the segmented region area with the kernel density estimation. The statistical distribution feature sets of the bubble size under the typical operational conditions are learned by unsupervised furthest neighbor clustering (FNC); subsequently, the current health state of the flotation process in the test time period is inferred by the Bayesian rules according to the dynamic change of the bubble size distribution; what is more, the bubble size distribution features extracting under the typical dosage addition conditions are updated timely according to the disturbance of the operation conditions. The proposed health state recognition method can track the dynamic change of the bubble size distribution and achieve the automatic recognition and evaluation of the reagent operation effectively, which lays a foundation for the realization of the optimal control of reagent addition in the flotation process operation.
(4) A kind of multi-scale and multi-orientation texture features of froth images based automatic operational condition classification and recognition method is proposed for the ultimate purpose of automatic identification and evaluation of the flotation production conditions according to the subtle change of the forth surface texture. The Gabor wavelet transformation is used to decompose the froth image into in advance in view of the two dimensional Gabor basis function can simulate the responses of the simple cells in the visual cortex of the most mammal brains. The convolution images including the real part (RGFR), imaginary part (IGFR), amplitude part (AGFR) and phase part of the Gabor filter responses (PGFR) are statistically analyzed respectively, whose marginal distribution and joint distribution features are both taken into account in this work. And then the distribution profiles of RGFR (IGFR) and AGFR are characterized by t Location-Scale and Gamma distribution respectively and the cumulative distribution features of each joint distribution of the Gabor filter responses are also calculated. Both the marginal distribution feature parameters and the joint distribution features of the Gabor filter responses at each sub-band are taken into account to construct the froth image texture feature parameter vector. At last, the extracted froth image texture variables are used to unsupervised clustering analysis and supervised recognition of the industrial production states. The experiment results demonstrated that this method can extract the distinctive froth texture in various flotation states and achieve high recognition rates of the flotation production states by these froth image texture parameters.
(5) The proposed image statistic analysis methods are applied in a bauxite flotation plant in the Zhongzhou branch of China limited aluminum corporation, where the flotation froth image acquisition device is designed and mounted by deploying the corresponding software system for image processing and process monitoring. The visual features of the froth image are extracted, including froth color, bubble size with size distribution and the froth surface texture, and so on. The optimal texture feature interval of the forth image is obtained in accordance with the relation of the froth image features and the production conditions. Consequently, the vision system can offer the real-time measurement and objective evaluation of the flotation process. The application results indicate that the extracted froth feature curves can offer operator useful production information with operation adjustment advice, which avoid the blindness of manual operation of the flotation process. To summarize, the established system with the proposed method improves the flotation performance efficiency and lays a foundation for the optimal control of the flotation process. There are89figures,10tables and211references in this dissertation.
(1)针对浮选泡沫图像噪声大,泡沫表面视觉特征不能准确提取的难题,提出一种时空信息联合的图像序列多尺度几何变换去噪方法。该方法首先通过收集大量未受噪声干扰的泡沫图像样本进行图像统计分布建模,建立了泡沫图像多尺度几何变换域系数的统计分布模型；然后,以所建立的图像统计模型为先验知识,采用贝叶斯最小二乘估计方法获得基于帧内信息的泡沫图像去噪结果；最后,根据图像序列时空相关的信息,通过加权处理帧间图像去噪信号,实现了时空信息融合的图像序列最优无噪图像信号估计,解决了常规图像去噪方法中经常遇到的图像细节系数与图像噪声难以区分的难题。该方法在提高图像去噪效果的同时,极大限度地保持了图像边缘和表面纹理细节,为泡沫图像特征分析与理解提供了高质量的处理信号。
(2)针对泡沫图像因色偏严重而难以实现泡沫表面真实颜色的准确测量问题,提出一种基于图像空间结构统计分布的最优泡沫图像颜色自动校正方法。基于已有的图像光照估计方法,首先深入分析了图像边缘响应统计分布特征与图像最优光照估计方法间的关系,建立了基于图像边缘响应统计分布的图像入射光照最优估计模型；然后,根据入射光照的估计结果将发生色偏的泡沫图像自动校正到标准参考光照下的颜色表示。在进行泡沫图像光照估计时,以Ciurea和Funt建立的包含11346帧图像的Gray-ball标准光照数据库为训练样本,通过统计学习获得基于图像边缘响应分布的图像最优光照估计方法选择的混合高斯(MoG)模型,实现了图像最优光照估计方法的自动选取。实验表明,该方法能自动根据泡沫图像边缘响应的统计分布特点实现图像最优光照估计,进而有效地对泡沫图像颜色进行颜色校正,为泡沫表面颜色特征的准确提取与浮选生产工况的客观鉴别奠定了基础。
(3)针对矿物浮选过程加药操作缺乏有效评价方法的问题,根据气泡大小分布随药剂操作的改变而动态变化的特点,提出一种基于泡沫大小动态分布特征自适应学习的浮选过程药剂操作健康状态统计模式识别方法。首先根据泡沫图像局部区域像素的统计分布特点和矿化气泡的几何边缘特性,提出一种改进的泡沫图像分割方法,解决了因泡沫表面矿物粒子随机粘附造成矿化气泡上表面高亮点分散而引起泡沫图像的严重过分割问题；然后通过核密度估计获得浮选泡沫大小的累积分布函数(CDF)；再采用无监督的最远邻聚类(FNC)学习方法获得各典型药剂操作状态下气泡尺寸统计分布特征集；最后,根据测试时间段的浮选气泡大小分布的动态变化特点,采用贝叶斯推理获得对应的药剂操作健康状态识别结果,并自动根据浮选生产工况的波动情况对各典型药剂状态下的气泡统计分布特征集进行在线修正。所提出浮选药剂操作健康状态的统计识别方法,能实时跟踪泡沫大小分布的动态变化,进而根据气泡大小分布的变化情况实现浮选药剂操作健康状态的自动识别与客观评价,为实现浮选生产过程的加药量优化控制奠定基础。
(4)为了能进一步根据浮选泡沫表面纹理的细微差别实现浮选生产工况的自动鉴别与评价,提出一种基于泡沫图像多尺度多方向纹理表征的浮选生产工况综合分类与识别方法。鉴于二维Gabor基函数具有与绝大多数哺乳动物的视觉皮层简单细胞的感知域模型相似的性质,将Gabor小波变换应用到泡沫图像的多尺度多方向视觉特征提取中。分别分析了各分解子带上的图像实部谱(RGFR)、虚部谱(IGFR)、幅度谱(AGFR)和相位谱(PGFR)的统计分布特征,建立了RGFR、IGFR、AGFR和PGFR的边缘分布和图像相邻像素值的联合统计分布模型；分别采用t Location-Scale分布和Gamma分布模型来拟合RGFR, IGFR与AGFR的边缘分布,并计算各Gabor小波卷积谱的联合分布特征参量共同作为泡沫图像的表面纹理特征参量集；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。实验效果表明,该纹理特征提取方法有效地获取了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。
(5)以中国铝业有限公司中州分公司的铝土矿浮选过程监控为例,将所提出的图像建模、分析和识别方法应用到矿物浮选过程监控中。在铝土矿浮选现场设计并搭建了浮选泡沫图像采集、处理软硬件平台,实时提取了包括泡沫颜色、气泡大小及其分布、表面纹理等浮选泡沫图像表面视觉特征,根据泡沫表面视觉特征与生产工况的关系,获得了最佳精选泡沫图像的局部纹理特征区间,并最终实现了整个浮选流程中关键浮选槽泡沫状态的机器鉴别与客观评价。应用表明,监控系统所获得的泡沫特征曲线为生产工人提供了明确的工况信息,并给出具体的操作建议,避免了工人操作的盲目性,有效提高了矿物回收率并稳定了选矿生产指标,为浮选过程优化控制奠定了基础。图89幅,表10个,参考文献211篇。
Froth flotation is the most important mineral separation technology, which is used to concentrate the valuable minerals from the source ores according to the different surface hydrophobicities of the mineral particles based on the principle of surface hydrophobicities. Though one hundred years have elapsed, the automatic monitoring of the flotation process is still hard to be put into effect, resulting in low recovery of the mineral resource with fluctuating product indexes, sine the practical industrial flotation process usually consist of long process circuits and great amount of influence factors with unclear inner mechanism. In view of the merits of the industrial vision monitoring system, which responses fast and provides objective and non-intrusive monitoring of the froth states, machine vision based flotation process monitoring is recently expected as a promising tool to improve the online detection, measurement and control means and ultimately to achieve the optimal control the flotation process by both the scientific researchers and industrial engineers. In the machine vision based flotation process monitoring, researching the proper froth image processing methods and extracting the effective bubble features closely relating to the production condition of the flotation process are the prerequisites for subsequent flotation process modeling and optimization. However, the traditional image processing and analysis methods are difficult to be used in the froth image analysis and feature extraction due to the random accumulation of the mineralized bubbles, which have diverse morphological structures without background between each other. In order to quantitatively delineate the randomly accumulated bubbles with various shapes and sizes and obtain their comprehensive visual features, which do not depend on the single bubble in the froth surface, the methods such as probability theory, statistical learning and pattern recognition are applied to the froth image analysis and processing. The statistical distribution models of the froth image are established in the image transform domain according to the statistical distribution profile of the random signal characteristics for the subsequent image analysis and understanding to provide effective prior knowledge. The statistical characteristics of the froth image are studied by establishing reasonable mathematical models for image distribution feature description, which effectively solve the problems of accurate extraction of the forth surface color, bubble size and bubble surface texture. The extracted froth image statistics are successfully applied in the froth state classification and the intelligent production condition recognition in the mineral flotation process. The main researches and contributions are as follows:
(1) A spatio-temporal information fused froth image denoising method based on multi-scale geometric transformation of the froth image sequences is proposed, aiming at solving the problem of serious noises on the froth image, which may lead to inaccurate extraction of the froth surface features. Firstly, a great many of froth images are collected for statistically modeling the froth image coefficients in the multi-scale transform domain. Then, the recovery of the clean froth image signals is obtained by Bayesian least square estimation based on the intra-frame information with the established statistical model of the froth images as the prior knowledge. At last, by weighting the inter-frame recovery signal, it restores the uncontaminated image signal optimally based on temporal and spatial fused image sequence information. It solves the problem of the confusion of the image details and image noises, which is frequently existed in the commonly used image denoising methods. This method can remove the image noise while effectively keeps the edge curves and surface textures of froth image simultaneously, which provides high-quality image signals for the subsequent froth feature extraction.
(2) An adaptive froth image color correction method is presented based on the statistical distribution of the spatial structures of the images, since the froth images are prone to have the color cast. The correlationship of the edge response distribution features of the froth images and the corresponding optimal illumination estimation method is analysed in advance, which results in the establishment of the optimal scence illumination estimation model based on the statistical distribution of the edge responses of the froth images. Then, the forh image color will be corrected to the comparative color space under a canonical illumination according to the estimated results of the incident illumination color. During the illumination estimation, a well known image database for color constancy research including11346images with the known illumination built by Ciurea and Funt is used as the training samples to establish the distribution model of the image edge responses. After building the Mixture of Gasussian classification model based on the relation of the statistical distribution features of the images with their corresponding optimal illumination estimation method, it achieves the automatic selection of the optimal illumination color estimation methods. The experimental results demonstrate that this method is capable of achieving the optimal estimation of the illumination of the image and effectively correcting the froth image color, which paves the way of accurate color feature extraction of froth images and automatic identification of the flotation production conditions.
(3) An intelligent recognition method of the health states of the reagents operation is presented based on the adaptive learning of the dynamic distribution features of the froth bubble size distribution according to the fact of the bubble size distribution of flotation froth varies with the dynamic changes of the reagent operation, for lack of the effective method to monitor and assess the health states of the reagent operation. Firstly, an improved froth image segmentation algorithm is proposed based on the local pixel gray-level distribution of the froth images in combination with geometric characteristics of the bubble edge curves. It solves the problem of image over-segmentation resulting from the dispersed highlights caused by the light reflections of the mineral particles on the bubble surface. Then, the cumulative density functions (CDF) of the froth images are fitted through the statistics of the segmented region area with the kernel density estimation. The statistical distribution feature sets of the bubble size under the typical operational conditions are learned by unsupervised furthest neighbor clustering (FNC); subsequently, the current health state of the flotation process in the test time period is inferred by the Bayesian rules according to the dynamic change of the bubble size distribution; what is more, the bubble size distribution features extracting under the typical dosage addition conditions are updated timely according to the disturbance of the operation conditions. The proposed health state recognition method can track the dynamic change of the bubble size distribution and achieve the automatic recognition and evaluation of the reagent operation effectively, which lays a foundation for the realization of the optimal control of reagent addition in the flotation process operation.
(4) A kind of multi-scale and multi-orientation texture features of froth images based automatic operational condition classification and recognition method is proposed for the ultimate purpose of automatic identification and evaluation of the flotation production conditions according to the subtle change of the forth surface texture. The Gabor wavelet transformation is used to decompose the froth image into in advance in view of the two dimensional Gabor basis function can simulate the responses of the simple cells in the visual cortex of the most mammal brains. The convolution images including the real part (RGFR), imaginary part (IGFR), amplitude part (AGFR) and phase part of the Gabor filter responses (PGFR) are statistically analyzed respectively, whose marginal distribution and joint distribution features are both taken into account in this work. And then the distribution profiles of RGFR (IGFR) and AGFR are characterized by t Location-Scale and Gamma distribution respectively and the cumulative distribution features of each joint distribution of the Gabor filter responses are also calculated. Both the marginal distribution feature parameters and the joint distribution features of the Gabor filter responses at each sub-band are taken into account to construct the froth image texture feature parameter vector. At last, the extracted froth image texture variables are used to unsupervised clustering analysis and supervised recognition of the industrial production states. The experiment results demonstrated that this method can extract the distinctive froth texture in various flotation states and achieve high recognition rates of the flotation production states by these froth image texture parameters.
(5) The proposed image statistic analysis methods are applied in a bauxite flotation plant in the Zhongzhou branch of China limited aluminum corporation, where the flotation froth image acquisition device is designed and mounted by deploying the corresponding software system for image processing and process monitoring. The visual features of the froth image are extracted, including froth color, bubble size with size distribution and the froth surface texture, and so on. The optimal texture feature interval of the forth image is obtained in accordance with the relation of the froth image features and the production conditions. Consequently, the vision system can offer the real-time measurement and objective evaluation of the flotation process. The application results indicate that the extracted froth feature curves can offer operator useful production information with operation adjustment advice, which avoid the blindness of manual operation of the flotation process. To summarize, the established system with the proposed method improves the flotation performance efficiency and lays a foundation for the optimal control of the flotation process. There are89figures,10tables and211references in this dissertation.
引文
[1]卢颖,孙胜义.我国矿山尾矿生产现状及综合治理利用[J].矿业工程,2007,5(2)：53-55.
[2]BENEVENTI D, BENESSE M, CARR B, et al. Modelling deinking selectivity in multistage flotation systems [J]. Separation and purification technology,2007, 54(1):77-87.
[3]FUERSTENAU D. The froth flotation century [J]. Advances in Flotation Technology SME, Littleton, CO, USA,1999:3-21.
[4]BERGH L Q YIANATOS J B. The long way toward multivariate predictive control of flotation processes [J]. Journal of Process Control,2011,21(2): 226-234.
[5]WATT J. On-stream analysis of metalliferous ore slurries [J]. The International Journal of Applied Radiation and Isotopes,1983,34(1):309-331.
[6]SOWERBY B, WATT J. Development of nuclear techniques for on-line analysis in the coal industry [J]. Nuclear Instruments and Methods in Physics Research Section A:Accelerators, Spectrometers, Detectors and Associated Equipment, 1990,299(1):642-647.
[7]周开军,阳春华,牟学民,et al.基于泡沫特征与LS-SVM的浮选回收率预测[J].仪器仪表学报,2009,30(6)：1295-1300.
[8]秦江波,于冬梅,孙永波.中国矿产资源现状与可持续发展研究[J].经济研究导刊,2011,(22)：11-12.
[9]MOOLMAN D, EKSTEEN J, ALDRICH C, et al. The significance of flotation froth appearance for machine vision control [J]. International Journal of Mineral Processing,1996,48(3):135-158.
[10]SHEAN B J, CILLIERS J J. A review of froth flotation control [J]. International Journal of Mineral Processing,2011,100(3-4):57-71.
[11]龚明光.泡沫浮选[M].冶金工业出版社,2007.
[12]赖亚J,加拿大,何伯泉.泡沫浮选表面化学[M].冶金工业出版社,1987.
[13]SHEAN B, CILLIERS J. A review of froth flotation control [J]. International Journal of Mineral Processing,2011,100(3):57-71.
[14]ABKHOSHK E, KOR M, REZAIB. A study on the effect of particle size on coal flotation kinetics using fuzzy logic [J]. Expert Systems with Applications,2010, 37(7):5201-5207.
[15]BASK M, JOHANSSON A, IEEE. Robust time-varying thresholds for supervision of valves in a flotation process [M].2004 43rd Ieee Conference on Decision and Control.2004:4305-4310.
[16]BONIFAZI G, SERRANTI S, VOLPE F, et al. Characterisation of flotation froth colour and structure by machine vision [J]. Computers & Geosciences,2001, 27(9):1111-1117.
[17]ALDRICH C, MARAIS C, SHEAN B J, et al. Online monitoring and control of froth flotation systems with machine vision:A review [J]. International Journal of Mineral Processing,2010,96(1-4):1-13.
[18]KAARTINEN J, HATONEN J, HYOTYNIEMI H, et al. Machine-vision-based control of zinc flotation-A case study [J]. Control Engineering Practice,2006, 14(12):1455-1466.
[19]VAN OLST M, BROWN N, BOURKE P, et al. Improving flotation plant performance at cadia by controlling and optimising the rate of froth recovery using outokumpu frothmaster; proceedings of the Seventh Mill Operators' Conference, F,2000 [C].
[20]阳春华,任会峰,许灿辉,et al.基于稀疏多核最小二乘支持向量机的浮选关键指标软测量[J].中国有色金属学报,2011,21(12)：3149-3154.
[21]NGAN H Y, PANG G K, YUNG N H. Automated fabric defect detection—A review [J]. Image and Vision Computing,2011,29(7):442-458.
[22]周开军.矿物浮选泡沫图像形态特征提取方法与应用[D],2010.
[23]GALAS J, LENCZOWSKI S, DASZKIEWICZ M. Applications of hybrid optical methods in mineral processing control systems; proceedings of the Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, F, 1994 [C].
[24]HOLTHAM P, NGUYEN K. On-line analysis of froth surface in coal and mineral flotation using JKFrothCam [J]. International Journal of Mineral Processing,2002,64(2):163-180.
[25]SUPOMO A, YAP E, ZHENG X, et al. PT Freeport Indonesia's mass-pull control strategy for rougher flotation [J]. Minerals Engineering,2008,21(12): 808-816.
[26]FORBES G. Texture and bubble size measurements for modelling concentrate grade in flotation froth systems [D]; University of Cape Town,2007.
[27]MOORE P. Flotation:Froth master [J]. Mining magazine,2006,195(1):20-21.
[28]OKELY A, SEAL G. Mass flow measurement improves froth recovery in gold flotation plants [J]. Engineering and Mining Journal(USA),2002,203(10):24.
[29]CIPRIANO A, GUARINI M, VIDAL R, et al. A real time visual sensor for supervision of flotation cells [J]. Minerals Engineering,1998,11(6):489-499.
[30]曾荣,沃国经.图像处理技术在镍选矿厂中的应用[J].矿冶,2002,11(01)：37-41.
[31]阳春华,周开军,牟学民,et al.基于计算机视觉的浮选泡沫颜色及尺寸测量方法[J].仪器仪表学报,2009,30(04)：717-721.
[32]阳春华,杨尽英,牟学民,et al.基于聚类预分割和高低精度距离重构的彩色浮选泡沫图像分割[J].电子与信息学报,2008,30(06)：1286-1290.
[33]任会峰,阳春华,周璇,et al.基于最小二乘支持向量回归机的矿浆酸碱度鲁棒软测量[J].上海交通大学学报,2011,45(08)：1136-1139+1145.
[34]周开军,阳春华,牟学民,et al.基于图像特征提取的浮选关键参数智能预测算法[J].控制与决策,2009,24(09)：1300-1305.
[35]KLINKER G J, SHAFER S A, KANADE T. The measurement of highlights in color images [J]. International Journal of Computer Vision,1988,2(1):7-32.
[36]VARCOE B, BRATTKE S, WEIDINGER M, et al. What is a photon? [J]. Nature, 2000,403:743-746.
[37]MOOLMAN D, ALDRICH C, VAN DEVENTER J, et al. Digital image processing as a tool for on-line monitoring of froth in flotation plants [J]. Minerals Engineering,1994,7(9):1149-1164.
[38]OESTREICH J, TOLLEY W, RICE D. The development of a color sensor system to measure mineral compositions [J]. Minerals Engineering,1995,8(1): 31-39.
[39]HAAVISTO O, KAARTINEN J, HY TYNIEMI H. Optical spectrum based measurement of flotation slurry contents [J]. International Journal of Mineral Processing,2008,88(3):80-88.
[40]KAARTINEN J, HAAVISTO O, HY TYNIEMI H. On-line colour measurement of flotation froth [J]. Spectrum,2006,400:1000nm.
[41]BONIFAZI G, GIANCONTIERI V, SERRANTI S, et al. A full color digital imaging based approach to characterize flotation froth:An experience in Pyhasalmi (SF) and Garpenberg (S) plants [M].2005.
[42]VATHAVOORAN A, BATCHELOR A, MILES N, et al. Applying froth imaging techniques to assess fine coal dewatering behavior [J]. Coal Preparation,2006, 26(2):103-121.
[43]REDDICK J, HESKETH A, MORAR S, et al. An evaluation of factors affecting the robustness of colour measurement and its potential to predict the grade of flotation concentrate [J]. Minerals Engineering,2009,22(1):64-69.
[44]YANG C H.XU C H, GUI W H, et al. Application of Highlight Removal and Multivariate Image Analysis to Color Measurement of Flotation Bubble Images [J]. International Journal of Imaging Systems and Technology,2009,19(4): 316-322.
[45]HAAVISTO O, HYOTYNIEMI H. Reflectance spectroscopy in the analysis of mineral flotation slurries [J]. Journal of Process Control,2011,21(2):246-253.
[46]NEETHLING S, CILLIERS J. Modelling flotation froths [J]. International Journal of Mineral Processing,2003,72(1):267-287.
[47]WANG Y, NEETHLING S J. Simulating realistic froth surfaces [J]. Minerals Engineering,2006,19(10):1069-1076.
[48]WANG Y, NEETHLING S J. The relationship between the surface and internal structure of dry foam [J]. Colloids and Surfaces A:Physicochemical and Engineering Aspects,2009,339(1):73-81.
[49]WEAIRE D, PHELAN R. The physics of foam [J]. Journal of Physics: Condensed Matter,1999,8(47):9519.
[50]MONNEREAU C, VIGNES-ADLER M. Optical tomography of real three-dimensional foams [J]. Journal of Colloid and Interface Science,1998, 202(1):45-53.
[51]SADR-KAZEMI N, CILLIERS J. An image processing algorithm for measurement of flotation froth bubble size and shape distributions [J]. Minerals Engineering,1997,10(10):1075-1083.
[52]SYMONDS P, DE JAGER G. A technique for automatically segmenting images of the surface froth structures that are prevalent in industrial flotation cells; proceedings of the Communications and Signal Processing,1992 COMSIG'92, Proceedings of the 1992 South African Symposium on, F,1992 [C]. IEEE.
[53]WANG W, BERGHOLM F, YANG B. Froth delineation based on image classification [J]. Minerals Engineering,2003,16(11):1183-1192.
[54]YANG C H, XU C H, MU X M, et al. Bubble size estimation using interfacial morphological information for mineral flotation process monitoring [J]. Transactions of Nonferrous Metals Society of China,2009,19(3):694-699.
[55]LIN B, RECKE B, KNUDSEN J K H, et al. Bubble size estimation for flotation processes [J]. Minerals Engineering,2008,21(7):539-548.
[56]牟春洁,张国英.基于射线的泡沫图象分割算法[J].北京石油化工学院学报,2010,18(01)：36-40.
[57]FORBES G, DE JAGER G Texture measures for improved watershed segmentation of froth images; proceedings of the Fifteenth Annual Symposium of the Pattern Recognition Association of South Africa, F,2004 [C]. Grabouw.
[58]刘金平,桂卫华,牟学民,et al.基于Gabor小波的浮选泡沫图像纹理特征提取[J].仪器仪表学报,2010,31(08)：1769-1775.
[59]MOOLMAN D, ALDRICH C, VAN DEVENTER J, et al. The classification of froth structures in a copper flotation plant by means of a neural net [J]. International Journal of Mineral Processing,1995,43(3):193-208.
[60]BARTOLACCI G, PELLETIER P, TESSIER J, et al. Application of numerical image analysis to process diagnosis and physical parameter measurement in mineral processes—Part Ⅰ:Flotation control based on froth textural characteristics [J]. Minerals Engineering,2006,19(6-8):734-747.
[61]刘文礼,路迈西.数字图像处理技术在煤泥浮选泡沫图像纹理特征的提取及识别上的应用[J].选煤技术,2004,(04)：78-81+92.
[62]刘文礼,路迈西,王凡,et al.煤泥浮选泡沫图像纹理特征的提取及泡沫状态的识别[J].化工学报,2003,64(06)：830-835.
[63]路迈西,王勇,王凡,et al.利用阈值分割技术提取煤泥浮选泡沫图像的物理特征[J].煤炭科学技术,2002,30(08)：34-37.
[64]程翠兰,阳春华,周开军,et al.基于模糊纹理谱的浮选泡沫图像纹理特征提取[J].矿业工程研究,2009,(04)：62-66.
[65]郝元宏,韩静,齐春.一种新的浮选泡沫图像识别方法[J].西安交通大学学报,2011,45(04)：104-108.
[66]何桂春,冯金妮,吴艺鹏,et al.浮选泡沫图像处理技术研究现状与进展[J].有色金属科学与工程,2011,2(02)：57-63.
[67]何桂春,黄开启.浮选指标与浮选泡沫数字图像关系研究[J].金属矿山,2008,(08)：96-101.
[68]黄玉华,李庆利,韩忠义,et al.基于灰色系统理论的煤泥浮选泡沫数字图像处理算法研究[J].选煤技术,2006,(04)：6-8.
[69]梁栋华,于飞,赵建军,et al. BFIPS—Ⅰ型浮选泡沫图像处理系统的应用与 研究[J].有色金属(选矿部分),2011,(01)：43-45.
[70]汪中伟,梁栋华.基于浮选泡沫图像特征参数的应用研究[J].矿冶,2011,(02)：82-84+94.
[71]唐朝晖,刘金平,桂卫华,et al.基于数字图像处理的浮选泡沫速度特征提取及分析[J].中南大学学报(自然科学版),2009,40(06)：1616-1622.
[72]唐朝晖,孙园园,桂卫华,et al.基于小波变换的浮选泡沫图像纹理特征提取[J].计算机工程,2011,37(18)：206-208.
[73]王红平,齐春,李金标,et al.基于主成分分析的矿物浮选泡沫图像分类与识别[J].矿冶,2005,14(03)：79-82.
[74]王建昆.浮选过程泡沫图像特征识别研究[J].云南冶金,2009,(01)：65-67.
[75]王介生,高宪文,张勇.基于图像纹理特征和多级SVM的浮选过程状态识别方法[J].控制与决策,2010,25(10)：1523-1526+1535.
[76]阳春华,任会峰,桂卫华,et al.基于泡沫纹理信度分配SVM的矿物浮选工况识别[J].仪器仪表学报,2011,32(10)：2205-2209.
[77]刘丽,匡纲要.图像纹理特征提取方法综述[J].中国图象图形学报,2009,14(4)：622-635.
[78]KAARTINEN J, H T NEN J, HY TYNIEMI H, et al. Machine-vision-based control of zinc flotation—a case study [J]. Control Engineering Practice,2006, 14(12):1455-1466.
[79]HOLTHAM P N, NGUYEN K K. On-line analysis of froth surface in coal and mineral flotation using JKFrothCam [J]. International Journal of Mineral Processing,2002,64:163-180.
[80]FRANCIS J, DE JAGER G An investigation into the suitability of various motion estimation algorithms for froth imaging; proceedings of the Communications and Signal Processing,1998 COMSIG'98 Proceedings of the 1998 South African Symposium on, F,1998 [C]. IEEE.
[81]牟学民,刘金平,桂卫华,et al.基于SIFT特征配准的浮选泡沫移动速度提取与分析[J].信息与控制,2011,40(4)：525-531.
[82]BARBIAN N, HADLER K, VENTURA-MEDINA E, et al. The froth stability column:linking froth stability and flotation performance [J]. Minerals Engineering,2005,18(3):317-324.
[83]BARBIAN N, HADLER K, CILLIERS J J. The froth stability column: Measuring froth stability at an industrial scale [J]. Minerals Engineering,2006, 19(6-8):713-718.
[84]AKTAS Z, CILLIERS J J, BANFORD A W. Dynamic froth stability:Particle size, airflow rate and conditioning time effects [J]. International Journal of Mineral Processing,2008,87(1-2):65-71.
[85]HU S, OFORI P, FIRTH B. Monitoring of froth stability using electrical impedance spectroscopy [J]. International Journal of Mineral Processing,2009, 92(1-2):15-21.
[86]ZANIN M, WIGHTMAN E, GRANO S R, et al. Quantifying contributions to froth stability in porphyry copper plants [J]. International Journal of Mineral Processing,2009,91(1):19-27.
[87]MORAR S H, BRADSHAW D J, HARRIS M C. The use of the froth surface lamellae burst rate as a flotation froth stability measurement [J]. Minerals Engineering,2012,36-38:152-159.
[88]ATTNEAVE F. Some informational aspects of visual perception [J]. Psychological review,1954,61(3):183.
[89]VAN DER SCHAAF A, VAN HATEREN J H. Modelling the power spectra of natural images:statistics and information [J]. Vision research,1996,36(17): 2759-2770.
[90]SRINIVASAN M, LAUGHLIN S, DUBS A. Predictive coding:a fresh view of inhibition in the retina [J]. Proceedings of the Royal Society of London Series B Biological Sciences,1982,216(1205):427-459.
[91]VAN HATEREN J. A theory of maximizing sensory information [J]. Biological cybernetics,1992,68(1):23-29.
[92]OLSHAUSEN B A. Emergence of simple-cell receptive field properties by learning a sparse code for natural images [J]. Nature,1996,381(6583):607-609.
[93]OLSHAUSEN B A, FIELD D J. Natural image statistics and efficient coding* [J]. Network:computation in neural systems,1996,7(2):333-339.
[94]KERSTEN D, MAMASSIAN P, YUILLE A. Object perception as Bayesian inference [J]. Annu Rev Psychol,2004,55:271-304.
[95]GREIG D, PORTEOUS B, SEHEULT A H. Exact maximum a posteriori estimation for binary images [J]. Journal of the Royal Statistical Society Series B (Methodological),1989:271-279.
[96]BOX G E, TIAO G C:DTIC Document,1973.
[97]GIJSENIJ A, GEVERS T. Color constancy using natural image statistics and scene semantics [J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,2011,33(4):687-698.
[98]SRIVASTAVA A, LEE A B, SIMONCELLI E P, et al. On advances in statistical modeling of natural images [J]. Journal of mathematical imaging and vision, 2003,18(1):17-33.
[99]CHO D, BUI T D. Multivariate statistical modeling for image denoising using wavelet transforms [J]. Signal Processing:Image Communication,2005,20(1): 77-89.
[100]VOLOSHYNOVSKIY S, KOVAL O, PUN T. Image denoising based on the edge-process model [J]. Signal Processing,2005,85(10):1950-1969.
[101]SCHARCANSKI J, JUNG C R. Denoising and enhancing digital mammographic images for visual screening [J]. Computerized Medical Imaging and Graphics,2006,30(4):243-254.
[102]CHEN S, LIU M, ZHANG W, et al. Edge preserving image denoising with a closed form solution [J]. Pattern Recognition,2013,46(3):976-988.
[103]GASPARINI F, SCHETTINI R. Color balancing of digital photos using simple image statistics [J]. Pattern Recognition,2004,37(6):1201-1217.
[104]CHAKRABARTI A, HIRAKAWA K, ZICKLER T. Color constancy with spatio-spectral statistics [J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,2012,34(8):1509-1519.
[105]IMAGE G. Statistical image segmentation [J]. Machine Graphics and Vision, 1992,1(1/2):261-268.
[106]CREMERS D, KOHLBERGER T, SCHN RR C. Shape statistics in kernel space for variational image segmentation [J]. Pattern Recognition,2003,36(9): 1929-1943.
[107]CAO H, KOT A C. Accurate detection of demosaicing regularity for digital image forensics [J]. Information Forensics and Security, IEEE Transactions on, 2009,4(4):899-910.
[108]HEILER M, SCHN RR C. Natural image statistics for natural image segmentation [J]. International Journal of Computer Vision,2005,63(1):5-19.
[109]TORRALBA A, OLIVA A. Statistics of natural image categories [J]. Network:Computation in Neural Systems,2003,14:391-412.
[110]YU L, HE Z, CAO Q. Gabor texture representation method for face recognition using the Gamma and generalized Gaussian models [J]. Image and Vision Computing,2010,28(1):177-187.
[111]GEISLER W S, PERRY J S. Contour statistics in natural images:Grouping across occlusions [J]. Visual neuroscience,2009,26(01):109-121.
[112]GEISLER W, PERRY J, SUPER B, et al. Edge co-occurrence in natural images predicts contour grouping performance [J]. Vision research,2001,41(6): 711-724.
[113]SRIVASTAVA A, LEE A B, SIMONCELLI E P, et al. On advances in statistical modeling of natural images [J]. Journal of Mathematical Imaging and Vision,2003,18(1):17-33.
[114]高贵.SAR图像统计建模研究综述[J].信号处理,2009,25(08)：1270-1278.
[115]KIM J, FISHER Ⅲ J W, YEZZI A, et al. A nonparametric statistical method for image segmentation using information theory and curve evolution [J]. Image Processing, IEEE Transactions on,2005,14(10):1486-1502.
[116]TORRALB A A, OLIVA A. Statistics of natural image categories [J]. Network: computation in neural systems,2003,14(3):391-412.
[117]SIMONCELLI E P, OLSHAUSEN B A. Natural image statistics and neural representation [J]. Annual Review of Neuroscience,2001,24(1):1193-1216.
[118]PIZURICA A, PHILIPS W. Estimating the probability of the presence of a signal of interest in multiresolution single-and multiband image denoising [J]. Image Processing, IEEE Transactions on,2006,15(3):654-665.
[119]YANG M-H, AHUJA N. Gaussian mixture model for human skin color and its application in image and video databases; proceedings of the Proc SPIE: Storage and Retrieval for Image and Video Databases VII, F,1999 [C].
[120]LYU S, SIMONCELLI E P. Statistical modeling of images with fields of Gaussian scale mixtures [J]. Advances in neural information processing systems, 2007,19:945.
[121]KIVANC MIHCAK M, KOZINTSEV I, RAMCHANDRAN K. Spatially adaptive statistical modeling of wavelet image coefficients and its application to denoising; proceedings of the Acoustics, Speech, and Signal Processing,1999 Proceedings,1999 IEEE International Conference on, F,1999 [C]. IEEE.
[122]HAMMOND D K, SIMONCELLI E P. Image modeling and denoising with orientation-adapted Gaussian scale mixtures [J]. Image Processing, IEEE Transactions on,2008,17(11):2089-2101.
[123]WACHTLER T, LEE T W, SEJNOWSKI T J. Chromatic structure of natural scenes [J]. JOSAA,2001,18(1):65-77.
[124]CHANG J H, SHIN J W, KIM N S, et al. Image probability distribution based on generalized gamma function [J]. Signal Processing Letters, IEEE,2005, 12(4):325-328.
[125]MITTAL A, PARAGIOS N. Motion-based background subtraction using adaptive kernel density estimation; proceedings of the Computer Vision and Pattern Recognition,2004 CVPR 2004 Proceedings of the 2004 IEEE Computer Society Conference on, F,2004 [C]. IEEE.
[126]TRAVEN H G A neural network approach to statistical pattern classification bysemiparametric'estimation of probability density functions [J]. Neural Networks, IEEE Transactions on,1991,2(3):366-377.
[127]MANTERO P, MOSER G, SERPICO S B. Partially supervised classification of remote sensing images through SVM-based probability density estimation [J]. Geoscience and Remote Sensing, IEEE Transactions on,2005,43(3):559-570.
[128]LIU C, WECHSLER H. Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition [J]. Image Processing, IEEE Transactions on,2002,11(4):467-476.
[129]YOUNG R A. The Gaussian derivative model for spatial vision:Ⅰ. Retinal mechanisms [J]. Spatial vision,1987,2(4):273-293.
[130]BUADES A, COLL B, MOREL J-M. A review of image denoising algorithms, with a new one [J]. Multiscale Modeling & Simulation,2005,4(2): 490-530.
[131]WEYRICH N, WARHOLA G T. Wavelet shrinkage and generalized cross validation for image denoising [J]. Image Processing, IEEE Transactions on, 1998,7(1):82-90.
[132]ACHIM A, TSAKALIDES P, BEZERIANOS A. SAR image denoising via Bayesian wavelet shrinkage based on heavy-tailed modeling [J]. Geoscience and Remote Sensing, IEEE Transactions on,2003,41(8):1773-1784.
[133]DONOHO D L, JOHNSTONE J M. Ideal spatial adaptation by wavelet shrinkage [J]. Biometrika,1994,81(3):425-455.
[134]DONOHO D L, JOHNSTONE I M. Adapting to unknown smoothness via wavelet shrinkage [J]. Journal of the American Statistical Association,1995, 90(432):1200-1224.
[135]GUERRERO-COL N J A, SIMONCELLI E P, PORTILLA J. Image denoising using mixtures of Gaussian scale mixtures; proceedings of the Image Processing,2008 ICIP 2008 15th IEEE International Conference on, F,2008 [C]. IEEE.
[136]PORTILLA J, STRELA V, WAINWRIGHT M J, et al. Image denoising using scale mixtures of Gaussians in the wavelet domain [J]. Image Processing, IEEE Transactions on,2003,12(11):1338-1351.
[137]CANDES E, DEMANET L, DONOHO D, et al. Fast discrete curvelet transforms [J]. Multiscale Modeling & Simulation,2006,5(3):861-899.
[138]VARGHESE G, WANG Z. Video denoising based on a spatiotemporal Gaussian scale mixture model [J]. Circuits and Systems for Video Technology, IEEE Transactions on,2010,20(7):1032-1040.
[139]STARCK J-L, CAND S E J, DONOHO D L. The curvelet transform for image denoising [J]. Image Processing, IEEE Transactions on,2002,11(6): 670-684.
[140]姜三平.基于小波变换的图像降噪[M].国防工业出版社,2009.
[141]HASHEMI M, BEHESHTI S. Adaptive noise variance estimation in BayesShrink [J]. Signal Processing Letters, IEEE,2010,17(1):12-15.
[142]FORSYTH D A. A novel algorithm for color constancy [J]. International Journal of Computer Vision,1990,5(1):5-35.
[143]RIZZI A, GATTA C, MARINI D. Color correction between gray world and white patch [J]. Electronic Imaging,2002:20-25.
[144]BUCHSBAUM G. A spatial processor model for object colour perception [J]. journal of the Franklin institute,1980,310(1):1-26.
[145]LAND E H, MCCANN J J. Lightness and retinex theory [J]. Journal of the Optical society of America,1971,61(1):1-11.
[146]GIJSENIJ A, GEVERS T, VAN DE WEIJER J. Improving color constancy by photometric edge weighting [J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,2012,34(5):918-929.
[147]CARDEI V C, FUNT B, BARNARD K. Estimating the scene illumination chromaticity by using a neural network [J]. JOSAA,2002,19(12):2374-2386.
[148]FINLAYSON G, HORDLEY S, TASTL I. Gamut constrained illuminant estimation [J]. International Journal of Computer Vision,2006,67(1):93-109.
[149]FINLAYSON G D, HORDLEY S D, HUBEL P M. Color by correlation:A simple, unifying framework for color constancy [J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,2001,23(11):1209-1221.
[150]COX I J, MILLER M L, MINKA T P, et al. The Bayesian image retrieval system, PicHunter:theory, implementation, and psychophysical experiments [J]. Image Processing, IEEE Transactions on,2000,9(1):20-37.
[151]CARDEI V C, FUNT B. Committee-based color constancy; proceedings of the IS&T/SID's Color Imaging Conference, F,1999 [C].
[152]HEALEY G, SLATER D. Global color constancy:recognition of objects by use of illumination-invariant properties of color distributions [J]. JOS A A,1994, 11(11):3003-3010.
[153]BIANCO S, CIOCCA G, CUSANO C, et al. Improving color constancy using indoor-outdoor image classification [J]. Image Processing, IEEE Transactions on,2008,17(12):2381-2392.
[154]RAHTU E, NIKKANEN J, KANNALA J, et al. Applying visual object categorization and memory colors for automatic color constancy [J]. Image Analysis and Processing-ICIAP 2009,2009:873-882.
[155]GEUSEBROEK J-M, SMEULDERS A W. A six-stimulus theory for stochastic texture [J]. International Journal of Computer Vision,2005,62(1-2): 7-16.
[156]BARCLAY A, SWEENEY P, DISSADO L, et al. Stochastic modelling of electrical treeing:fractal and statistical characteristics [J]. Journal of Physics D: Applied Physics,2000,23(12):1536.
[157]CIUREA F, FUNT B. A large image database for color constancy research; proceedings of the Proc IS&T SID Eleventh colour Imaging Conference, Society for Imaging Science and Technology, F,2003 [C].
[158]GIJSENIJ A, GEVERS T, VAN DE WEIJER J. Computational color constancy:Survey and experiments [J]. Image Processing, IEEE Transactions on, 2011,20(9):2475-2489.
[159]ZHU J, WANG Y K, IEEE. Application of Image Recognition System in Flotation Process [M].2008.
[160]CARR-BRION K. Recent developments in detectors for process control x-ray sensors [J]. X-Ray Spectrometry,1980,9(4):184-188.
[161]XU C, GUI W, YANG C, et al. Flotation process fault detection using output PDF of bubble size distribution [J]. Minerals Engineering,2012,26:5-12.
[162]GORAIN B, FRANZIDIS J-P, MANLAPIG E. Studies on impeller type, impeller speed and air flow rate in an industrial scale flotation cell—Part 1: Effect on bubble size distribution [J]. Minerals Engineering,1995,8(6):615-635.
[163]LIU J, GUI W, TANG Z, et al. Recognition of the operational statuses of reagent addition using dynamic bubble size distribution in copper flotation process [J]. Minerals Engineering,2013,45(0):128-141.
[164]CITIR C, AKTAS Z, BERBER R. Off-line image analysis for froth flotation of coal [M]//KRASLAWSKI A, TURUNEN I. European Symposium on Computer Aided Process Engineering-13.2003:605-610.
[165]CITIR C, AKTAS Z, BERBER R. Off-line image analysis for froth flotation of coal [J]. Computers & Chemical Engineering,2004,28(5):625-632.
[166]XUE-MIN M, JIN-PING L, WEI-HUA G, et al. Machine vision based flotation froth mobility analysis; proceedings of the Control Conference (CCC), 2010 29th Chinese, F,2010 [C]. IEEE.
[167]VINCENT L, SOILLE P. Watersheds in digital spaces:an efficient algorithm based on immersion simulations [J]. IEEE transactions on pattern analysis and machine intelligence,1991,13(6):583-598.
[168]CHEN X F, GUI W H, YANG C H, et al. Adaptive Image Processing for Bubbles in Flotation Process [J]. Measurement & Control,2011,44(4):121-125.
[169]LI Y, LIN X Z, ZHAO G Q, et al. A classification of flotation froth based on geometry [M].2007.
[170]LIN X Z, ZHAO G Q, GU Y Y. A classification of flotation froth based on geometry [M].2007.
[171]MEHRSHAD N, MASSINAEI M, DANESHMAND F, et al. AN ADAPTIVE APPROACH FOR BUBBLE SIZE MEASUREMENT FROM FLOTATION FROTH IMAGES [M].10th International Multidisciplinary Scientific Geoconference:Sgem 2010, Vol Ii.2010:619-624.
[172]TERRELL G R, SCOTT D W. Variable kernel density estimation [J]. The Annals of Statistics,1992,20(3):1236-1265.
[173]VINCENT L. Morphological grayscale reconstruction in image analysis: Applications and efficient algorithms [J]. Image Processing, IEEE Transactions on,1993,2(2):176-201.
[174]阳春华,杨尽英,牟学民,et al.基于聚类预分割和高低精度距离重构的彩色浮选泡沫图像分割[J].电子与信息学报,2008,(06)：1286-1290.
[175]方美娥,汪国昭,贺志民.实平面奇异代数曲线的全局B样条逼近[J]. 软件学报,2006,17(10)：2173-2180.
[176]UNNIKRISHNAN R, HEBERT M. Measures of similarity; proceedings of the Application of Computer Vision,2005 WACV/MOTIONS'05 Volume 1 Seventh IEEE Workshops on, F,2005 [C]. IEEE.
[177]WANG H, ZHANG J H. Bounded stochastic distributions control for pseudo-ARMAX stochastic systems [J]. Automatic Control, IEEE Transactions on,2001,46(3):486-490.
[178]SHEATHER S J, JONES M C.A reliable data-based bandwidth selection method for kernel density estimation [J]. Journal of the Royal Statistical Society Series B (Methodological),1991:683-690.
[179]LILLIEFORS H W. On the Kolmogorov-Smirnov test for normality with mean and variance unknown [J]. Journal of the American Statistical Association, 1967,62(318):399-402.
[180]KARYPIS G, HAN E H, KUMAR V. Chameleon:Hierarchical clustering using dynamic modeling [J]. IEEE Computer,1999,32(8):68-75.
[181]FERN NDEZ A, G MEZ S. Solving non-uniqueness in agglomerative hierarchical clustering using multidendrograms [J]. Journal of classification, 2008,25(1):43-65.
[182]HARALICK R M, SHANMUGAM K, DINSTEIN I H. Textural features for image classification [J]. Systems, Man and Cybernetics, IEEE Transactions on, 1.973, (6):610-621.
[183]BHARATI M H, LIU J J, MACGREGOR J F. Image texture analysis: methods and comparisons [J]. Chemometrics and intelligent laboratory systems, 2004,72(1):57-71.
[184]MOOLMAN D, ALDRICH C, SCHMITZ G, et al. The interrelationship between surface froth characteristics and industrial flotation performance [J]. Minerals Engineering,1996,9(8):837-854.
[185]HARGRAVE J, HALL S. Diagnosis of concentrate grade and mass flowrate in tin flotation from colour and surface texture analysis [J]. Minerals Engineering, 1997,10(6):613-621.
[186]LIU J J, MACGREGOR J F, DUCHESNE C, et al. Monitoring of flotation processes using multiresolutional multivariate image analysis(MR-MIA)[C]. Proc. IFAC Dycops'7, Cambridge, MA, July,2004.
[187]HUA B, FU-LONG M, LI-CHENG J. Research on Computation of GLCM of Image Texture [J]. Acta Electronica Sinica,2006,1(1):155-158.
[188]WOLD S, SJ STR M M, ERIKSSON L. PLS-regression:a basic tool of chemometrics [J]. Chemometrics and intelligent laboratory systems,2001,58(2): 109-130.
[189]ARIVAZHAGAN S, GANESAN L, PRIYAL S P. Texture classification using Gabor wavelets based rotation invariant features [J]. Pattern Recognition Letters,2006,27(16):1976-1982.
[190]GABOR D. Theory of communication. Part 1:The analysis of information [J]. Electrical Engineers-Part Ⅲ:Radio and Communication Engineering, Journal of the Institution of,1946,93(26):429-441.
[191]DAUGMAN J G. Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters [J]. Optical Society of America, Journal, A:Optics and Image Science,1985,2: 1160-1169.
[192]MANJUNATH B S, MA W Y. Texture features for browsing and retrieval of image data [J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 1996,18(8):837-842.
[193]LADES M, VORBRUGGEN J C, BUHMANN J, et al. Distortion invariant object recognition in the dynamic link architecture [J]. Computers, IEEE Transactions on,1993,42(3):300-311.
[194]FERSTER D, CHUNG S, WHEAT H. Orientation selectivity of thalamic input to simple cells of cat visual cortex [J]. Nature,1996,380(6571):249-252.
[195]JOSHI R L, FISCHER T R. Comparison of generalized Gaussian and Laplacian modeling in DCT image coding [J]. Signal Processing Letters, IEEE, 1995,2(5):81-82.
[196]ZHANG B, SHAN S, CHEN X, et al. Histogram of Gabor phase patterns (HGPP):a novel object representation approach for face recognition [J]. Image Processing, IEEE Transactions on,2007,16(1):57-68.
[197]DU BUF J. Gabor phase in texture discrimination [J]. Signal Processing, 1990,21(3):221-240.
[198]QING L, SHAN S, CHEN X, et al. Face recognition under varying lighting based on the probabilistic model of gabor phase; proceedings of the Pattern Recognition,2006 ICPR 2006 18th International Conference on, F,2006 [C]. IEEE.
[199]ZHANG W, SHAN S, QING L, et al. Are Gabor phases really useless for face recognition? [J]. Pattern Analysis & Applications,2009,12(3):301-307.
[200]STRUC V, PAVESIC N. Phase congruency features for palm-print verification [J]. Signal Processing, IET,2009,3(4):258-268.
[201]BERNARD S, BOUJEMAA N, VITALE D, et al. Fingerprint segmentation using the phase of multiscale gabor wavelets; proceedings of the The 5th Asian Conference on Computer Vision, Melbourne, Australia, F,2002 [C]. Citeseer.
[202]LAWRENCE S, GILES C L, TSOI A C, et al. Face recognition:A convolutional neural-network approach [J]. Neural Networks, IEEE Transactions on,1997,8(1):98-113.
[203]ANGADI S, GOUR S. LVQ-Neural Network Based Signature Recognition System Using Wavelet Features; proceedings of the Proceedings of the Fourth International Conference on Signal and Image Processing 2012 (ICSIP 2012), F, 2013 [C]. Springer.
[204]YU D, DENG L, SEIDE F. The deep tensor neural network with applications to large vocabulary speech recognition [J].2013.
[205]KOHONEN T. Improved versions of learning vector quantization; proceedings of the Proceedings of the International Joint Conference on Neural Networks, pages I, F,1990 [C].
[206]KOHONEN T. Self-organizing maps [M]. Springer Verlag,2001.
[207]张伦和,何静华,张颖.我国氧化铝工业现状及发展对策[J].轻金属,2006,2：1-3.
[208]刘水红,郑桂兵,任爱军,et al.水硬铝石和含铝硅酸盐在胺类捕收剂作用下的浮选行为[J].有色金属,2007,59(4)：127-130.
[209]BERGH L, YIANATOS J. The long way toward multivariate predictive control of flotation processes [J]. Journal of Process Control,2011,21(2): 226-234.
[210]OJALA T, PIETIKAINEN M, MAENPAA T. A generalized local binary pattern operator for multiresolution gray scale and rotation invariant texture classification [M]. Advances in Pattern Recognition—ICAPR 2001. Springer. 2001:399-408.
[211]HEIKKILA M, PIETIKAINEN M. A texture-based method for modeling the background and detecting moving objects [J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,2006,28(4):657-662.
[2]BENEVENTI D, BENESSE M, CARR B, et al. Modelling deinking selectivity in multistage flotation systems [J]. Separation and purification technology,2007, 54(1):77-87.
[3]FUERSTENAU D. The froth flotation century [J]. Advances in Flotation Technology SME, Littleton, CO, USA,1999:3-21.
[4]BERGH L Q YIANATOS J B. The long way toward multivariate predictive control of flotation processes [J]. Journal of Process Control,2011,21(2): 226-234.
[5]WATT J. On-stream analysis of metalliferous ore slurries [J]. The International Journal of Applied Radiation and Isotopes,1983,34(1):309-331.
[6]SOWERBY B, WATT J. Development of nuclear techniques for on-line analysis in the coal industry [J]. Nuclear Instruments and Methods in Physics Research Section A:Accelerators, Spectrometers, Detectors and Associated Equipment, 1990,299(1):642-647.
[7]周开军,阳春华,牟学民,et al.基于泡沫特征与LS-SVM的浮选回收率预测[J].仪器仪表学报,2009,30(6)：1295-1300.
[8]秦江波,于冬梅,孙永波.中国矿产资源现状与可持续发展研究[J].经济研究导刊,2011,(22)：11-12.
[9]MOOLMAN D, EKSTEEN J, ALDRICH C, et al. The significance of flotation froth appearance for machine vision control [J]. International Journal of Mineral Processing,1996,48(3):135-158.
[10]SHEAN B J, CILLIERS J J. A review of froth flotation control [J]. International Journal of Mineral Processing,2011,100(3-4):57-71.
[11]龚明光.泡沫浮选[M].冶金工业出版社,2007.
[12]赖亚J,加拿大,何伯泉.泡沫浮选表面化学[M].冶金工业出版社,1987.
[13]SHEAN B, CILLIERS J. A review of froth flotation control [J]. International Journal of Mineral Processing,2011,100(3):57-71.
[14]ABKHOSHK E, KOR M, REZAIB. A study on the effect of particle size on coal flotation kinetics using fuzzy logic [J]. Expert Systems with Applications,2010, 37(7):5201-5207.
[15]BASK M, JOHANSSON A, IEEE. Robust time-varying thresholds for supervision of valves in a flotation process [M].2004 43rd Ieee Conference on Decision and Control.2004:4305-4310.
[16]BONIFAZI G, SERRANTI S, VOLPE F, et al. Characterisation of flotation froth colour and structure by machine vision [J]. Computers & Geosciences,2001, 27(9):1111-1117.
[17]ALDRICH C, MARAIS C, SHEAN B J, et al. Online monitoring and control of froth flotation systems with machine vision:A review [J]. International Journal of Mineral Processing,2010,96(1-4):1-13.
[18]KAARTINEN J, HATONEN J, HYOTYNIEMI H, et al. Machine-vision-based control of zinc flotation-A case study [J]. Control Engineering Practice,2006, 14(12):1455-1466.
[19]VAN OLST M, BROWN N, BOURKE P, et al. Improving flotation plant performance at cadia by controlling and optimising the rate of froth recovery using outokumpu frothmaster; proceedings of the Seventh Mill Operators' Conference, F,2000 [C].
[20]阳春华,任会峰,许灿辉,et al.基于稀疏多核最小二乘支持向量机的浮选关键指标软测量[J].中国有色金属学报,2011,21(12)：3149-3154.
[21]NGAN H Y, PANG G K, YUNG N H. Automated fabric defect detection—A review [J]. Image and Vision Computing,2011,29(7):442-458.
[22]周开军.矿物浮选泡沫图像形态特征提取方法与应用[D],2010.
[23]GALAS J, LENCZOWSKI S, DASZKIEWICZ M. Applications of hybrid optical methods in mineral processing control systems; proceedings of the Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, F, 1994 [C].
[24]HOLTHAM P, NGUYEN K. On-line analysis of froth surface in coal and mineral flotation using JKFrothCam [J]. International Journal of Mineral Processing,2002,64(2):163-180.
[25]SUPOMO A, YAP E, ZHENG X, et al. PT Freeport Indonesia's mass-pull control strategy for rougher flotation [J]. Minerals Engineering,2008,21(12): 808-816.
[26]FORBES G. Texture and bubble size measurements for modelling concentrate grade in flotation froth systems [D]; University of Cape Town,2007.
[27]MOORE P. Flotation:Froth master [J]. Mining magazine,2006,195(1):20-21.
[28]OKELY A, SEAL G. Mass flow measurement improves froth recovery in gold flotation plants [J]. Engineering and Mining Journal(USA),2002,203(10):24.
[29]CIPRIANO A, GUARINI M, VIDAL R, et al. A real time visual sensor for supervision of flotation cells [J]. Minerals Engineering,1998,11(6):489-499.
[30]曾荣,沃国经.图像处理技术在镍选矿厂中的应用[J].矿冶,2002,11(01)：37-41.
[31]阳春华,周开军,牟学民,et al.基于计算机视觉的浮选泡沫颜色及尺寸测量方法[J].仪器仪表学报,2009,30(04)：717-721.
[32]阳春华,杨尽英,牟学民,et al.基于聚类预分割和高低精度距离重构的彩色浮选泡沫图像分割[J].电子与信息学报,2008,30(06)：1286-1290.
[33]任会峰,阳春华,周璇,et al.基于最小二乘支持向量回归机的矿浆酸碱度鲁棒软测量[J].上海交通大学学报,2011,45(08)：1136-1139+1145.
[34]周开军,阳春华,牟学民,et al.基于图像特征提取的浮选关键参数智能预测算法[J].控制与决策,2009,24(09)：1300-1305.
[35]KLINKER G J, SHAFER S A, KANADE T. The measurement of highlights in color images [J]. International Journal of Computer Vision,1988,2(1):7-32.
[36]VARCOE B, BRATTKE S, WEIDINGER M, et al. What is a photon? [J]. Nature, 2000,403:743-746.
[37]MOOLMAN D, ALDRICH C, VAN DEVENTER J, et al. Digital image processing as a tool for on-line monitoring of froth in flotation plants [J]. Minerals Engineering,1994,7(9):1149-1164.
[38]OESTREICH J, TOLLEY W, RICE D. The development of a color sensor system to measure mineral compositions [J]. Minerals Engineering,1995,8(1): 31-39.
[39]HAAVISTO O, KAARTINEN J, HY TYNIEMI H. Optical spectrum based measurement of flotation slurry contents [J]. International Journal of Mineral Processing,2008,88(3):80-88.
[40]KAARTINEN J, HAAVISTO O, HY TYNIEMI H. On-line colour measurement of flotation froth [J]. Spectrum,2006,400:1000nm.
[41]BONIFAZI G, GIANCONTIERI V, SERRANTI S, et al. A full color digital imaging based approach to characterize flotation froth:An experience in Pyhasalmi (SF) and Garpenberg (S) plants [M].2005.
[42]VATHAVOORAN A, BATCHELOR A, MILES N, et al. Applying froth imaging techniques to assess fine coal dewatering behavior [J]. Coal Preparation,2006, 26(2):103-121.
[43]REDDICK J, HESKETH A, MORAR S, et al. An evaluation of factors affecting the robustness of colour measurement and its potential to predict the grade of flotation concentrate [J]. Minerals Engineering,2009,22(1):64-69.
[44]YANG C H.XU C H, GUI W H, et al. Application of Highlight Removal and Multivariate Image Analysis to Color Measurement of Flotation Bubble Images [J]. International Journal of Imaging Systems and Technology,2009,19(4): 316-322.
[45]HAAVISTO O, HYOTYNIEMI H. Reflectance spectroscopy in the analysis of mineral flotation slurries [J]. Journal of Process Control,2011,21(2):246-253.
[46]NEETHLING S, CILLIERS J. Modelling flotation froths [J]. International Journal of Mineral Processing,2003,72(1):267-287.
[47]WANG Y, NEETHLING S J. Simulating realistic froth surfaces [J]. Minerals Engineering,2006,19(10):1069-1076.
[48]WANG Y, NEETHLING S J. The relationship between the surface and internal structure of dry foam [J]. Colloids and Surfaces A:Physicochemical and Engineering Aspects,2009,339(1):73-81.
[49]WEAIRE D, PHELAN R. The physics of foam [J]. Journal of Physics: Condensed Matter,1999,8(47):9519.
[50]MONNEREAU C, VIGNES-ADLER M. Optical tomography of real three-dimensional foams [J]. Journal of Colloid and Interface Science,1998, 202(1):45-53.
[51]SADR-KAZEMI N, CILLIERS J. An image processing algorithm for measurement of flotation froth bubble size and shape distributions [J]. Minerals Engineering,1997,10(10):1075-1083.
[52]SYMONDS P, DE JAGER G. A technique for automatically segmenting images of the surface froth structures that are prevalent in industrial flotation cells; proceedings of the Communications and Signal Processing,1992 COMSIG'92, Proceedings of the 1992 South African Symposium on, F,1992 [C]. IEEE.
[53]WANG W, BERGHOLM F, YANG B. Froth delineation based on image classification [J]. Minerals Engineering,2003,16(11):1183-1192.
[54]YANG C H, XU C H, MU X M, et al. Bubble size estimation using interfacial morphological information for mineral flotation process monitoring [J]. Transactions of Nonferrous Metals Society of China,2009,19(3):694-699.
[55]LIN B, RECKE B, KNUDSEN J K H, et al. Bubble size estimation for flotation processes [J]. Minerals Engineering,2008,21(7):539-548.
[56]牟春洁,张国英.基于射线的泡沫图象分割算法[J].北京石油化工学院学报,2010,18(01)：36-40.
[57]FORBES G, DE JAGER G Texture measures for improved watershed segmentation of froth images; proceedings of the Fifteenth Annual Symposium of the Pattern Recognition Association of South Africa, F,2004 [C]. Grabouw.
[58]刘金平,桂卫华,牟学民,et al.基于Gabor小波的浮选泡沫图像纹理特征提取[J].仪器仪表学报,2010,31(08)：1769-1775.
[59]MOOLMAN D, ALDRICH C, VAN DEVENTER J, et al. The classification of froth structures in a copper flotation plant by means of a neural net [J]. International Journal of Mineral Processing,1995,43(3):193-208.
[60]BARTOLACCI G, PELLETIER P, TESSIER J, et al. Application of numerical image analysis to process diagnosis and physical parameter measurement in mineral processes—Part Ⅰ:Flotation control based on froth textural characteristics [J]. Minerals Engineering,2006,19(6-8):734-747.
[61]刘文礼,路迈西.数字图像处理技术在煤泥浮选泡沫图像纹理特征的提取及识别上的应用[J].选煤技术,2004,(04)：78-81+92.
[62]刘文礼,路迈西,王凡,et al.煤泥浮选泡沫图像纹理特征的提取及泡沫状态的识别[J].化工学报,2003,64(06)：830-835.
[63]路迈西,王勇,王凡,et al.利用阈值分割技术提取煤泥浮选泡沫图像的物理特征[J].煤炭科学技术,2002,30(08)：34-37.
[64]程翠兰,阳春华,周开军,et al.基于模糊纹理谱的浮选泡沫图像纹理特征提取[J].矿业工程研究,2009,(04)：62-66.
[65]郝元宏,韩静,齐春.一种新的浮选泡沫图像识别方法[J].西安交通大学学报,2011,45(04)：104-108.
[66]何桂春,冯金妮,吴艺鹏,et al.浮选泡沫图像处理技术研究现状与进展[J].有色金属科学与工程,2011,2(02)：57-63.
[67]何桂春,黄开启.浮选指标与浮选泡沫数字图像关系研究[J].金属矿山,2008,(08)：96-101.
[68]黄玉华,李庆利,韩忠义,et al.基于灰色系统理论的煤泥浮选泡沫数字图像处理算法研究[J].选煤技术,2006,(04)：6-8.
[69]梁栋华,于飞,赵建军,et al. BFIPS—Ⅰ型浮选泡沫图像处理系统的应用与 研究[J].有色金属(选矿部分),2011,(01)：43-45.
[70]汪中伟,梁栋华.基于浮选泡沫图像特征参数的应用研究[J].矿冶,2011,(02)：82-84+94.
[71]唐朝晖,刘金平,桂卫华,et al.基于数字图像处理的浮选泡沫速度特征提取及分析[J].中南大学学报(自然科学版),2009,40(06)：1616-1622.
[72]唐朝晖,孙园园,桂卫华,et al.基于小波变换的浮选泡沫图像纹理特征提取[J].计算机工程,2011,37(18)：206-208.
[73]王红平,齐春,李金标,et al.基于主成分分析的矿物浮选泡沫图像分类与识别[J].矿冶,2005,14(03)：79-82.
[74]王建昆.浮选过程泡沫图像特征识别研究[J].云南冶金,2009,(01)：65-67.
[75]王介生,高宪文,张勇.基于图像纹理特征和多级SVM的浮选过程状态识别方法[J].控制与决策,2010,25(10)：1523-1526+1535.
[76]阳春华,任会峰,桂卫华,et al.基于泡沫纹理信度分配SVM的矿物浮选工况识别[J].仪器仪表学报,2011,32(10)：2205-2209.
[77]刘丽,匡纲要.图像纹理特征提取方法综述[J].中国图象图形学报,2009,14(4)：622-635.
[78]KAARTINEN J, H T NEN J, HY TYNIEMI H, et al. Machine-vision-based control of zinc flotation—a case study [J]. Control Engineering Practice,2006, 14(12):1455-1466.
[79]HOLTHAM P N, NGUYEN K K. On-line analysis of froth surface in coal and mineral flotation using JKFrothCam [J]. International Journal of Mineral Processing,2002,64:163-180.
[80]FRANCIS J, DE JAGER G An investigation into the suitability of various motion estimation algorithms for froth imaging; proceedings of the Communications and Signal Processing,1998 COMSIG'98 Proceedings of the 1998 South African Symposium on, F,1998 [C]. IEEE.
[81]牟学民,刘金平,桂卫华,et al.基于SIFT特征配准的浮选泡沫移动速度提取与分析[J].信息与控制,2011,40(4)：525-531.
[82]BARBIAN N, HADLER K, VENTURA-MEDINA E, et al. The froth stability column:linking froth stability and flotation performance [J]. Minerals Engineering,2005,18(3):317-324.
[83]BARBIAN N, HADLER K, CILLIERS J J. The froth stability column: Measuring froth stability at an industrial scale [J]. Minerals Engineering,2006, 19(6-8):713-718.
[84]AKTAS Z, CILLIERS J J, BANFORD A W. Dynamic froth stability:Particle size, airflow rate and conditioning time effects [J]. International Journal of Mineral Processing,2008,87(1-2):65-71.
[85]HU S, OFORI P, FIRTH B. Monitoring of froth stability using electrical impedance spectroscopy [J]. International Journal of Mineral Processing,2009, 92(1-2):15-21.
[86]ZANIN M, WIGHTMAN E, GRANO S R, et al. Quantifying contributions to froth stability in porphyry copper plants [J]. International Journal of Mineral Processing,2009,91(1):19-27.
[87]MORAR S H, BRADSHAW D J, HARRIS M C. The use of the froth surface lamellae burst rate as a flotation froth stability measurement [J]. Minerals Engineering,2012,36-38:152-159.
[88]ATTNEAVE F. Some informational aspects of visual perception [J]. Psychological review,1954,61(3):183.
[89]VAN DER SCHAAF A, VAN HATEREN J H. Modelling the power spectra of natural images:statistics and information [J]. Vision research,1996,36(17): 2759-2770.
[90]SRINIVASAN M, LAUGHLIN S, DUBS A. Predictive coding:a fresh view of inhibition in the retina [J]. Proceedings of the Royal Society of London Series B Biological Sciences,1982,216(1205):427-459.
[91]VAN HATEREN J. A theory of maximizing sensory information [J]. Biological cybernetics,1992,68(1):23-29.
[92]OLSHAUSEN B A. Emergence of simple-cell receptive field properties by learning a sparse code for natural images [J]. Nature,1996,381(6583):607-609.
[93]OLSHAUSEN B A, FIELD D J. Natural image statistics and efficient coding* [J]. Network:computation in neural systems,1996,7(2):333-339.
[94]KERSTEN D, MAMASSIAN P, YUILLE A. Object perception as Bayesian inference [J]. Annu Rev Psychol,2004,55:271-304.
[95]GREIG D, PORTEOUS B, SEHEULT A H. Exact maximum a posteriori estimation for binary images [J]. Journal of the Royal Statistical Society Series B (Methodological),1989:271-279.
[96]BOX G E, TIAO G C:DTIC Document,1973.
[97]GIJSENIJ A, GEVERS T. Color constancy using natural image statistics and scene semantics [J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,2011,33(4):687-698.
[98]SRIVASTAVA A, LEE A B, SIMONCELLI E P, et al. On advances in statistical modeling of natural images [J]. Journal of mathematical imaging and vision, 2003,18(1):17-33.
[99]CHO D, BUI T D. Multivariate statistical modeling for image denoising using wavelet transforms [J]. Signal Processing:Image Communication,2005,20(1): 77-89.
[100]VOLOSHYNOVSKIY S, KOVAL O, PUN T. Image denoising based on the edge-process model [J]. Signal Processing,2005,85(10):1950-1969.
[101]SCHARCANSKI J, JUNG C R. Denoising and enhancing digital mammographic images for visual screening [J]. Computerized Medical Imaging and Graphics,2006,30(4):243-254.
[102]CHEN S, LIU M, ZHANG W, et al. Edge preserving image denoising with a closed form solution [J]. Pattern Recognition,2013,46(3):976-988.
[103]GASPARINI F, SCHETTINI R. Color balancing of digital photos using simple image statistics [J]. Pattern Recognition,2004,37(6):1201-1217.
[104]CHAKRABARTI A, HIRAKAWA K, ZICKLER T. Color constancy with spatio-spectral statistics [J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,2012,34(8):1509-1519.
[105]IMAGE G. Statistical image segmentation [J]. Machine Graphics and Vision, 1992,1(1/2):261-268.
[106]CREMERS D, KOHLBERGER T, SCHN RR C. Shape statistics in kernel space for variational image segmentation [J]. Pattern Recognition,2003,36(9): 1929-1943.
[107]CAO H, KOT A C. Accurate detection of demosaicing regularity for digital image forensics [J]. Information Forensics and Security, IEEE Transactions on, 2009,4(4):899-910.
[108]HEILER M, SCHN RR C. Natural image statistics for natural image segmentation [J]. International Journal of Computer Vision,2005,63(1):5-19.
[109]TORRALBA A, OLIVA A. Statistics of natural image categories [J]. Network:Computation in Neural Systems,2003,14:391-412.
[110]YU L, HE Z, CAO Q. Gabor texture representation method for face recognition using the Gamma and generalized Gaussian models [J]. Image and Vision Computing,2010,28(1):177-187.
[111]GEISLER W S, PERRY J S. Contour statistics in natural images:Grouping across occlusions [J]. Visual neuroscience,2009,26(01):109-121.
[112]GEISLER W, PERRY J, SUPER B, et al. Edge co-occurrence in natural images predicts contour grouping performance [J]. Vision research,2001,41(6): 711-724.
[113]SRIVASTAVA A, LEE A B, SIMONCELLI E P, et al. On advances in statistical modeling of natural images [J]. Journal of Mathematical Imaging and Vision,2003,18(1):17-33.
[114]高贵.SAR图像统计建模研究综述[J].信号处理,2009,25(08)：1270-1278.
[115]KIM J, FISHER Ⅲ J W, YEZZI A, et al. A nonparametric statistical method for image segmentation using information theory and curve evolution [J]. Image Processing, IEEE Transactions on,2005,14(10):1486-1502.
[116]TORRALB A A, OLIVA A. Statistics of natural image categories [J]. Network: computation in neural systems,2003,14(3):391-412.
[117]SIMONCELLI E P, OLSHAUSEN B A. Natural image statistics and neural representation [J]. Annual Review of Neuroscience,2001,24(1):1193-1216.
[118]PIZURICA A, PHILIPS W. Estimating the probability of the presence of a signal of interest in multiresolution single-and multiband image denoising [J]. Image Processing, IEEE Transactions on,2006,15(3):654-665.
[119]YANG M-H, AHUJA N. Gaussian mixture model for human skin color and its application in image and video databases; proceedings of the Proc SPIE: Storage and Retrieval for Image and Video Databases VII, F,1999 [C].
[120]LYU S, SIMONCELLI E P. Statistical modeling of images with fields of Gaussian scale mixtures [J]. Advances in neural information processing systems, 2007,19:945.
[121]KIVANC MIHCAK M, KOZINTSEV I, RAMCHANDRAN K. Spatially adaptive statistical modeling of wavelet image coefficients and its application to denoising; proceedings of the Acoustics, Speech, and Signal Processing,1999 Proceedings,1999 IEEE International Conference on, F,1999 [C]. IEEE.
[122]HAMMOND D K, SIMONCELLI E P. Image modeling and denoising with orientation-adapted Gaussian scale mixtures [J]. Image Processing, IEEE Transactions on,2008,17(11):2089-2101.
[123]WACHTLER T, LEE T W, SEJNOWSKI T J. Chromatic structure of natural scenes [J]. JOSAA,2001,18(1):65-77.
[124]CHANG J H, SHIN J W, KIM N S, et al. Image probability distribution based on generalized gamma function [J]. Signal Processing Letters, IEEE,2005, 12(4):325-328.
[125]MITTAL A, PARAGIOS N. Motion-based background subtraction using adaptive kernel density estimation; proceedings of the Computer Vision and Pattern Recognition,2004 CVPR 2004 Proceedings of the 2004 IEEE Computer Society Conference on, F,2004 [C]. IEEE.
[126]TRAVEN H G A neural network approach to statistical pattern classification bysemiparametric'estimation of probability density functions [J]. Neural Networks, IEEE Transactions on,1991,2(3):366-377.
[127]MANTERO P, MOSER G, SERPICO S B. Partially supervised classification of remote sensing images through SVM-based probability density estimation [J]. Geoscience and Remote Sensing, IEEE Transactions on,2005,43(3):559-570.
[128]LIU C, WECHSLER H. Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition [J]. Image Processing, IEEE Transactions on,2002,11(4):467-476.
[129]YOUNG R A. The Gaussian derivative model for spatial vision:Ⅰ. Retinal mechanisms [J]. Spatial vision,1987,2(4):273-293.
[130]BUADES A, COLL B, MOREL J-M. A review of image denoising algorithms, with a new one [J]. Multiscale Modeling & Simulation,2005,4(2): 490-530.
[131]WEYRICH N, WARHOLA G T. Wavelet shrinkage and generalized cross validation for image denoising [J]. Image Processing, IEEE Transactions on, 1998,7(1):82-90.
[132]ACHIM A, TSAKALIDES P, BEZERIANOS A. SAR image denoising via Bayesian wavelet shrinkage based on heavy-tailed modeling [J]. Geoscience and Remote Sensing, IEEE Transactions on,2003,41(8):1773-1784.
[133]DONOHO D L, JOHNSTONE J M. Ideal spatial adaptation by wavelet shrinkage [J]. Biometrika,1994,81(3):425-455.
[134]DONOHO D L, JOHNSTONE I M. Adapting to unknown smoothness via wavelet shrinkage [J]. Journal of the American Statistical Association,1995, 90(432):1200-1224.
[135]GUERRERO-COL N J A, SIMONCELLI E P, PORTILLA J. Image denoising using mixtures of Gaussian scale mixtures; proceedings of the Image Processing,2008 ICIP 2008 15th IEEE International Conference on, F,2008 [C]. IEEE.
[136]PORTILLA J, STRELA V, WAINWRIGHT M J, et al. Image denoising using scale mixtures of Gaussians in the wavelet domain [J]. Image Processing, IEEE Transactions on,2003,12(11):1338-1351.
[137]CANDES E, DEMANET L, DONOHO D, et al. Fast discrete curvelet transforms [J]. Multiscale Modeling & Simulation,2006,5(3):861-899.
[138]VARGHESE G, WANG Z. Video denoising based on a spatiotemporal Gaussian scale mixture model [J]. Circuits and Systems for Video Technology, IEEE Transactions on,2010,20(7):1032-1040.
[139]STARCK J-L, CAND S E J, DONOHO D L. The curvelet transform for image denoising [J]. Image Processing, IEEE Transactions on,2002,11(6): 670-684.
[140]姜三平.基于小波变换的图像降噪[M].国防工业出版社,2009.
[141]HASHEMI M, BEHESHTI S. Adaptive noise variance estimation in BayesShrink [J]. Signal Processing Letters, IEEE,2010,17(1):12-15.
[142]FORSYTH D A. A novel algorithm for color constancy [J]. International Journal of Computer Vision,1990,5(1):5-35.
[143]RIZZI A, GATTA C, MARINI D. Color correction between gray world and white patch [J]. Electronic Imaging,2002:20-25.
[144]BUCHSBAUM G. A spatial processor model for object colour perception [J]. journal of the Franklin institute,1980,310(1):1-26.
[145]LAND E H, MCCANN J J. Lightness and retinex theory [J]. Journal of the Optical society of America,1971,61(1):1-11.
[146]GIJSENIJ A, GEVERS T, VAN DE WEIJER J. Improving color constancy by photometric edge weighting [J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,2012,34(5):918-929.
[147]CARDEI V C, FUNT B, BARNARD K. Estimating the scene illumination chromaticity by using a neural network [J]. JOSAA,2002,19(12):2374-2386.
[148]FINLAYSON G, HORDLEY S, TASTL I. Gamut constrained illuminant estimation [J]. International Journal of Computer Vision,2006,67(1):93-109.
[149]FINLAYSON G D, HORDLEY S D, HUBEL P M. Color by correlation:A simple, unifying framework for color constancy [J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,2001,23(11):1209-1221.
[150]COX I J, MILLER M L, MINKA T P, et al. The Bayesian image retrieval system, PicHunter:theory, implementation, and psychophysical experiments [J]. Image Processing, IEEE Transactions on,2000,9(1):20-37.
[151]CARDEI V C, FUNT B. Committee-based color constancy; proceedings of the IS&T/SID's Color Imaging Conference, F,1999 [C].
[152]HEALEY G, SLATER D. Global color constancy:recognition of objects by use of illumination-invariant properties of color distributions [J]. JOS A A,1994, 11(11):3003-3010.
[153]BIANCO S, CIOCCA G, CUSANO C, et al. Improving color constancy using indoor-outdoor image classification [J]. Image Processing, IEEE Transactions on,2008,17(12):2381-2392.
[154]RAHTU E, NIKKANEN J, KANNALA J, et al. Applying visual object categorization and memory colors for automatic color constancy [J]. Image Analysis and Processing-ICIAP 2009,2009:873-882.
[155]GEUSEBROEK J-M, SMEULDERS A W. A six-stimulus theory for stochastic texture [J]. International Journal of Computer Vision,2005,62(1-2): 7-16.
[156]BARCLAY A, SWEENEY P, DISSADO L, et al. Stochastic modelling of electrical treeing:fractal and statistical characteristics [J]. Journal of Physics D: Applied Physics,2000,23(12):1536.
[157]CIUREA F, FUNT B. A large image database for color constancy research; proceedings of the Proc IS&T SID Eleventh colour Imaging Conference, Society for Imaging Science and Technology, F,2003 [C].
[158]GIJSENIJ A, GEVERS T, VAN DE WEIJER J. Computational color constancy:Survey and experiments [J]. Image Processing, IEEE Transactions on, 2011,20(9):2475-2489.
[159]ZHU J, WANG Y K, IEEE. Application of Image Recognition System in Flotation Process [M].2008.
[160]CARR-BRION K. Recent developments in detectors for process control x-ray sensors [J]. X-Ray Spectrometry,1980,9(4):184-188.
[161]XU C, GUI W, YANG C, et al. Flotation process fault detection using output PDF of bubble size distribution [J]. Minerals Engineering,2012,26:5-12.
[162]GORAIN B, FRANZIDIS J-P, MANLAPIG E. Studies on impeller type, impeller speed and air flow rate in an industrial scale flotation cell—Part 1: Effect on bubble size distribution [J]. Minerals Engineering,1995,8(6):615-635.
[163]LIU J, GUI W, TANG Z, et al. Recognition of the operational statuses of reagent addition using dynamic bubble size distribution in copper flotation process [J]. Minerals Engineering,2013,45(0):128-141.
[164]CITIR C, AKTAS Z, BERBER R. Off-line image analysis for froth flotation of coal [M]//KRASLAWSKI A, TURUNEN I. European Symposium on Computer Aided Process Engineering-13.2003:605-610.
[165]CITIR C, AKTAS Z, BERBER R. Off-line image analysis for froth flotation of coal [J]. Computers & Chemical Engineering,2004,28(5):625-632.
[166]XUE-MIN M, JIN-PING L, WEI-HUA G, et al. Machine vision based flotation froth mobility analysis; proceedings of the Control Conference (CCC), 2010 29th Chinese, F,2010 [C]. IEEE.
[167]VINCENT L, SOILLE P. Watersheds in digital spaces:an efficient algorithm based on immersion simulations [J]. IEEE transactions on pattern analysis and machine intelligence,1991,13(6):583-598.
[168]CHEN X F, GUI W H, YANG C H, et al. Adaptive Image Processing for Bubbles in Flotation Process [J]. Measurement & Control,2011,44(4):121-125.
[169]LI Y, LIN X Z, ZHAO G Q, et al. A classification of flotation froth based on geometry [M].2007.
[170]LIN X Z, ZHAO G Q, GU Y Y. A classification of flotation froth based on geometry [M].2007.
[171]MEHRSHAD N, MASSINAEI M, DANESHMAND F, et al. AN ADAPTIVE APPROACH FOR BUBBLE SIZE MEASUREMENT FROM FLOTATION FROTH IMAGES [M].10th International Multidisciplinary Scientific Geoconference:Sgem 2010, Vol Ii.2010:619-624.
[172]TERRELL G R, SCOTT D W. Variable kernel density estimation [J]. The Annals of Statistics,1992,20(3):1236-1265.
[173]VINCENT L. Morphological grayscale reconstruction in image analysis: Applications and efficient algorithms [J]. Image Processing, IEEE Transactions on,1993,2(2):176-201.
[174]阳春华,杨尽英,牟学民,et al.基于聚类预分割和高低精度距离重构的彩色浮选泡沫图像分割[J].电子与信息学报,2008,(06)：1286-1290.
[175]方美娥,汪国昭,贺志民.实平面奇异代数曲线的全局B样条逼近[J]. 软件学报,2006,17(10)：2173-2180.
[176]UNNIKRISHNAN R, HEBERT M. Measures of similarity; proceedings of the Application of Computer Vision,2005 WACV/MOTIONS'05 Volume 1 Seventh IEEE Workshops on, F,2005 [C]. IEEE.
[177]WANG H, ZHANG J H. Bounded stochastic distributions control for pseudo-ARMAX stochastic systems [J]. Automatic Control, IEEE Transactions on,2001,46(3):486-490.
[178]SHEATHER S J, JONES M C.A reliable data-based bandwidth selection method for kernel density estimation [J]. Journal of the Royal Statistical Society Series B (Methodological),1991:683-690.
[179]LILLIEFORS H W. On the Kolmogorov-Smirnov test for normality with mean and variance unknown [J]. Journal of the American Statistical Association, 1967,62(318):399-402.
[180]KARYPIS G, HAN E H, KUMAR V. Chameleon:Hierarchical clustering using dynamic modeling [J]. IEEE Computer,1999,32(8):68-75.
[181]FERN NDEZ A, G MEZ S. Solving non-uniqueness in agglomerative hierarchical clustering using multidendrograms [J]. Journal of classification, 2008,25(1):43-65.
[182]HARALICK R M, SHANMUGAM K, DINSTEIN I H. Textural features for image classification [J]. Systems, Man and Cybernetics, IEEE Transactions on, 1.973, (6):610-621.
[183]BHARATI M H, LIU J J, MACGREGOR J F. Image texture analysis: methods and comparisons [J]. Chemometrics and intelligent laboratory systems, 2004,72(1):57-71.
[184]MOOLMAN D, ALDRICH C, SCHMITZ G, et al. The interrelationship between surface froth characteristics and industrial flotation performance [J]. Minerals Engineering,1996,9(8):837-854.
[185]HARGRAVE J, HALL S. Diagnosis of concentrate grade and mass flowrate in tin flotation from colour and surface texture analysis [J]. Minerals Engineering, 1997,10(6):613-621.
[186]LIU J J, MACGREGOR J F, DUCHESNE C, et al. Monitoring of flotation processes using multiresolutional multivariate image analysis(MR-MIA)[C]. Proc. IFAC Dycops'7, Cambridge, MA, July,2004.
[187]HUA B, FU-LONG M, LI-CHENG J. Research on Computation of GLCM of Image Texture [J]. Acta Electronica Sinica,2006,1(1):155-158.
[188]WOLD S, SJ STR M M, ERIKSSON L. PLS-regression:a basic tool of chemometrics [J]. Chemometrics and intelligent laboratory systems,2001,58(2): 109-130.
[189]ARIVAZHAGAN S, GANESAN L, PRIYAL S P. Texture classification using Gabor wavelets based rotation invariant features [J]. Pattern Recognition Letters,2006,27(16):1976-1982.
[190]GABOR D. Theory of communication. Part 1:The analysis of information [J]. Electrical Engineers-Part Ⅲ:Radio and Communication Engineering, Journal of the Institution of,1946,93(26):429-441.
[191]DAUGMAN J G. Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters [J]. Optical Society of America, Journal, A:Optics and Image Science,1985,2: 1160-1169.
[192]MANJUNATH B S, MA W Y. Texture features for browsing and retrieval of image data [J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 1996,18(8):837-842.
[193]LADES M, VORBRUGGEN J C, BUHMANN J, et al. Distortion invariant object recognition in the dynamic link architecture [J]. Computers, IEEE Transactions on,1993,42(3):300-311.
[194]FERSTER D, CHUNG S, WHEAT H. Orientation selectivity of thalamic input to simple cells of cat visual cortex [J]. Nature,1996,380(6571):249-252.
[195]JOSHI R L, FISCHER T R. Comparison of generalized Gaussian and Laplacian modeling in DCT image coding [J]. Signal Processing Letters, IEEE, 1995,2(5):81-82.
[196]ZHANG B, SHAN S, CHEN X, et al. Histogram of Gabor phase patterns (HGPP):a novel object representation approach for face recognition [J]. Image Processing, IEEE Transactions on,2007,16(1):57-68.
[197]DU BUF J. Gabor phase in texture discrimination [J]. Signal Processing, 1990,21(3):221-240.
[198]QING L, SHAN S, CHEN X, et al. Face recognition under varying lighting based on the probabilistic model of gabor phase; proceedings of the Pattern Recognition,2006 ICPR 2006 18th International Conference on, F,2006 [C]. IEEE.
[199]ZHANG W, SHAN S, QING L, et al. Are Gabor phases really useless for face recognition? [J]. Pattern Analysis & Applications,2009,12(3):301-307.
[200]STRUC V, PAVESIC N. Phase congruency features for palm-print verification [J]. Signal Processing, IET,2009,3(4):258-268.
[201]BERNARD S, BOUJEMAA N, VITALE D, et al. Fingerprint segmentation using the phase of multiscale gabor wavelets; proceedings of the The 5th Asian Conference on Computer Vision, Melbourne, Australia, F,2002 [C]. Citeseer.
[202]LAWRENCE S, GILES C L, TSOI A C, et al. Face recognition:A convolutional neural-network approach [J]. Neural Networks, IEEE Transactions on,1997,8(1):98-113.
[203]ANGADI S, GOUR S. LVQ-Neural Network Based Signature Recognition System Using Wavelet Features; proceedings of the Proceedings of the Fourth International Conference on Signal and Image Processing 2012 (ICSIP 2012), F, 2013 [C]. Springer.
[204]YU D, DENG L, SEIDE F. The deep tensor neural network with applications to large vocabulary speech recognition [J].2013.
[205]KOHONEN T. Improved versions of learning vector quantization; proceedings of the Proceedings of the International Joint Conference on Neural Networks, pages I, F,1990 [C].
[206]KOHONEN T. Self-organizing maps [M]. Springer Verlag,2001.
[207]张伦和,何静华,张颖.我国氧化铝工业现状及发展对策[J].轻金属,2006,2：1-3.
[208]刘水红,郑桂兵,任爱军,et al.水硬铝石和含铝硅酸盐在胺类捕收剂作用下的浮选行为[J].有色金属,2007,59(4)：127-130.
[209]BERGH L, YIANATOS J. The long way toward multivariate predictive control of flotation processes [J]. Journal of Process Control,2011,21(2): 226-234.
[210]OJALA T, PIETIKAINEN M, MAENPAA T. A generalized local binary pattern operator for multiresolution gray scale and rotation invariant texture classification [M]. Advances in Pattern Recognition—ICAPR 2001. Springer. 2001:399-408.
[211]HEIKKILA M, PIETIKAINEN M. A texture-based method for modeling the background and detecting moving objects [J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,2006,28(4):657-662.