矿物浮选泡沫图像形态特征提取方法与应用
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摘要
矿物浮选是在特定工艺条件下,在矿浆中加入浮选药剂,并充入空气、然后搅拌产生大量气泡,最后通过回收含矿泡沫来提高原矿品位,以此满足还原冶炼要求的一种选矿方法。浮选泡沫具有数量多、粘连、混杂、形状不规则等特点,泡沫尺寸与形状特征难以定量描述。目前,我国有色金属矿山的浮选过程主要通过人工观察泡沫状态来调整操作,影响浮选过程的优化运行和有用金属回收率。因此,研究泡沫图像形态特征提取方法,并用于指导浮选工业过程操作,对提高矿产资源的利用效率、实现企业的可持续发展,具有非常重要的现实意义。
论文在分析研究浮选机理的基础上,依据气泡与矿粒的粘附原理,研究了气泡尺寸、形状特征与浮选工况之间的关系,在此基础上,提出了基于机器视觉的浮选泡沫图像形态特征提取方法,并成功应用于矿物浮选过程泡沫图像监控系统中。论文主要研究工作及创新性成果体现在以下几个方面：
(1)针对浮选泡沫图像分割过程中人工选择结构元素存在的问题,详细分析了气泡亮点的统计信息特点,提出了基于几何模式谱的结构元素选择方法。以改进的模糊C均值算法对图像进行聚类,为结构元素的选取提供先验知识,以形态学面积重构开闭运算方法完成图像的除噪。通过定义二值图像的几何模式谱,证明了任意形状属性算子的非递增特性,为将该特性扩展至灰度图像,运用最大树原理对图像进行枝剪,使得算法能够高效地计算尺寸与形状模式谱值。该方法充分利用气泡表面的亮点信息,有效地为泡沫图像分割过程提供结构元素,确保了图像分割准确性,在很大程度上减少了对人工经验的依赖。
(2)针对形态学处理过程中的一类结构元素寻优问题,提出了基于粒子群算法的结构元素优化方法,在定义几何模式谱目标函数的基础上,通过连续改变结构元素的尺寸与形状,计算几何模式谱的全局最小值得到最优结构元素。该方法有效避免了局部最优问题,同时保证了算法的实时性。
(3)针对气泡混杂粘连的特点,提出了基于分级分水岭算法的泡沫图像自适应分割方法。在选取最优结构元素的基础上,以经典分水岭算法完成对泡沫图像的粗分割。提出了基于模糊纹理谱的泡沫图像识别方法,对模糊纹理谱方法加以改进,提出了非线性模糊纹理谱特征提取方法,以支持向量机完成粗分割区域的识别,根据识别结果采取不同处理策略,其中对欠分割的小泡区域进行细分割,运用图像区域合并方法,对过分割的区域进行区域合并,通过定义图像分割评估机制,完成分割结果的评估。这种图像分割、特征提取与识别协同处理的方法极大地提高了算法的鲁棒性,避免了气泡混杂不均对分割结果的影响,有效地减少了欠分割和过分割区域。
(4)针对泡沫尺寸与形状特征难以定量描述问题,提出了泡沫图像的尺寸与形状特征提取方法。在图像分割的基础上,对气泡分割区域像素进行标定,引入样本统计分布的概念,提取了气泡平均尺寸、方差、偏斜度及陡峭度等统计特征。从定性与定量的角度描述了泡沫形状特征,针对定性分析的局限性,提出了基于形态学签名变换的泡沫形状特征提取方法,将气泡复杂形状的特征提取问题转化为从多个签名形状中抽取简单形状特征的问题,在很大程度上简化了复杂形状的描述。该方法通过统计气泡群的特征信息,有效地量化了气泡形态特征,具有良好的实用性。
(5)以实际矿物浮选过程为研究对象,设计了泡沫图像获取硬件平台,开发了矿物浮选泡沫图像监控系统,在此基础上,分析泡沫形态特征与矿物回收率的相关性,建立了基于最小二乘支持向量机的矿物回收率预测模型,为提高算法实时性,对模型进行稀疏化处理,实现了浮选生产过程实时监控。系统的泡沫特征曲线能够为生产工人提供明确的工况信息,并给出具体的操作建议,避免了工人操作的盲目性,提高了浮选生产效率,为浮选过程优化控制奠定了基础。
Mineral flotation is a kind of mineral processing methods.During the process, flotation reagents are added to the pulp and air is filled in the slot. Then they are mixed to bring a great deal of air bubbles.Finally ore grade is improved by retrieving froth containing minerals to meet smelting requirements.Usually, froth is large quantity, adherence, hybrid and irregular shape. Flotation process of nonferrous metal mines in China is usually operated by experienced workers through observing froth surface. As a result, it is hard to work in optimized running state and mineral recovery ratio is low. Consequently, researching the morphological characteristics extract method for froth images and applying them into practice are great significance for optimizing flotation process, maximum using resource, reducing resource consume and maintaining enterprise sustainable development.
According the adhesion principle of air bubbles and mineral particles, relationship between froth morphological characteristics and flotation operating condition is researched in this paper based on flotation mechanism. Then froth image characteristics extraction scheme based on machine vision is proposed and applied in online monitoring system of mineral flotation process successfully. Main research work and innovative achievements are as follows:
(1)Considering the limitation that manual selecting structural element in image segmentation, adaptive selection method for structural element was proposed in this paper based on geometric pattern spectra. Improved fuzzy C-means for image clustering and area reconstruction by open and close operation for de-noise were used to provide prior knowledge for image segmentation.To solve structure elements automatically, morphological geometric pattern spectrum was introduced for binary image. The non-increasing is proved on condition that arbitrary shape operator. In order to extend the characteristic to gray image, a max tree principle is utilized to prune the image. Thus, the proposed algorithm can compute the size and shape pattern spectra value efficiently. This method utilizes the bubble lighting spot information adequately, and provides image segmentation with the structural element. Consequently, it ensured the accuracy of image segmentation, and decreased manual experience to a great extent.
(2) Aiming at the structural element optimization problem in morphological processing, a novel structural element optimization method based on particle swarm optimization(PSO) algorithm. On the basis of geometrical pattern spectra objection function, the global mixmization value is obtained by serially changing the size and shape of structural element, which not only avoid local optimum problem, but also ensure the real time property of the proposed algorithm.
(3)Considering the problem of the asymmetrical size and irregular shape of bubbles in process of image segmentation, froth image adaptive segmentation based on hierarchical watershed algorithm was presented. On the basis of selection of optimal structure element, coarse segmentation was done by using watershed algorithm. Then texture features of the segmentation regions were extracted by fuzzy texture spectrum. Besides, fine-grained segmentation for under-segmentation vesicle regions was preceded through support vector machines for region recognition. At the same time, image region merging mechanism was introduced to merge the big bubble regions which were over-segmentation. Finally the result of segmentation was evaluated. The cooperation process mechanism combines image segmentation, feature extraction and image recognition. Thus, it enhanced the robustness of algorithm, and avoided the effection of all kinds of segmentation caused by hybrid bubble, and decreased under-segmentation and over-segmentation region.
(4) A method for morphological characteristics extraction of froth image was presented. On the basis of image segmentation, pixels in bubble segmentation region were calibrated. At the meantime, concept of sample distribution statistics was introduced. Moreover, some statistical characteristics, such as the average size of bubbles, variance, skewness and abruptness were extracted. The bubble shapes were described from qualitative and quantitative view separately in order to extract bubble shape features. Morphological signature transformation and multi-structural elements were also used for extract bubble morphological characteristics.The feature extraction problem of complex shape is transform into extraction simple shape feature from several signature shapes, which simplify the shaple description method. The experimental results show that the morphological characteristics extracted in this paper have strong practicability.
(5)For an acctural mineral flotation process, a hardware platform was designed to obtain froth images and mineral flotation froth video monitoring system was developed. On the basis of this, correlation analysis of froth morphological characteristics and production index was proposed. Sensitivity analysis of the extracted features was researched firstly. Then correlation analysis of forth morphological characteristics and rate of ore recovery was studied. As a result, prediction model of process indexes was established by using least square support vector machines.For the sake of real-timing of algorithm, the model is proceeded with sparse. Because of the using of this method for industrial flotation, real time monitoring of flotation process was realized. The system can give operating mode information and operation suggestion to the worker. Furthermore, labor productivity is increased and operation aimlessness is avoided, which provided the foundation for optimal control of flotation process.
论文在分析研究浮选机理的基础上,依据气泡与矿粒的粘附原理,研究了气泡尺寸、形状特征与浮选工况之间的关系,在此基础上,提出了基于机器视觉的浮选泡沫图像形态特征提取方法,并成功应用于矿物浮选过程泡沫图像监控系统中。论文主要研究工作及创新性成果体现在以下几个方面：
(1)针对浮选泡沫图像分割过程中人工选择结构元素存在的问题,详细分析了气泡亮点的统计信息特点,提出了基于几何模式谱的结构元素选择方法。以改进的模糊C均值算法对图像进行聚类,为结构元素的选取提供先验知识,以形态学面积重构开闭运算方法完成图像的除噪。通过定义二值图像的几何模式谱,证明了任意形状属性算子的非递增特性,为将该特性扩展至灰度图像,运用最大树原理对图像进行枝剪,使得算法能够高效地计算尺寸与形状模式谱值。该方法充分利用气泡表面的亮点信息,有效地为泡沫图像分割过程提供结构元素,确保了图像分割准确性,在很大程度上减少了对人工经验的依赖。
(2)针对形态学处理过程中的一类结构元素寻优问题,提出了基于粒子群算法的结构元素优化方法,在定义几何模式谱目标函数的基础上,通过连续改变结构元素的尺寸与形状,计算几何模式谱的全局最小值得到最优结构元素。该方法有效避免了局部最优问题,同时保证了算法的实时性。
(3)针对气泡混杂粘连的特点,提出了基于分级分水岭算法的泡沫图像自适应分割方法。在选取最优结构元素的基础上,以经典分水岭算法完成对泡沫图像的粗分割。提出了基于模糊纹理谱的泡沫图像识别方法,对模糊纹理谱方法加以改进,提出了非线性模糊纹理谱特征提取方法,以支持向量机完成粗分割区域的识别,根据识别结果采取不同处理策略,其中对欠分割的小泡区域进行细分割,运用图像区域合并方法,对过分割的区域进行区域合并,通过定义图像分割评估机制,完成分割结果的评估。这种图像分割、特征提取与识别协同处理的方法极大地提高了算法的鲁棒性,避免了气泡混杂不均对分割结果的影响,有效地减少了欠分割和过分割区域。
(4)针对泡沫尺寸与形状特征难以定量描述问题,提出了泡沫图像的尺寸与形状特征提取方法。在图像分割的基础上,对气泡分割区域像素进行标定,引入样本统计分布的概念,提取了气泡平均尺寸、方差、偏斜度及陡峭度等统计特征。从定性与定量的角度描述了泡沫形状特征,针对定性分析的局限性,提出了基于形态学签名变换的泡沫形状特征提取方法,将气泡复杂形状的特征提取问题转化为从多个签名形状中抽取简单形状特征的问题,在很大程度上简化了复杂形状的描述。该方法通过统计气泡群的特征信息,有效地量化了气泡形态特征,具有良好的实用性。
(5)以实际矿物浮选过程为研究对象,设计了泡沫图像获取硬件平台,开发了矿物浮选泡沫图像监控系统,在此基础上,分析泡沫形态特征与矿物回收率的相关性,建立了基于最小二乘支持向量机的矿物回收率预测模型,为提高算法实时性,对模型进行稀疏化处理,实现了浮选生产过程实时监控。系统的泡沫特征曲线能够为生产工人提供明确的工况信息,并给出具体的操作建议,避免了工人操作的盲目性,提高了浮选生产效率,为浮选过程优化控制奠定了基础。
Mineral flotation is a kind of mineral processing methods.During the process, flotation reagents are added to the pulp and air is filled in the slot. Then they are mixed to bring a great deal of air bubbles.Finally ore grade is improved by retrieving froth containing minerals to meet smelting requirements.Usually, froth is large quantity, adherence, hybrid and irregular shape. Flotation process of nonferrous metal mines in China is usually operated by experienced workers through observing froth surface. As a result, it is hard to work in optimized running state and mineral recovery ratio is low. Consequently, researching the morphological characteristics extract method for froth images and applying them into practice are great significance for optimizing flotation process, maximum using resource, reducing resource consume and maintaining enterprise sustainable development.
According the adhesion principle of air bubbles and mineral particles, relationship between froth morphological characteristics and flotation operating condition is researched in this paper based on flotation mechanism. Then froth image characteristics extraction scheme based on machine vision is proposed and applied in online monitoring system of mineral flotation process successfully. Main research work and innovative achievements are as follows:
(1)Considering the limitation that manual selecting structural element in image segmentation, adaptive selection method for structural element was proposed in this paper based on geometric pattern spectra. Improved fuzzy C-means for image clustering and area reconstruction by open and close operation for de-noise were used to provide prior knowledge for image segmentation.To solve structure elements automatically, morphological geometric pattern spectrum was introduced for binary image. The non-increasing is proved on condition that arbitrary shape operator. In order to extend the characteristic to gray image, a max tree principle is utilized to prune the image. Thus, the proposed algorithm can compute the size and shape pattern spectra value efficiently. This method utilizes the bubble lighting spot information adequately, and provides image segmentation with the structural element. Consequently, it ensured the accuracy of image segmentation, and decreased manual experience to a great extent.
(2) Aiming at the structural element optimization problem in morphological processing, a novel structural element optimization method based on particle swarm optimization(PSO) algorithm. On the basis of geometrical pattern spectra objection function, the global mixmization value is obtained by serially changing the size and shape of structural element, which not only avoid local optimum problem, but also ensure the real time property of the proposed algorithm.
(3)Considering the problem of the asymmetrical size and irregular shape of bubbles in process of image segmentation, froth image adaptive segmentation based on hierarchical watershed algorithm was presented. On the basis of selection of optimal structure element, coarse segmentation was done by using watershed algorithm. Then texture features of the segmentation regions were extracted by fuzzy texture spectrum. Besides, fine-grained segmentation for under-segmentation vesicle regions was preceded through support vector machines for region recognition. At the same time, image region merging mechanism was introduced to merge the big bubble regions which were over-segmentation. Finally the result of segmentation was evaluated. The cooperation process mechanism combines image segmentation, feature extraction and image recognition. Thus, it enhanced the robustness of algorithm, and avoided the effection of all kinds of segmentation caused by hybrid bubble, and decreased under-segmentation and over-segmentation region.
(4) A method for morphological characteristics extraction of froth image was presented. On the basis of image segmentation, pixels in bubble segmentation region were calibrated. At the meantime, concept of sample distribution statistics was introduced. Moreover, some statistical characteristics, such as the average size of bubbles, variance, skewness and abruptness were extracted. The bubble shapes were described from qualitative and quantitative view separately in order to extract bubble shape features. Morphological signature transformation and multi-structural elements were also used for extract bubble morphological characteristics.The feature extraction problem of complex shape is transform into extraction simple shape feature from several signature shapes, which simplify the shaple description method. The experimental results show that the morphological characteristics extracted in this paper have strong practicability.
(5)For an acctural mineral flotation process, a hardware platform was designed to obtain froth images and mineral flotation froth video monitoring system was developed. On the basis of this, correlation analysis of froth morphological characteristics and production index was proposed. Sensitivity analysis of the extracted features was researched firstly. Then correlation analysis of forth morphological characteristics and rate of ore recovery was studied. As a result, prediction model of process indexes was established by using least square support vector machines.For the sake of real-timing of algorithm, the model is proceeded with sparse. Because of the using of this method for industrial flotation, real time monitoring of flotation process was realized. The system can give operating mode information and operation suggestion to the worker. Furthermore, labor productivity is increased and operation aimlessness is avoided, which provided the foundation for optimal control of flotation process.
引文
[1]Vallebuona G.,Casali A.,Rodriguez, et al.Bubbles size distribution in mechanical flotation cells at laboratory and industrial scale and modeling of the effects of the operating variables.Revista de metalurgia,2008, 44(3):233-242
[2]Aldrich C.,Feng D..Effect of frothers on bubble size distributions in flotation pulp phases and surface froths. Minerals Engineering,2000,13(10):1049-1057
[3]Murphy D.G.,Zimmerman W.,Woodburn E.T.. Kinematic model of bubble motion in a flotation froth. Powder Tocbeotogy,1996,87(5):3-12
[4]Moolman D.W.,Aldrich C.,Van Deventer J.S.J.. The interpretation of flotation froth surfaces by using digital image analysis and neural networks.Chemical Engineering Science,1995,50(22):3501-3513
[5]瓜里尼M.,左焕莹译.图像处理技术用于评估矿物浮选过程的质量.国外金属矿选,1999,36(12)：38-41
[6]Moolman D.W.,Aldrich C.,Van Deventer J. S. J.,et al.Digital image analysis as a tool for on-line monitoring of forth in flotation plants.Minerals Engineering,1994,9(7):1149-1164
[7]西普利安诺A.,毛益平译.采用数字图像处理的浮选专家管理系统.国外金属矿选矿,1998,35(10)：44-47
[8]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.In:International conference on imaging: technology and applications for the 21st century, Beijing, China,2005. 172-173
[9]Sadr-Kazemi N.,Cilliers J.J..An image processing algorithm for measurement of flotation froth bubble size and shape distributions.Minerals Engineering,1997,10(10):1075-1083
[10]Hatonen J.,Hyotyniemi H.,Miettunen J..Using image information and partial least squares method to estimate mineral concentrations in mineral flotation. In:John A. M.. Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials, Hawaii, USA,1999. 459-464
[11]Kaartinen J.,Hatonen J.,Hyotyniemi H.,et al.Machine vision based control of zinc flotation-A case study. Control Engineering Practice,2006,14(12): 1455-1466
[12]Bonifazi G,Serranti S.,Volpe F.,et al.Characterisation of flotation froth colour and structure by machine vision. Computers & Geosciences,2001, 27(9):1111-1117
[13]Holtham P. N.,Nguyen K. K.. On-line analysis of froth surface in coal and mineral flotation using JKFrothCam. International Journal of Mineral Processing,2002,64(2/3):163-180
[14]Lin B.,Recke B.,Knudsen J.K. H.,et al. Bubble size estimation for flotation processes.Minerals Engineering.2008,21(7):539-548
[15]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. Minerals Engineering,2006,6(19):734-747
[16]Hyotyniemi H.,Ylinen R.. Modeling of visual flotation froth data. Control Engineering Practice,2000,8(10):313-318
[17]Barbian N.,Cilliers J.J.,Morar S.H.,et al. Froth imaging, air recovery and bubble loading to describe flotation bank performance. International Journal of Mineral Processing,2007,84(1-4):81-88
[18]陈子鸣,茹青.使用多媒体技术研究浮选泡沫现象的初步探索.矿冶工程,1997,17(3)：247-248
[19]刘文礼,路迈西,王凡等.煤泥浮选泡沫图像纹理特征的提取及泡沫状态的识别.化工学报,2003,54(6)：830-835
[20]李珍香,罗宏宇.一种基于PC机的煤泥浮选自动识别系统.电脑开发与应用,2000,13(2)：29-30
[21]刘富强,钱建生,王新红等.基于图像处理与识别技术的煤矿矸石自动分选.煤炭学报,2000,25(5)：534-537
[22]刘文礼,陈子彤,路迈西.煤泥浮选泡沫的数字图像处理.燃料化学学报,2002,30(3)：198-203
[23]王晓晨,张奕奎,郑士芹.泡沫图像特征与浮选精煤指标关系的研究.淮南工业学院学报,2001,1(1)：41-43
[24]曾荣,沃国经.图像处理技术在镍选矿厂中的应用.矿冶,2002,1(11)：37-41
[25]Lu M. X.,Wang F., Liu X. M.. Analysis of texture of froth image in coal floatation. Journal of China University of Mining & Technology,2001,11(2): 100-103
[26]路迈西,王勇,王凡等.利用阈值分割技术提取煤泥浮选泡沫图像的物理特征.煤炭科学技术,2002,30(8)：34-37
[27]刘文礼,路迈西,王勇等.煤泥浮选泡沫数字图像处理研究(之一).中国矿业大学学报,2002,31(2)：120-123
[28]Botha, C.P.,Weber, D. M., van O1st M.. A practical system for realtime on-plant flotation froth visual parameter extraction. In:Proceedings of IEEE Africon, Cape Town, South Africa,1999.103-106
[29]Wang W.,Bergholm F.,Yang B..Froth delineation based on image classification. Minerals Engineering,2003,16(3):1183-1192
[30]林小竹,谷莹莹,赵国庆.煤泥浮选泡沫图像分割与特征提取.煤炭学报,2007,32(3)：304-308
[31]王麓雅,唐文胜,刘相滨.浮选中泡沫图像的分割算法.湖南师范大学学报,2002,25(2)：23-26
[32]阳春华,杨尽英,牟学民等.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.电子与信息学报,2008,30(6)：1286-1290
[33]曾荣.浮选泡沫图像边缘检测方法的研究.中国矿业大学学报,2002,31(5)：421-425
[34]Awad M.,Chehdi K.,Nasri A.. Multi-component image segmentation using a hybrid dynamic genetic algorithm and fuzzy C-means.IET Image Processing, 2009,3(2):52-62
[35]Mahmoodi S..Shape-based active contours for fast video segmentation. IEEE Signal Processing Letters,2008,16(10):857-860
[36]Wang L.,He L.,Mishra A.,et al.Active contours driven by local Gaussian distribution fitting energy. Signal Processing,2009,89(12):2435-2447
[37]Pan Y. S..Top-down image segmentation using the Mumford-Shah functional and level set image representation. In:IEEE International Conference on Acoustics, Speech and Signal Processing, Taipei,2009.1241-1244
[38]Fan J.C.,Han M.,Wang J..Single point iterative weighted fuzzy c-means clustering algorithm for remote sensing image segmentation. Pattern Recognition,2009,42(11):2527-2540
[39]Cheng J.R.,Rajapakse J. C..Segmentation of clustered nuclei with shape markers and marking function. IEEE Transactions on Biomedical Engineering, 2009,56(3):741-748
[40]Tao W. B.,Jin H.,Zhang Y.M.. Color image segmentation based on mean shift and normalized cuts. IEEE Transactions on Systems, Man, and Cybernetics, Part B,2007,37(5):1382-1389
[41]Kuo W. F.,Lin C.Y.,Sun Y. N..Region similarity relationship between watershed and penalized fuzzy Hopfield neural network algorithms for brain image segmentation. International Journal of Pattern Recognition and Artificial Intelligence,2008,22(7):1403-1425
[42]Mignotte M..Segmentation by fusion of histogram-based K-means clusters in different color spaces.IEEE Transactions on Image Processing,2008, 17(5):780-787
[43]Fei, Z. G. Guo J.J.,Wan P.,et al.Fast automatic image segmentation based on bayesian decision-making theory. In:Proceedings of the International Conference on Information and Automation,Zhuhai,Macau,2009.184-188
[44]Wang L.,Leckie C.,Ramamohanarao K.,et al.Automatically determining the number of clusters in unlabeled data sets.IEEE Transactions on Knowledge and Data Engineering,2009,21(3):335-350
[45]Soille P.,Vogt P..Morphological segmentation of binary patterns.Pattern Recognition Letters,2009,30(4):456-459
[46]Sun H.Q.,Luo Y. J..Adaptive watershed segmentation of binary particle image. Journal of Microscopy-Oxford,2009,233(2):326-330
[47]Faruquzzaman A. B.M.,Paiker N. R.,Arafat J.,et al.Object segmentation based on split and merge algorithm.In:Proceedings of the IEEE Region 10 Conference, Hyderabad, India,2008.1447-1450
[48]Lezoray O.,Charrier C..Color image segmentation using morphological clustering and fusion with automatic scale selection. Pattern Recognition Letters,2009,30(4):397-406
[49]Hamarneh G,Li X. X.. Watershed segmentation using prior shape and appearance knowledge. Image and Vision Computing,2009,27(1-2):59-68
[50]Gao H.,Lin W. S.,Xue P.,et al.Marker-based image segmentation relying on disjoint set union. Signal Processing:Image Communication,2006,21(2): 100-112
[51]Bleau A.,Leon L. J..Watershed-based segmentation and region merging, Computer Vision and Image Understanding,2000,77(3):317-370
[52]Keceli A. S.,Can A. B..Automatic segmentation of white matter lesions.In: Proceedings of the IEEE 17th Signal Processing and Communications Applications Conference, Antalya,2009.181-184
[53]Fan J.,Zeng G,Body M.,et al.Seeded region growing:an extensive and comparative study. Pattern Recognition Letters,2005,26(8):1139-1156
[54]Hanbury A.,Marcotegui B..Morphological segmentation on learned boundaries. Image and Vision Computing,2009,27(4):480-488
[55]Lezoray O.,Cardot H..Cooperation of color pixel classification schemes and color watershed:a study for microscopic images.IEEE Transactions on Image Processing,2002,11(7):783-789
[56]Levner I.,Zhang H.. Classification-driven watershed segmentation. IEEE Transactions on Image Processing,2007,16(5):1437-1445
[57]Li C.H.,Huang W. C.,Kuo B.C.,et al.A novel fuzzy weighted c-means method for image classification. International Journal of Fuzzy Systems, 2008,10(3):168-173
[58]王向阳,王春花.基于特征散度的自适应FCM图像分割算法.中国图象图形学报,2008,13(5)：906-910
[59]Lin X. Z.,Zhao G. Q.,Gu Y. Y..A classification of flotation froth based on geometry. In:Proceedings of the 2007 IEEE International Conference on Mechatronics and Automation,Harbin,China,2007.2716-2720
[60]Grau R. A.,Heiskanen K.. Visual technique for measuring bubble size in flotation machines.Minerals Engineering,2002,15(7):507-513
[61]周开军,阳春华,牟学民等.基于模糊C均值和形态学分割的泡沫形态特征提取,中南大学学报,已录用,2009年发表
[62]Estrada-Ruiz R.H.,Perez-Garibay R.. Neural networks to estimate bubble diameter and bubble size distribution of flotation froth surfaces. Journal of the South African Institute of Mining and Metallurgy,2009,109(7):441-446
[63]Aldrich C.,Schmitz G. P. J.,Gouws F. S.Development of fuzzy rule-based systems for industrial flotation plants by use of inductive techniques and genetic algorithms.Journal of The South African Institute of Mining and Metallurgy,2000,100(2):129-134
[64]Saghatoleslam N.,Karimi H.,Rahimi R.,et al.Modeling of texture and color froth characteristics for evaluation of flotation performance in sarcheshmeh copper pilot plant using image analysis and neural networks.International Journal of Engineering, Transactions B:Applications,2004,17(2):121-130
[65]Rughooputh H.C.S.,Rughooputh S.D.D.V..Neural network process vision systems for flotation process. Kybernetes,2002,31(3-4):529-535
[66]HyoKtyniemi H.,Ylinen R.. Modeling of visual fotation froth data.Control Engineering Practice,2000,8(3):313-318
[67]Holtham P. N.,Nguyen K. K.. On-line analysis of froth surface in coal and mineral flotation using JKFrothCam. International Journal of Mineral Processing,2002,64(2-3):163-180
[68]Liu J.J.,MacGregor J. F..Froth-based modeling and control of flotation processes. Minerals Engineering.2008,21(6):642-651
[69]Gianni, B.Patrick P. J.,Jayson T. 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. Minerals Engineering,2006,19(6-8):734-747
[70]Bonifazi G.,Massacci P.,Meloul A.. Prediction of complex sulfide flotation by a combined 3D fractal and colour analysisof the froths.Minetals Engineering, 2000,13(7):737-747
[71]Moolman D.W.,Aldrich C.,van Deventer J. S.J.,et al.The classification of froth structures in copper flotation plant by means of a neural net. International Journal of Mineral Processing,1995,43(3/4):193-208
[72]范德文特J.S.J.,墨尔曼D.W..具有泡沫特征的神经计算机视觉在线显像浮选过程.国外金属矿选矿,1999,36(7)：14-19
[73]Hargrave J.M.,Hall S.T.. Diagnosis of concentrate grade and mass flow rate in tin flotation from color and surface texture analysis.Minerals Engineering, 1997,6(10):613-621
[74]Nunez F.,Cipriano A..Visual information model based predictor for froth speed control in flotation process.Minerals Engineering,2009,22(4):366-371
[75]曾荣,沃国经.图像处理技术在浮选过程中的应用.有色金属,2001,53(4)：70-72
[76]张奕奎,陈凌,王飞.基于图像识别的浮选控制系统.工矿自动化,20023(1)：14-16
[77]Suykens J.A. K.,Vanderwalle J..Least squares support vector machine classifiers.Neural Processing Letters,1998,3(9):293-300
[78]Kemal Polat, Bayram Akdemir, Salih Gunes.Computer aided diagnosis of ECG data on the least square support vector machine. Digital Signal Processing.2008,1(11):25-32
[79]周开军,阳春华,牟学民等.基于泡沫特征与LSSVM的浮选回收率预测,仪器仪表学报,2009,30(6)：1295-1300；
[80]宋海鹰,桂卫华,阳春华.稀疏最小二乘支持向量机及其应用研究.信息与控制.2008,3(37)：334-338
[81]Jiao L. C.,Bo L. F.,Wang L..Fast sparse approximation for least squares support vector machine. IEEE Transactions on Neural Networks,2007, 18(3):685-697
[82]Hoegaerts L.,Suykens J. A. K.,Vandewalle J.,et al.Subset based least squares subspace regression in RKHS.Neurocomputing,2005,63(1):293-323
[83]Heisterkamp D.,Peng J.,Dai H..An adaptive quasi-conformal kernel metric for image retrieval.In:Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Kauai Marriott, Hawaii,2001.236-243
[84]周开军,阳春华,牟学民等.基于图像特征的浮选关键参数智能预测算法,控制与决策,2009,24(9)：1300-1305
[85]龚明光.浮游选矿.北京：冶金工业出版社,1988
[86]卢寿慈,瓮达.界面分选原理及应用.北京：冶金工业出版社,1992
[87]胡熙庚,黄和慰,毛钜凡等.浮选理论与工艺.长沙：中南工业大学出版社,1991
[88]邱冠周,胡岳华,王淀佐.颗粒间相互作用与细粒浮选.长沙：中南工业大学出版社,1993
[89]列皮伦J.O.,刘万峰,肖力子.粗颗粒浮选的有效技术-泡沫中分选.国外金属矿选矿,2004,41(9)：9-14
[90]Weber W.,Paddok D..Interceptional and gravitational collision efficiencies for single collectors at intermediate Raynolds numbers.Journal of Colloid and Interface Science,1983,94(5):328-335
[91]Yoon R. H..The role of hydrodynamic and surface forces in bubble-particle interaction. International Journal of Mineral Processing,2000, 58(1-4):129-143
[92]Schulze H.,Radoev J.,Geidle B.,et al.Investigations of the collision process between particles and gas bubbles in flotation-a theoretical analysis.Mineral Processing,1989,27:263-278
[93]Nguyen A. V.,Evans G.M.. Attachment interaction between air bubbles and particles in froth flotation. Experimental Thermal and Fluid Science,2004, 28(5):381-385
[94]Dbobby G. S.,Finch J. A.. Particle size dependence in flotation derived from fundamental model of the capture process.International Journal of Mineral Processing,1987,21(3-4):241-260
[95]Mankosa M. J.,Luttrell G.H.,Yoon R. H..A study of axial mixing in column flotation. International Journal of Mineral Processing,1992,35(2):51-64
[96]Woodburn E.T.,Austin L. G,Stockton J.B..A froth based flotation kinetic model.Chemical engineering Research and Design,1994,72(2):211-226
[97]Zhou K. J.,Yang C.H.,Gui W. H.,et al.Clustering-driven watershed adaptive segmentation of bubble image, Journal of Central South University of Technology,已录用,2010年发表
[98]Liang Wang, Leckie, C.,Ramamohanarao, K.,et al.Automatically determining the number of clusters in unlabeled data sets.IEEE Transactions on Knowledge and Data Engineering,2009,21(3):335-350
[99]Cousty J.,Bertrand G.,Najman L.. Watershed cuts:Minimum spanning forests and the drop of water principle. IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(8):1362-1374
[100]Theera-Umpon N.,Dhompongsa S. Morphological granulometric features of nucleus in automatic bone marrow white blood cell classification. IEEE Transactions on Information Technology in Biomedicine,2007, 11(3):353-359
[101]Bobin J.,Starck J.L.,Fadili J.M.. Morphological component analysis:An adaptive thresholding strategy. IEEE Transactions on Image Processing,2007, 16(11):2675-2681
[102]Soille P. Morphological image compositing. IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(5):673-683
[103]Urbach E.R.,Roerdink J.B.T. M.,Wilkinson M. H. F.. Connected shape-size pattern spectra for rotation and scale-invariant classification of gray-scale images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007,29(2):272-285
[104]Meijster A.,Wilkinson M. H.F.. A comparison of algorithms for connected set openings and closings.IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(4):484-494
[105]Braga-Neto U.,Goutsias J.. Object-based image analysis using multiscale connectivity. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005,27(6):892-907
[106]Breen E.J.,Jones R.,Talbot H..Mathematical morphology:A useful set of tools for image analysis. Statistics and Computing,2000,10(2):105-120
[107]Hirata N. S.T..Multilevel training of binary morphological operators. IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(4): 707-720
[108]Zapater V.,Martinez-Costa L.,Ayala G.A granulometric analysis of specular microscopy images of human corneal endothelia. Computer Vision and Image Understanding,2005,97(3):297-314
[109]Serra J, Vincent L. An overview of morphological filtering. Circuits Systems and Signal Processing,1992, 11(1):47-108
[110]Maragos P..Pattern spectrum and multiscale shape representation. IEEE Transactions on Pattern Analysis and Machine Intelligence,1989, 11(7):701-716
[111]Jones R.,Svalbe I.. Algorithms for the decomposition of gray-scale morphological operations. IEEE Transactions on Pattern Analysis and Machine Intelligence,1994,16(6):581-588
[112]Salembier P.,Oliveras A.,Garrido L.. Anti-extensive connected operators for image and sequence processing. IEEE Transactions on Image Processing, 1998,4(7):555-570
[113]Westenberg M.A.,Roerdink J.B.T. M.,Wilkinson M.H.F.. Volumetric attribute filtering and interactive visualization using the max-tree representation. IEEE Transactions on Image Processing,2007,16(12): 2943-2952
[114]Li C. H.,Huang W. C.,Kuo B.C.,et al. A novel fuzzy weighted c-means method for image classification. International Journal of Fuzzy Systems,2008, 10(3):168-173
[115]Jimenez-Sanchez A. R.,Mendiola-Santibanez J.D.,Terol-Villalobos I. R.. Morphological background detection and enhancement of images with poor lighting. IEEE Transactions on Image Processing,2009,18(3):613-623
[116]Cai J.H.,Zhang G.X.,Zhou Z.K.. The application of area-reconstruction operator in automatic visual inspection of quality control.In:Proceedings of the sixth World Congress on Intelligent Control and Automation, Dalian, China,2006.10111-10115
[117]Asano A.. Texture analysis using morphological pattern spectrum and optimization of structuring elements. In:Proceedings of the 10th International Conference on Image Analysis and Processing, Barcelona, Spain,1999.209-214
[118]Yuen C. W. M.,Wong W. K.,Qian S.Q.,et al.A hybrid model using genetic algorithm and neural network for classifying garment defects.Expert Systems with Applications,2009,36(2):2037-2047
[119]Shen T. S.,Liu S.T.,Zhou X. D..Optimal design of morphological filters based on adaptive immune algorithm.In:Proceedings of the 7th International Conference on Signal Processing, Beijing, China,2004.1064-1067
[120]Fan S.K. S.,Lin Y.,Wu C.C..Image thresholding using a novel estimation method in generalized Gaussian distribution mixture modeling. Neurocomputing,2008,72(1):500-512
[121]Yang C.H.,Xu C.H.,Mu X. M.,et al.Bubble size estimation using interfacial morphological information for mineral flotation process monitoring. Transactions of Nonferrous Metals Society of China,2009,19(3):694-699
[122]Vincent L.,Soille P..Watersheds in digital spaces:An efficient algorithm based on immersion simulations.IEEE Transaction on Pattern Analysis and Machine Intelligence,1991,13(6):583-598
[123]Barcelo A.,Montseny E.,Sobrevilla P..Fuzzy texture unit and fuzzy texture spectrum for texture characterization. Fuzzy Sets and Systems,2007,158 (3):239-252
[124]Grau-Sanchez M.,Montseny E.,Sobrevilla P..On the use of fuzzy texture spectrum for homogeneous and textured images discrimination. In: Proceedings of the IEEE International Conference on Fuzzy Systems, London, England,2007.1891-1896
[125]Osowski S.,Siroic R.,Markiewicz T.. Application of support vector machine and genetic algorithm for improved blood cell recognition. IEEE Transactions on Instrumentation and Measurement,2009,58(7):2159-2168
[126]Vapnik V.N.An overview of statistical learning theory. IEEE Transactions on Neural Networks,1999,10(5):988-999
[127]Ng H.P.,Ong S.H.,Foong K. W. C.,et al.Masseter segmentation using an improved watershed algorithm with unsupervised classification. Computers in Biology and Medicine,2008,38(2):171-184
[128]曾凡,胡永平,杨毅等,矿物加工颗粒学.徐州：中国矿业大学出版社,2001
[129]Loncaric S.,Dhawan A. P..Near-optimal MST-based shape description using genetic algorithm.Pattern Recognition,1995,28(4):571-579
[130]Liposcak Z.,Loncaric S..Face recognition from profiles using morphological signature transform. In:Proceedings of the 21st International Conference Information Technology Interfaces, Pula, Croatia,1999.93-98
[131]阳春华,周开军,牟学民等.基于计算机视觉的浮选泡沫颜色及尺寸测量方法,仪器仪表学报,2009,30(4)：717-721
[132]Mu S.J.,Zeng Y. Z.,Liu R. L.. Online dual updating with recursive PLS model and its application in predicting crystal size of purified terephthalic acid(PTA)process.Process Control,2002,16(6):557-566
[133]甘良志,孙宗海,孙优贤.稀疏最小二乘支持向量机.浙江大学学报(工学版),2007,41(2)：245-248
[2]Aldrich C.,Feng D..Effect of frothers on bubble size distributions in flotation pulp phases and surface froths. Minerals Engineering,2000,13(10):1049-1057
[3]Murphy D.G.,Zimmerman W.,Woodburn E.T.. Kinematic model of bubble motion in a flotation froth. Powder Tocbeotogy,1996,87(5):3-12
[4]Moolman D.W.,Aldrich C.,Van Deventer J.S.J.. The interpretation of flotation froth surfaces by using digital image analysis and neural networks.Chemical Engineering Science,1995,50(22):3501-3513
[5]瓜里尼M.,左焕莹译.图像处理技术用于评估矿物浮选过程的质量.国外金属矿选,1999,36(12)：38-41
[6]Moolman D.W.,Aldrich C.,Van Deventer J. S. J.,et al.Digital image analysis as a tool for on-line monitoring of forth in flotation plants.Minerals Engineering,1994,9(7):1149-1164
[7]西普利安诺A.,毛益平译.采用数字图像处理的浮选专家管理系统.国外金属矿选矿,1998,35(10)：44-47
[8]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.In:International conference on imaging: technology and applications for the 21st century, Beijing, China,2005. 172-173
[9]Sadr-Kazemi N.,Cilliers J.J..An image processing algorithm for measurement of flotation froth bubble size and shape distributions.Minerals Engineering,1997,10(10):1075-1083
[10]Hatonen J.,Hyotyniemi H.,Miettunen J..Using image information and partial least squares method to estimate mineral concentrations in mineral flotation. In:John A. M.. Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials, Hawaii, USA,1999. 459-464
[11]Kaartinen J.,Hatonen J.,Hyotyniemi H.,et al.Machine vision based control of zinc flotation-A case study. Control Engineering Practice,2006,14(12): 1455-1466
[12]Bonifazi G,Serranti S.,Volpe F.,et al.Characterisation of flotation froth colour and structure by machine vision. Computers & Geosciences,2001, 27(9):1111-1117
[13]Holtham P. N.,Nguyen K. K.. On-line analysis of froth surface in coal and mineral flotation using JKFrothCam. International Journal of Mineral Processing,2002,64(2/3):163-180
[14]Lin B.,Recke B.,Knudsen J.K. H.,et al. Bubble size estimation for flotation processes.Minerals Engineering.2008,21(7):539-548
[15]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. Minerals Engineering,2006,6(19):734-747
[16]Hyotyniemi H.,Ylinen R.. Modeling of visual flotation froth data. Control Engineering Practice,2000,8(10):313-318
[17]Barbian N.,Cilliers J.J.,Morar S.H.,et al. Froth imaging, air recovery and bubble loading to describe flotation bank performance. International Journal of Mineral Processing,2007,84(1-4):81-88
[18]陈子鸣,茹青.使用多媒体技术研究浮选泡沫现象的初步探索.矿冶工程,1997,17(3)：247-248
[19]刘文礼,路迈西,王凡等.煤泥浮选泡沫图像纹理特征的提取及泡沫状态的识别.化工学报,2003,54(6)：830-835
[20]李珍香,罗宏宇.一种基于PC机的煤泥浮选自动识别系统.电脑开发与应用,2000,13(2)：29-30
[21]刘富强,钱建生,王新红等.基于图像处理与识别技术的煤矿矸石自动分选.煤炭学报,2000,25(5)：534-537
[22]刘文礼,陈子彤,路迈西.煤泥浮选泡沫的数字图像处理.燃料化学学报,2002,30(3)：198-203
[23]王晓晨,张奕奎,郑士芹.泡沫图像特征与浮选精煤指标关系的研究.淮南工业学院学报,2001,1(1)：41-43
[24]曾荣,沃国经.图像处理技术在镍选矿厂中的应用.矿冶,2002,1(11)：37-41
[25]Lu M. X.,Wang F., Liu X. M.. Analysis of texture of froth image in coal floatation. Journal of China University of Mining & Technology,2001,11(2): 100-103
[26]路迈西,王勇,王凡等.利用阈值分割技术提取煤泥浮选泡沫图像的物理特征.煤炭科学技术,2002,30(8)：34-37
[27]刘文礼,路迈西,王勇等.煤泥浮选泡沫数字图像处理研究(之一).中国矿业大学学报,2002,31(2)：120-123
[28]Botha, C.P.,Weber, D. M., van O1st M.. A practical system for realtime on-plant flotation froth visual parameter extraction. In:Proceedings of IEEE Africon, Cape Town, South Africa,1999.103-106
[29]Wang W.,Bergholm F.,Yang B..Froth delineation based on image classification. Minerals Engineering,2003,16(3):1183-1192
[30]林小竹,谷莹莹,赵国庆.煤泥浮选泡沫图像分割与特征提取.煤炭学报,2007,32(3)：304-308
[31]王麓雅,唐文胜,刘相滨.浮选中泡沫图像的分割算法.湖南师范大学学报,2002,25(2)：23-26
[32]阳春华,杨尽英,牟学民等.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.电子与信息学报,2008,30(6)：1286-1290
[33]曾荣.浮选泡沫图像边缘检测方法的研究.中国矿业大学学报,2002,31(5)：421-425
[34]Awad M.,Chehdi K.,Nasri A.. Multi-component image segmentation using a hybrid dynamic genetic algorithm and fuzzy C-means.IET Image Processing, 2009,3(2):52-62
[35]Mahmoodi S..Shape-based active contours for fast video segmentation. IEEE Signal Processing Letters,2008,16(10):857-860
[36]Wang L.,He L.,Mishra A.,et al.Active contours driven by local Gaussian distribution fitting energy. Signal Processing,2009,89(12):2435-2447
[37]Pan Y. S..Top-down image segmentation using the Mumford-Shah functional and level set image representation. In:IEEE International Conference on Acoustics, Speech and Signal Processing, Taipei,2009.1241-1244
[38]Fan J.C.,Han M.,Wang J..Single point iterative weighted fuzzy c-means clustering algorithm for remote sensing image segmentation. Pattern Recognition,2009,42(11):2527-2540
[39]Cheng J.R.,Rajapakse J. C..Segmentation of clustered nuclei with shape markers and marking function. IEEE Transactions on Biomedical Engineering, 2009,56(3):741-748
[40]Tao W. B.,Jin H.,Zhang Y.M.. Color image segmentation based on mean shift and normalized cuts. IEEE Transactions on Systems, Man, and Cybernetics, Part B,2007,37(5):1382-1389
[41]Kuo W. F.,Lin C.Y.,Sun Y. N..Region similarity relationship between watershed and penalized fuzzy Hopfield neural network algorithms for brain image segmentation. International Journal of Pattern Recognition and Artificial Intelligence,2008,22(7):1403-1425
[42]Mignotte M..Segmentation by fusion of histogram-based K-means clusters in different color spaces.IEEE Transactions on Image Processing,2008, 17(5):780-787
[43]Fei, Z. G. Guo J.J.,Wan P.,et al.Fast automatic image segmentation based on bayesian decision-making theory. In:Proceedings of the International Conference on Information and Automation,Zhuhai,Macau,2009.184-188
[44]Wang L.,Leckie C.,Ramamohanarao K.,et al.Automatically determining the number of clusters in unlabeled data sets.IEEE Transactions on Knowledge and Data Engineering,2009,21(3):335-350
[45]Soille P.,Vogt P..Morphological segmentation of binary patterns.Pattern Recognition Letters,2009,30(4):456-459
[46]Sun H.Q.,Luo Y. J..Adaptive watershed segmentation of binary particle image. Journal of Microscopy-Oxford,2009,233(2):326-330
[47]Faruquzzaman A. B.M.,Paiker N. R.,Arafat J.,et al.Object segmentation based on split and merge algorithm.In:Proceedings of the IEEE Region 10 Conference, Hyderabad, India,2008.1447-1450
[48]Lezoray O.,Charrier C..Color image segmentation using morphological clustering and fusion with automatic scale selection. Pattern Recognition Letters,2009,30(4):397-406
[49]Hamarneh G,Li X. X.. Watershed segmentation using prior shape and appearance knowledge. Image and Vision Computing,2009,27(1-2):59-68
[50]Gao H.,Lin W. S.,Xue P.,et al.Marker-based image segmentation relying on disjoint set union. Signal Processing:Image Communication,2006,21(2): 100-112
[51]Bleau A.,Leon L. J..Watershed-based segmentation and region merging, Computer Vision and Image Understanding,2000,77(3):317-370
[52]Keceli A. S.,Can A. B..Automatic segmentation of white matter lesions.In: Proceedings of the IEEE 17th Signal Processing and Communications Applications Conference, Antalya,2009.181-184
[53]Fan J.,Zeng G,Body M.,et al.Seeded region growing:an extensive and comparative study. Pattern Recognition Letters,2005,26(8):1139-1156
[54]Hanbury A.,Marcotegui B..Morphological segmentation on learned boundaries. Image and Vision Computing,2009,27(4):480-488
[55]Lezoray O.,Cardot H..Cooperation of color pixel classification schemes and color watershed:a study for microscopic images.IEEE Transactions on Image Processing,2002,11(7):783-789
[56]Levner I.,Zhang H.. Classification-driven watershed segmentation. IEEE Transactions on Image Processing,2007,16(5):1437-1445
[57]Li C.H.,Huang W. C.,Kuo B.C.,et al.A novel fuzzy weighted c-means method for image classification. International Journal of Fuzzy Systems, 2008,10(3):168-173
[58]王向阳,王春花.基于特征散度的自适应FCM图像分割算法.中国图象图形学报,2008,13(5)：906-910
[59]Lin X. Z.,Zhao G. Q.,Gu Y. Y..A classification of flotation froth based on geometry. In:Proceedings of the 2007 IEEE International Conference on Mechatronics and Automation,Harbin,China,2007.2716-2720
[60]Grau R. A.,Heiskanen K.. Visual technique for measuring bubble size in flotation machines.Minerals Engineering,2002,15(7):507-513
[61]周开军,阳春华,牟学民等.基于模糊C均值和形态学分割的泡沫形态特征提取,中南大学学报,已录用,2009年发表
[62]Estrada-Ruiz R.H.,Perez-Garibay R.. Neural networks to estimate bubble diameter and bubble size distribution of flotation froth surfaces. Journal of the South African Institute of Mining and Metallurgy,2009,109(7):441-446
[63]Aldrich C.,Schmitz G. P. J.,Gouws F. S.Development of fuzzy rule-based systems for industrial flotation plants by use of inductive techniques and genetic algorithms.Journal of The South African Institute of Mining and Metallurgy,2000,100(2):129-134
[64]Saghatoleslam N.,Karimi H.,Rahimi R.,et al.Modeling of texture and color froth characteristics for evaluation of flotation performance in sarcheshmeh copper pilot plant using image analysis and neural networks.International Journal of Engineering, Transactions B:Applications,2004,17(2):121-130
[65]Rughooputh H.C.S.,Rughooputh S.D.D.V..Neural network process vision systems for flotation process. Kybernetes,2002,31(3-4):529-535
[66]HyoKtyniemi H.,Ylinen R.. Modeling of visual fotation froth data.Control Engineering Practice,2000,8(3):313-318
[67]Holtham P. N.,Nguyen K. K.. On-line analysis of froth surface in coal and mineral flotation using JKFrothCam. International Journal of Mineral Processing,2002,64(2-3):163-180
[68]Liu J.J.,MacGregor J. F..Froth-based modeling and control of flotation processes. Minerals Engineering.2008,21(6):642-651
[69]Gianni, B.Patrick P. J.,Jayson T. 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. Minerals Engineering,2006,19(6-8):734-747
[70]Bonifazi G.,Massacci P.,Meloul A.. Prediction of complex sulfide flotation by a combined 3D fractal and colour analysisof the froths.Minetals Engineering, 2000,13(7):737-747
[71]Moolman D.W.,Aldrich C.,van Deventer J. S.J.,et al.The classification of froth structures in copper flotation plant by means of a neural net. International Journal of Mineral Processing,1995,43(3/4):193-208
[72]范德文特J.S.J.,墨尔曼D.W..具有泡沫特征的神经计算机视觉在线显像浮选过程.国外金属矿选矿,1999,36(7)：14-19
[73]Hargrave J.M.,Hall S.T.. Diagnosis of concentrate grade and mass flow rate in tin flotation from color and surface texture analysis.Minerals Engineering, 1997,6(10):613-621
[74]Nunez F.,Cipriano A..Visual information model based predictor for froth speed control in flotation process.Minerals Engineering,2009,22(4):366-371
[75]曾荣,沃国经.图像处理技术在浮选过程中的应用.有色金属,2001,53(4)：70-72
[76]张奕奎,陈凌,王飞.基于图像识别的浮选控制系统.工矿自动化,20023(1)：14-16
[77]Suykens J.A. K.,Vanderwalle J..Least squares support vector machine classifiers.Neural Processing Letters,1998,3(9):293-300
[78]Kemal Polat, Bayram Akdemir, Salih Gunes.Computer aided diagnosis of ECG data on the least square support vector machine. Digital Signal Processing.2008,1(11):25-32
[79]周开军,阳春华,牟学民等.基于泡沫特征与LSSVM的浮选回收率预测,仪器仪表学报,2009,30(6)：1295-1300；
[80]宋海鹰,桂卫华,阳春华.稀疏最小二乘支持向量机及其应用研究.信息与控制.2008,3(37)：334-338
[81]Jiao L. C.,Bo L. F.,Wang L..Fast sparse approximation for least squares support vector machine. IEEE Transactions on Neural Networks,2007, 18(3):685-697
[82]Hoegaerts L.,Suykens J. A. K.,Vandewalle J.,et al.Subset based least squares subspace regression in RKHS.Neurocomputing,2005,63(1):293-323
[83]Heisterkamp D.,Peng J.,Dai H..An adaptive quasi-conformal kernel metric for image retrieval.In:Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Kauai Marriott, Hawaii,2001.236-243
[84]周开军,阳春华,牟学民等.基于图像特征的浮选关键参数智能预测算法,控制与决策,2009,24(9)：1300-1305
[85]龚明光.浮游选矿.北京：冶金工业出版社,1988
[86]卢寿慈,瓮达.界面分选原理及应用.北京：冶金工业出版社,1992
[87]胡熙庚,黄和慰,毛钜凡等.浮选理论与工艺.长沙：中南工业大学出版社,1991
[88]邱冠周,胡岳华,王淀佐.颗粒间相互作用与细粒浮选.长沙：中南工业大学出版社,1993
[89]列皮伦J.O.,刘万峰,肖力子.粗颗粒浮选的有效技术-泡沫中分选.国外金属矿选矿,2004,41(9)：9-14
[90]Weber W.,Paddok D..Interceptional and gravitational collision efficiencies for single collectors at intermediate Raynolds numbers.Journal of Colloid and Interface Science,1983,94(5):328-335
[91]Yoon R. H..The role of hydrodynamic and surface forces in bubble-particle interaction. International Journal of Mineral Processing,2000, 58(1-4):129-143
[92]Schulze H.,Radoev J.,Geidle B.,et al.Investigations of the collision process between particles and gas bubbles in flotation-a theoretical analysis.Mineral Processing,1989,27:263-278
[93]Nguyen A. V.,Evans G.M.. Attachment interaction between air bubbles and particles in froth flotation. Experimental Thermal and Fluid Science,2004, 28(5):381-385
[94]Dbobby G. S.,Finch J. A.. Particle size dependence in flotation derived from fundamental model of the capture process.International Journal of Mineral Processing,1987,21(3-4):241-260
[95]Mankosa M. J.,Luttrell G.H.,Yoon R. H..A study of axial mixing in column flotation. International Journal of Mineral Processing,1992,35(2):51-64
[96]Woodburn E.T.,Austin L. G,Stockton J.B..A froth based flotation kinetic model.Chemical engineering Research and Design,1994,72(2):211-226
[97]Zhou K. J.,Yang C.H.,Gui W. H.,et al.Clustering-driven watershed adaptive segmentation of bubble image, Journal of Central South University of Technology,已录用,2010年发表
[98]Liang Wang, Leckie, C.,Ramamohanarao, K.,et al.Automatically determining the number of clusters in unlabeled data sets.IEEE Transactions on Knowledge and Data Engineering,2009,21(3):335-350
[99]Cousty J.,Bertrand G.,Najman L.. Watershed cuts:Minimum spanning forests and the drop of water principle. IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(8):1362-1374
[100]Theera-Umpon N.,Dhompongsa S. Morphological granulometric features of nucleus in automatic bone marrow white blood cell classification. IEEE Transactions on Information Technology in Biomedicine,2007, 11(3):353-359
[101]Bobin J.,Starck J.L.,Fadili J.M.. Morphological component analysis:An adaptive thresholding strategy. IEEE Transactions on Image Processing,2007, 16(11):2675-2681
[102]Soille P. Morphological image compositing. IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(5):673-683
[103]Urbach E.R.,Roerdink J.B.T. M.,Wilkinson M. H. F.. Connected shape-size pattern spectra for rotation and scale-invariant classification of gray-scale images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007,29(2):272-285
[104]Meijster A.,Wilkinson M. H.F.. A comparison of algorithms for connected set openings and closings.IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(4):484-494
[105]Braga-Neto U.,Goutsias J.. Object-based image analysis using multiscale connectivity. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005,27(6):892-907
[106]Breen E.J.,Jones R.,Talbot H..Mathematical morphology:A useful set of tools for image analysis. Statistics and Computing,2000,10(2):105-120
[107]Hirata N. S.T..Multilevel training of binary morphological operators. IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(4): 707-720
[108]Zapater V.,Martinez-Costa L.,Ayala G.A granulometric analysis of specular microscopy images of human corneal endothelia. Computer Vision and Image Understanding,2005,97(3):297-314
[109]Serra J, Vincent L. An overview of morphological filtering. Circuits Systems and Signal Processing,1992, 11(1):47-108
[110]Maragos P..Pattern spectrum and multiscale shape representation. IEEE Transactions on Pattern Analysis and Machine Intelligence,1989, 11(7):701-716
[111]Jones R.,Svalbe I.. Algorithms for the decomposition of gray-scale morphological operations. IEEE Transactions on Pattern Analysis and Machine Intelligence,1994,16(6):581-588
[112]Salembier P.,Oliveras A.,Garrido L.. Anti-extensive connected operators for image and sequence processing. IEEE Transactions on Image Processing, 1998,4(7):555-570
[113]Westenberg M.A.,Roerdink J.B.T. M.,Wilkinson M.H.F.. Volumetric attribute filtering and interactive visualization using the max-tree representation. IEEE Transactions on Image Processing,2007,16(12): 2943-2952
[114]Li C. H.,Huang W. C.,Kuo B.C.,et al. A novel fuzzy weighted c-means method for image classification. International Journal of Fuzzy Systems,2008, 10(3):168-173
[115]Jimenez-Sanchez A. R.,Mendiola-Santibanez J.D.,Terol-Villalobos I. R.. Morphological background detection and enhancement of images with poor lighting. IEEE Transactions on Image Processing,2009,18(3):613-623
[116]Cai J.H.,Zhang G.X.,Zhou Z.K.. The application of area-reconstruction operator in automatic visual inspection of quality control.In:Proceedings of the sixth World Congress on Intelligent Control and Automation, Dalian, China,2006.10111-10115
[117]Asano A.. Texture analysis using morphological pattern spectrum and optimization of structuring elements. In:Proceedings of the 10th International Conference on Image Analysis and Processing, Barcelona, Spain,1999.209-214
[118]Yuen C. W. M.,Wong W. K.,Qian S.Q.,et al.A hybrid model using genetic algorithm and neural network for classifying garment defects.Expert Systems with Applications,2009,36(2):2037-2047
[119]Shen T. S.,Liu S.T.,Zhou X. D..Optimal design of morphological filters based on adaptive immune algorithm.In:Proceedings of the 7th International Conference on Signal Processing, Beijing, China,2004.1064-1067
[120]Fan S.K. S.,Lin Y.,Wu C.C..Image thresholding using a novel estimation method in generalized Gaussian distribution mixture modeling. Neurocomputing,2008,72(1):500-512
[121]Yang C.H.,Xu C.H.,Mu X. M.,et al.Bubble size estimation using interfacial morphological information for mineral flotation process monitoring. Transactions of Nonferrous Metals Society of China,2009,19(3):694-699
[122]Vincent L.,Soille P..Watersheds in digital spaces:An efficient algorithm based on immersion simulations.IEEE Transaction on Pattern Analysis and Machine Intelligence,1991,13(6):583-598
[123]Barcelo A.,Montseny E.,Sobrevilla P..Fuzzy texture unit and fuzzy texture spectrum for texture characterization. Fuzzy Sets and Systems,2007,158 (3):239-252
[124]Grau-Sanchez M.,Montseny E.,Sobrevilla P..On the use of fuzzy texture spectrum for homogeneous and textured images discrimination. In: Proceedings of the IEEE International Conference on Fuzzy Systems, London, England,2007.1891-1896
[125]Osowski S.,Siroic R.,Markiewicz T.. Application of support vector machine and genetic algorithm for improved blood cell recognition. IEEE Transactions on Instrumentation and Measurement,2009,58(7):2159-2168
[126]Vapnik V.N.An overview of statistical learning theory. IEEE Transactions on Neural Networks,1999,10(5):988-999
[127]Ng H.P.,Ong S.H.,Foong K. W. C.,et al.Masseter segmentation using an improved watershed algorithm with unsupervised classification. Computers in Biology and Medicine,2008,38(2):171-184
[128]曾凡,胡永平,杨毅等,矿物加工颗粒学.徐州：中国矿业大学出版社,2001
[129]Loncaric S.,Dhawan A. P..Near-optimal MST-based shape description using genetic algorithm.Pattern Recognition,1995,28(4):571-579
[130]Liposcak Z.,Loncaric S..Face recognition from profiles using morphological signature transform. In:Proceedings of the 21st International Conference Information Technology Interfaces, Pula, Croatia,1999.93-98
[131]阳春华,周开军,牟学民等.基于计算机视觉的浮选泡沫颜色及尺寸测量方法,仪器仪表学报,2009,30(4)：717-721
[132]Mu S.J.,Zeng Y. Z.,Liu R. L.. Online dual updating with recursive PLS model and its application in predicting crystal size of purified terephthalic acid(PTA)process.Process Control,2002,16(6):557-566
[133]甘良志,孙宗海,孙优贤.稀疏最小二乘支持向量机.浙江大学学报(工学版),2007,41(2)：245-248