基于复杂网络时空特性的泡沫图像动态纹理特征分析

doi:10.11949/j.issn.0438-1157.20161632

化工学报 ›› 2017, Vol. 68 ›› Issue (3): 1023-1031.DOI: 10.11949/j.issn.0438-1157.20161632

基于复杂网络时空特性的泡沫图像动态纹理特征分析

马爱莲, 徐德刚, 谢永芳, 阳春华, 桂卫华

中南大学信息科学与工程学院, 湖南长沙 410083

收稿日期:2016-11-18 修回日期:2016-11-19 出版日期:2017-03-05 发布日期:2017-03-05
通讯作者: 徐德刚,dgxu@csu.edu.cn
基金资助:
国家自然科学基金项目（61473319）；国家自然科学基金创新研究群体项目（61321003）；中南大学创新驱动计划项目（2016CX014）。

Analysis of dynamic texture features of floatation froth images based on space-time characteristics of complex networks

MA Ailian, XU Degang, XIE Yongfang, YANG Chunhua, GUI Weihua

School of Information Science and Engineering, Central South University, Changsha 410083, Hunan, China

Received:2016-11-18 Revised:2016-11-19 Online:2017-03-05 Published:2017-03-05
Contact: 10.11949/j.issn.0438-1157.20161632
Supported by:
supported by the National Natural Science Foundation of China (61473319),the Foundation for Innovative Research Groups of National Natural Science Foundation of China (61321003) and the Innovation Research Funds of Central South University (2016CX014).

摘要/Abstract

摘要：

基于机器视觉的浮选过程监控方法已经被广泛应用于浮选过程中，泡沫表面纹理特征是过程监控的关键视觉特征之一。当前静态纹理特征只能从空间维度描述图像特征，在时间维度上刻画图像序列的内在变化特性存在不足，不能准确反映浮选泡沫浮选过程动态特性。提出了基于复杂网络时空特性的泡沫图像序列动态纹理特征方法。通过将每帧图像的像素点映射到网络各节点，利用邻接矩阵建立复杂网络模型和网络权值动态演化反应不同时刻的图像特征，基于复杂网络时空特性提取泡沫图像序列的动态纹理特征。结合实际生产数据进行仿真验证，实验结果表明该方法可准确识别浮选动态状况，为浮选生产过程的实时调节提供重要的指导信息。

关键词: 浮选, 纹理特征, 复杂网络, 动态建模, 过程控制

Abstract:

Methods for monitoring flotation processes based on machine vision has been widely used, which surface texture feature of froths is one of the key visual parameters in process monitoring. Static texture features can only describe images in space dimensions but do not well describe inherent variation characteristics of the image sequence in time dimension, so they can not accurately reflect dynamic characteristics of froths in flotation process. An extraction and analysis method for dynamic texture features of flotation froth images was proposed on a basis of space-time characteristics of complex networks. After pixels of each image were mapped into nodes of complex networks, a complex network model was established by adjacent matrix. The image characteristics at different time were described by network-weighted dynamic evolution and dynamic texture characteristics of image sequences were obtained by the space-time characteristics of complex networks. Simulation results with actual production data showed that the method could accurately identify flotation dynamic conditions and provide guidance for instant regulation of flotation process.

Key words: flotation, texture features, complex network, dynamic modeling, process control

中图分类号:

TQ028.8

马爱莲, 徐德刚, 谢永芳, 阳春华, 桂卫华. 基于复杂网络时空特性的泡沫图像动态纹理特征分析[J]. 化工学报, 2017, 68(3): 1023-1031.

MA Ailian, XU Degang, XIE Yongfang, YANG Chunhua, GUI Weihua. Analysis of dynamic texture features of floatation froth images based on space-time characteristics of complex networks[J]. CIESC Journal, 2017, 68(3): 1023-1031.

参考文献 31

[1]	赵洪伟, 谢永芳, 蒋朝辉, 等. 基于泡沫图像特征的浮选槽液位智能优化设定方法[J]. 自动化学报, 2014, 40(6):1086-1097. ZHAO H W, XIE Y F, JIANG C H, et al. An intelligent optimal setting approach based on froth features for level of flotation cells[J]. Acta Automatica Sinica, 2014, 40(6):1087-1097.
[2]	桂卫华, 廖茜, 阳春华, 等. 一种新的浮选泡沫图像纹理特征提取方法[J]. 中国科技论文, 2012, 7(4):277-281. GUI W H, LIAO X, YANG C H, et al. A new texture extraction method for flotation froth images[J]. China Science Paper, 2012, 7(4):277-281.
[3]	李建奇. 矿物浮选泡沫图像增强与分割方法研究及应用[D]. 长沙:中南大学, 2013. LI J Q. Froth image enhancement and segmentation method and its application for mineral flotation[D]. Changsha:Central South University, 2013.
[4]	林霞. 浮选泡沫图像纹理特征提取研究及应用[D]. 长沙:中南大学, 2013. LIN X. Research and application of flotation froth image texture extraction[D]. Changsha:Central South University, 2013.
[5]	MOOLMAN D W, ALDRICH C. Digital image processing as a tool for on-line monitoring of froth in flotation plants[J]. Minerals Engineering, 1994, 7(9):1149-1164.
[6]	BARTOLACCI G, PELLETIER P J, TESSIER J J, et al. Application of numerical image analysis to process diagnosis and physical parameter measurement in mineral processes(Ⅰ):Flotation control based on froth textural characteristics[J]. Minerals Engineering, 2006, 19(6/7/8):734-747.
[7]	KAARTINEN J, KOIVO H. Machine vision based measurement and control of zinc flotation circuit[J]. Studies in Informatics and Control, 2002, 11(1):97-105.
[8]	刘金平, 桂卫华, 牟学民, 等. 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(8):1699-1775. LIU J P, GUI W H, MU X M, et al. Flotation froth image texture feature extraction based on Gabor wavelets[J]. Chinese J. Sci. Instrum., 2010, 31(8):1699-1775.
[9]	程翠兰, 阳春华, 周开军, 等. 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, 24(4):62-66. CHENG C L, YANG C H, ZHOU K J, et al. Flotation froth image texture extraction method based on fuzzy texture spectrum[J]. Miner. Eng. Res., 2009, 24(4):62-66.
[10]	XU D G, CHEN X, XIE Y F, et al. Complex networks-based texture extraction and classification method for mineral flotation froth images[J]. Minerals Engineering, 2015, 83:105-116.
[11]	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, (3):694-699.
[12]	WESLEY N G, BRUNO B M, ODEMIR M B. A complex network approach for dynamic texture recognition[J]. Neurocomputing, 2015, 153:211-220.
[13]	XU Y, QUAN Y H, ZHANG Z M, et al. Classifying dynamic textures via spatiotemporal fractal analysis[J]. Pattern Recognition, 2015, 48(10):3239-3248.
[14]	周云龙, 李莹, 赵红梅. 基于图像动态纹理特征的气固流化床流型识别[J]. 化学工程, 2011, 39(12):59-63. ZHOU Y L, LI Y, ZHAO H M. Flow regime identification of gas-solid fluidized bed based on images dynamic texture characteristics[J]. Chemical Engineering (China), 2011, 39(12):59-63.
[15]	牟学民. 矿物浮选泡沫图像序列动态特征提取及工业应用[D]. 长沙:中南大学, 2012. MU X M. Dynamic features extraction and its industrial application of mineral flotation froth image sequences[D]. Changsha:Central South University, 2012.
[16]	DORETTO G, CREMERS D, FAVARO P, et al. Dynamic texture segmentation[C]//Proceedings of IEEE International Conference on Computer Vision, 2003, 2:1236-1242
[17]	LUO B, HANCOCK E R. Structural graph matching using the EM algorithm and singular value decomposition[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2001, 23(10):1120-1136.
[18]	GAO X, XIAO B, TAO D, et al. Image categorization:Graph edit distance+edge direction histogram[J]. Pattern Recognition, 2008, 41(10):3179-3191.
[19]	WU Z X, LU X, DENG Y. Image edge detection based on local dimension:a complex networks approach[J]. Physica A:Statistical Mechanics and Its Applications, 2015, 440:9-18.
[20]	ALBERT R, BARABÁSI A. Statistical mechanics of complex networks[J]. Rev.Mod.Phys., 2002, 74(1):47-97.
[21]	周涛, 柏文洁, 汪秉宏, 等. 复杂网络研究概述[J]. 物理, 2005, 34(1):31-36. ZHOU T, BAI W J, WANG B H, et al. A brief review of complex networks[J]. Physics, 2005, 34(1):31-36.
[22]	NEWMAN M E J. The structure and function of complex networks[J]. SIAM Review, 2003, 45(2):167-256.
[23]	TANG J, JIANG B, CHANG C C, et al. Graph structure analysis based on complex network[J]. Digital Signal Processing, 2012, 22(5):713-725.
[24]	汪小帆. 复杂网络理论及其应用[M]. 北京:清华大学出版社, 2006. WANG X F. Complex Networks Theory and Its Application[M]. Beijing:Tsinghua University Press, 2006.
[25]	郭雷. 复杂网络[M]. 上海:上海科技教育出版社, 2006. GUO L. Complex Networks[M]. Shanghai:Shanghai Scientific and Technological Education Publishing House, 2006.
[26]	ZITOVA B, FLUSSER J. Image registration methods:a survey[J]. Image Vision Computing, 2003, 21(11):977-1000.
[27]	DACOSTA L F, RODRIGUES F A, TRAVIESO G, et al. Characterization of complex networks:a survey of measurements[J]. Advances in Physics, 2008, 56(1):167-242.
[28]	ZHANG B, ZHOU Y, XU X, et al. Dynamic structure evolution of time-dependent network[J]. Physica A Statistical Mechanics & Its Applications, 2016, 456:347-358.
[29]	ZHAN C, CHI K T, SMALL M. A general model for studying time evolution of transition networks[J]. Understanding Complex Systems, 2016, 73:373-393.
[30]	王勇, 胡士强. 基于混沌特征的运动模式分割和动态纹理分类[J]. 自动化学报, 2014, 40(4):604-614 WANG Y, HU S Q. Chaotic features for motion pattern segmentation and dynamic texture classification[J]. Acta Automatica Sinica, 2014, 40(4):604-614.
[31]	汤进, 陈影, 江波, 等. 基于复杂网络的图像建模与特征提取方法[J]. 计算机工程, 2013, 39(5):244-247. TANG J, CHEN Y, JIANG B, et al. Image modeling and feature extraction method based on complex network[J]. Computer Engineering, 2013, 39(5):244-247.

基于复杂网络时空特性的泡沫图像动态纹理特征分析

Analysis of dynamic texture features of floatation froth images based on space-time characteristics of complex networks

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