Machine Vision Based Production Condition Classification and Recognition for Mineral Flotation Process Monitoring

Abstract A novel froth image analysis based production condition recognition method is presented to identify the froth phases under various production conditions. Gabor wavelet transformation is employed to froth image processing firstly due to the ability of Gabor functions in simulating the response of the simple cells in the visual cortex. Successively, the statistical distribution profiles based feature parameters of the Gabor filter responses rather than the conventional mean and variance are extracted to delineate the essential statistical information of the froth images. The amplitude and phase representations of the Gabor filter responses are both taken into account by empirical marginal and joint statistical modeling. At last, a simple learning vector quantization (LVQ) neural network model is used to learn an effective classifier to recognize the froth production conditions. The effectiveness of this method is validated by the real production data on industrial scale from a bauxite dressing plant.

[1]  Norbert Krüger,et al.  Face Recognition by Elastic Bunch Graph Matching , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Chris Aldrich,et al.  The interrelationship between surface froth characteristics and industrial flotation performance , 1996 .

[3]  Tieniu Tan,et al.  Brief review of invariant texture analysis methods , 2002, Pattern Recognit..

[4]  J. Daugman.  Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[5]  Jayson Tessier,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 , 2006 .

[6]  John Daugman,et al.  High Confidence Visual Recognition of Persons by a Test of Statistical Independence , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Lei Deng,et al.  Analysis of wavelet packet and statistical textures for object-oriented classification of forest-agriculture ecotones using SPOT 5 imagery , 2012 .

[8]  Chris Aldrich,et al.  The classification of froth structures in a copper flotation plant by means of a neural net , 1995 .

[9]  Wei-Hua Gui,et al.  Machine vision based flotation froth mobility analysis , 2010, Proceedings of the 29th Chinese Control Conference.

[10]  Jani Kaartinen,et al.  Machine-vision-based control of zinc flotation—A case study , 2006 .

[11]  Norbert Krüger,et al.  Face recognition by elastic bunch graph matching , 1997, Proceedings of International Conference on Image Processing.

[12]  E. C. Çilek,et al.  Application of neural networks to predict locked cycle flotation test results , 2002 .

[13]  H. Lilliefors.  On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown , 1967 .

[14]  Peter N. Holtham,et al.  On-line analysis of froth surface in coal and mineral flotation using JKFrothCam , 2002 .

[15]  Nicolai Petkov,et al.  Comparison of texture features based on Gabor filters , 2002, IEEE Trans. Image Process..

[16]  Tack-Don Han,et al.  Unsupervised clustering approaches to color classification for color-based image code recognition. , 2008, Applied optics.

[17]  John F. MacGregor,et al.  Flotation froth monitoring using multiresolutional multivariate image analysis , 2005 .

[18]  Jan J. Cilliers,et al.  Calculation of the specific surface area in flotation , 2000 .

[19]  Juan Yianatos,et al.  The long way toward multivariate predictive control of flotation processes , 2011 .

[20]  J. M. Hargrave,et al.  Diagnosis of concentrate grade and mass flowrate in tin flotation from colour and surface texture analysis , 1997 .

[21]  Chris Aldrich,et al.  Online monitoring and control of froth flotation systems with machine vision: A review , 2010 .

[22]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[23]  Tianyou Chai.  Optimal Operational Control for Complex Industrial Processes , 2012 .

[24]  Lei Yu,et al.  Gabor texture representation method for face recognition using the Gamma and generalized Gaussian models , 2010, Image Vis. Comput..

[25]  Wen Gao,et al.  Histogram of Gabor Phase Patterns (HGPP): A Novel Object Representation Approach for Face Recognition , 2007, IEEE Transactions on Image Processing.

[26]  Ridvan Berber,et al.  Off-line image analysis for froth flotation of coal , 2004, Comput. Chem. Eng..

[27]  Chris Aldrich,et al.  Digital image processing as a tool for on-line monitoring of froth in flotation plants , 1994 .

[28]  Sameer H. Morar,et al.  The use of machine vision to predict flotation performance , 2012 .

[29]  Arivazhagan Selvaraj,et al.  Texture classification using Gabor wavelets based rotation invariant features , 2006, Pattern Recognit. Lett..

[30]  S. Chehreh Chelgani,et al.  Prediction of coal response to froth flotation based on coal analysis using regression and artificial neural network , 2009 .

[31]  M. Suichies,et al.  An implementation of generalized predictive control in a flotation plant , 1998 .

[32]  Joachim M. Buhmann,et al.  Distortion Invariant Object Recognition in the Dynamic Link Architecture , 1993, IEEE Trans. Computers.

[33]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[34]  James H. Elder,et al.  Texture properties affecting the accuracy of surface attitude judgements , 2006, Vision Research.

[35]  Evguenii V Kurmyshev,et al.  Image scale determination for optimal texture classification using coordinated clusters representation. , 2007, Applied optics.

[36]  S. T. Hall,et al.  A Fractal Characterisation of the Structure of Coal Froths , 1998 .