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

References

• Suichies , M. , Leroux , D. Dechert , C. 2000 . An implementation of generalized predictive control in a flotation plant . Control Eng. Pract. , 8 ( 3 ) : 319 – 325 .
   Web of Science ®Google Scholar
• T. Y. Chai , Optimal operation control for complex industrial processes , in the 8th IFAC Symposium on Advance Control of Chemical Processes , eds. V. Kariwala , L. Samavedham and R.D. Braatz , Furama Riverfont , Singapore , 2012 , pp. 722 – 731 .
   Google Scholar
• Moolman , D. W. , Aldrich , C. and Schmitz , G. P. J. 1996 . The interrelationship between surface froth characteristics and industrial flotation performance . Miner. Eng. , 9 ( 8 ) : 837 – 854 .
   Web of Science ®Google Scholar
• Moolman , D. W. , Aldrich , C. , van-Deventer , J.S.J and Stange , W.W. 1995 . The classification of froth structures in a copper flotation plant by means of a neural net . Int. J. Miner. Process , 43 ( 3-4 ) : 193 – 208 .
   Web of Science ®Google Scholar
• Kaartinen , J. , Hatonen , J. Hyotyniemi , H. 2006 . Machine-vision-based control of zinc flotation-a case study . Control Eng. Pract. , 14 ( 12 ) : 1455 – 1466 .
   Web of Science ®Google Scholar
• Citir , C. , Aktas , Z. and Berber , R. 2004 . Off-line image analysis for froth flotation of coal . Comput. & Chem. Eng. , 28 ( 5 ) : 625 – 632 .
   Web of Science ®Google Scholar
• Holtham , P. N. and Nguyen , K. K. 2002 . On-line analysis of froth surface in coal and mineral flotation using JKFrothCam . Int. J. Miner. Process. , 64 ( 2-3 ) : 163 – 180 .
   Google Scholar
• Cilek , E. C. 2002 . Application of neural networks to predict locked cycle flotation test results . Miner. Eng. , 15 ( 12 ) : 1095 – 1104 .
   Web of Science ®Google Scholar
• Harrave , J. M. and Hall , S. T. 1997 . Diagnosis of concentrate grade and mass flowrate in tin flotation from color and surface texture analysis . Miner. Eng. , 10 ( 6 ) : 6 – 13 .
   Web of Science ®Google Scholar
• Ventura-Medin , A. E. and Cilliers , J. J. 2000 . Calculation of the specific surface area in floatation . Miner. Eng. , 13 ( 3 ) : 265 – 275 .
   Web of Science ®Google Scholar
• Aldrich , C. , Marais , C. , Shean , B. J. and Cilliers , J.J. 2010 . Online monitoring and control of froth flotation systems with machine vision: A review . Int. J. Miner. Process. , 96 ( 1-4 ) : 1 – 13 .
   Web of Science ®Google Scholar
• Bergh , L. G. and Yianatos , J. B. 2011 . The long way toward multivariate predictive control of flotation processes . J. Process Contr. , 21 ( 2 ) : 226 – 234 .
   Web of Science ®Google Scholar
• Bartolacci , G. , Pelletier , P. J. Tessier , J. J. 2006 . Application of numerical image analysis to process diagnosis and physical parameter measurement in mineral processes—part I: flotation control based on froth textural characteristics . Miner. Eng. , 19 ( 6-8 ) : 734 – 747 .
   Web of Science ®Google Scholar
• Liu , J. , Macgregor , J. F. Duchesne , C. 2005 . Flotation froth monitoring using multiresolutional multivariate image analysis . Miner. Eng. , 18 ( 1 ) : 65 – 76 .
   Web of Science ®Google Scholar
• Moolman , D. W. , Aldrich , C. Van Deventer , J. S. J. 1994 . Digital image processing as a tool for on-line monitoring of froth in flotation plants . Miner. Eng. , 7 ( 9 ) : 1149 – 1164 .
   Web of Science ®Google Scholar
• Hargrave , J. M. , Brown , G. J. and Hall , S. T. 1998 . A fractal characterization of the structure of coal froths . Coal Prep. , 19 ( 1-2 ) : 69 – 82 .
   Google Scholar
• J. Kaartinen , J. Hätönen and H. Hyötyniemi , et al. . Machine-vision-based control of zinc flotation—A case study , Control Eng. Pract ., 14 12 2006 , no.12 , pp. 1455 – 1466 .
   Web of Science ®Google Scholar
• X.M. Mu , J.P. Liu , W.H. Gui , et al. ., Machine vision based flotation froth mobility analysis , In the 29th Chinese Control Conference(CCC2010) , eds. J. Chen , Beijing , , China , 2010 , pp. 3012 – 3017 .
   Google Scholar
• S. H. Morar , M. C. Harris , D. J. Bradshaw , The use of machine vision to predict flotation performance , Miner. Eng ., 36-38 2012 , pp. 31 – 36 .
   Google Scholar
• Kurmyshev , E. V. , Poterasu , M. and Guillen-Bonilla , J. T. 2007 . The efficiency of texture image classification is certainly influenced by image scale when a feature space or a classification method is not scale invariant. An alternative approach . Appl. Opt. , 46 ( 9 ) : 1467 – 1476 .
   PubMedGoogle Scholar
• Cheong , C. , Bowman , G. and Han , T. D. 2008 . Unsupervised clustering approaches to color classification for color-based image code recognition . Appl. Opt. , 47 ( 13 ) : 2326 – 2345 .
   PubMed Web of Science ®Google Scholar
• Zhang , J. and Tan , T. 2002 . Brief review of invariant texture analysis methods . Pattern Recog. , 35 ( 3 ) : 735 – 747 .
   Web of Science ®Google Scholar
• Velisavljevic , L. and Elder , J. H. 2006 . Texture properties affecting the accuracy of surface attitude judgments . Vision Research , 46 ( 14 ) : 2166 – 2191 .
   PubMed Web of Science ®Google Scholar
• Grigorescu , S.E. , Petkov , N. and Kruizinga , P. 2002 . Comparison of texture features based on Gabor filters . IEEE T. on Image Process. , 11 ( 10 ) : 1160 – 1167 .
   PubMed Web of Science ®Google Scholar
• Su , W. , Zhang , C. Yang , J. 2012 . Analysis of wavelet packet and statistical textures for object-oriented classification of forest-agiculture ecotones using SPOT 5 imagery . Int J Remote Sens , 33 ( 11 ) : 3557 – 3579 .
   Web of Science ®Google Scholar
• Daugman , J. G. 1985 . Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters . Journal of the Optical Society of America A , 2 ( 7 ) : 1160 – 1169 .
   PubMedGoogle Scholar
• Yu , L. , He , Z. S. and Cao , Q. 2010 . Gabor texture representation method for face recognition using the Gamma and generalized Gaussian models . Image Vision Comput. , 28 ( 1 ) : 177 – 187 .
   Web of Science ®Google Scholar
• Wiskott , L. , Fellous , J. M. , Kruger , N. and Malsburg , C. von der . 1997 . Face recognition by elastic bunch graph matching . IEEE Trans. Pattern Anal. Mach. Intell. , 19 ( 4 ) : 775 – 779 .
   Google Scholar
• Arivazhagan , S. , Ganesan , L. and Padam-Priyal , S. 2007 . Texture classification using Gabor wavelets based rotation invariant features . Pattern Recog. Lett. , 27 ( 16 ) : 1976 – 1982 .
   Web of Science ®Google Scholar
• Manjunath , B. S. and Ma , W. Y. 1996 . Texture feature for browsing and retrieval of image data . IEEE Trans. Pattern Analysis and Machine Intel. , 18 ( 8 ) : 837 – 842 .
   Web of Science ®Google Scholar
• Lades , M. , Vorbrüggen , J.C. , Buhmann , J. Lange , J. 1993 . Distortion invariant object recognition in the dynamic link architecture . IEEE Trans. Comput. , 42 ( 3 ) : 300 – 311 .
   Web of Science ®Google Scholar
• Daugman , J. G. 1993 . High confidence visual recognition of persons by a test of statistical independence . IEEE T Pattern Anal , 15 ( 11 ) : 1148 – 1161 .
   Web of Science ®Google Scholar
• Zhang , B. 2007 . Histogram of Gabor phase patterns(HGPP): A novel object representation approach for face recognition . IEEE T Image Process , 16 ( 1 ) : 57 – 68 .
   PubMed Web of Science ®Google Scholar
• Lilliefors , H. W. 1967 . On the Kolmogorov-Sminov test for normality with mean and variance unknown . J. Am. Stat. Assoc. , 62 ( 318 ) : 399 – 402 .
   Web of Science ®Google Scholar
• Jorjani , E. , Asadollahi Poorali , H. Sam , A. 2009 . Prediction of coal response to froth flotation based on coal analysis using regression and artificial neural network . Miner. Eng. , 22 ( 11 ) : 970 – 976 .
   Web of Science ®Google Scholar
• Jorjani , E. , Asadollahi Poorali , H. Sam , A. 2009 . Prediction of coal response to froth flotation based on coal analysis using regression and artificial neural network . Miner. Eng. , 22 ( 11 ) : 970 – 976 .
   Web of Science ®Google Scholar
• Kohonen , T. 1997 . Self-Organizing Maps , Berlin : Springer .
   Google Scholar