[1] Wills, B.A., Napier-Munn, T.J. (2006). Mineral Processing Technology. Elsevier Science & Technology Books.
[2] Cubillos, F.A., Lima, E.L. (1997). Identification and optimizing control of rougher flotation circuit using an adaptable hybrid-neural model. Minerals Engineering, 10 (7), 707-721.
[3] Holtham P.N., Nguyen, L.K. (2002). On-line analysis of froth surface in coal and mineral flotation using JKFrothCam, International Journal of Mineral Processing, 64, 163-180.
[4] Haavisto, O., Kaartinen, J. (2009). Multichannel reflectance spectral assaying of zinc and copper flotation slurries. International Journal of Mineral Processing, 93 (2), 187-193.
[5] Moolman, D.M., Aldrich, C. and Van Deventer, J.S.J. (1995). The interpretation of flotation froth surfaces by using digital image analysis and neural networks. Chemical Engineering Science, 50, 3501-3513.
[6] Moolman, D.W., Eksteen, J.J., Aldrich, C. and Van Deventer, J.S.J. (1996a). The significance of flotation froth appearance for machine vision control. International Journal of Mineral Processing, 48(3-4), 135-158.
[7] Moolman, D.W., Aldrich, C., Schmitz, G.P.J. and Van Deventer, J.S.J. (1996b). The interrelationship between surface froth characteristics and industrial flotation performance. Minerals Engineering, 9 (8), 837-854.
[8] Kaartinen, J., Hatonen, J., Hyotyniemi, H. and Miettunen, J. (2006). Machine vision based control of zinc flotation-A case study. Control Engineering Practice, 14, 1455-1466.
[9] Vanegas, C. and Holtham, P. (2008). On-line froth acoustic emission measurements in industrial sites. Minerals Engineering, 21, 883-888.
[10] Aldrich, C., Marais, C., Shean, B.J. and Cilliers, J.J. (2010). On-line monitoring and control of froth flotation systems with machine vision: A review. International Journal of Mineral Processing, 96, 1-13.
[11] Morar, S.H., Harris, M.C. and Bradshaw, D.J. (2012). The use of machine vision to predict flotation performance. Minerals Engineering, 36-38, 31-36.
[12] Mehrabi, A., Mehrshad, N., Massinaei, M. (2014). Machine vision based monitoring of an industrial flotation cell in an iron flotation plant. International Journal of Mineral Processing, 133, 60-66.
[13] Aldrich, C., Moolman, D.W., Bunkell S.-J., Harris, M.C. and Theron, D.A. (1997). Relationship between surface froth features and process conditions in the batch flotation of a sulphide ore. Minerals Engineering, 10 (11), 1207-1218.
[14] Banford, A., Aktas, Z., Woodburn, E. (1998). Interpretation of the effect of froth structure on the performance of froth flotation using image analysis. Powder Technology, 98, 61-73.
[15] Bonifazi, G., Massacci, P. and Meloni, A. (2000). Prediction of complex sulfide flotation performances by a combined 3D fractal and colour analysis of the froths. Minerals Engineering, 13 (7), 737-746, 2000.
[16] Hargrave, J., Hall, S. (1997). Diagnosis of concentrate grade and mass flowrate in tin flotation from colour and surface texture analysis. Minerals Engineering, 10, 613-621.
[17] Hargrave, J., Miles, N., Hall, S. (1996). The use of grey level measurement in predicting coal flotation performance. Minerals Engineering, 9, 667-674.
[18] Jahedsaravani, A., Marhaban, M.H., Massinaei, M. (2014). Prediction of the metallurgical performances of a batch flotation system by image analysis and neural networks, Minerals Engineering, 69, 137-145.
[19] Gadkari, D. (2004). Image Quality Analysis Using GLCM. MSc. Thesis in Modeling and Simulation in the College of Arts and Sciences, University of Central Florida, Orlando, Florida.
[20] Marais, C., Aldrich, C. (2011). Estimation of platinum flotation grades from froth image data. Minerals Engineering, 24, 433-441.
[21] Gui, W., Liu, J., Yang, C., Chen, N., Liao, X. (2013). Color co-occurrence matrix based froth image texture extraction for mineral flotation. Minerals Engineering, 46-47, 60-67