Flow velocity measurement and analysis based on froth image SIFT features and Kalman filter for froth flotation

The flow velocity of flotation froth involves important information about the concentrate grade and the mineral recovery. It is significant to maintain the flow velocity of the froth at proper levels to achieve a good production performance in the machine vision-based process monitoring and control. However, the accurate velocity field measurement for the heavily deformed and seriously fragile froth bubbles is still a great challenge. Scale-invariant feature transform (SIFT) feature-based image matching provides an effective registration method for deformed objects, but most of the processing time would be wasted on the feature extraction and matching in irrelevant regions of the image pairs, which is unsuitable for online process monitoring. The Kalman filter is an effective tool to predict the next positions of the subblocks in the froth image sequences. Taking advantage of the merits of the SIFT and the Kalman filtering, an online froth velocity field measurement method based on the SIFT features and an improved Kalman filtering is presented in this paper. This method can obtain the accurate velocity field of various kinds of froth bubbles flowing to the scraper in the flotation cells, even including the bubbles with serious collapse and heavy deformation. The accuracy of the subblock registration is evaluated by a deformation model. After a long period of observation in a bauxite flotation plant, the relationship between the froth flow velocity and the production performance indices is analyzed and discussed. Finally, a kind of conclusive operation advice based on the relationship between the froth velocity and the flotation performance is established. The velocity measurement and the operation guidelines for the flotation operation and automatic control lay a foundation for the establishment of a flotation optimal control system based on machine vision monitoring.

Anahtar Kelimeler:

Froth images, froth flow velocity, scale invariant feature, Kalman filter, motion estimation, online monitoring

Flow velocity measurement and analysis based on froth image SIFT features and Kalman filter for froth flotation

Keywords:

Froth images, froth flow velocity, scale invariant feature, Kalman filter, motion estimation, online monitoring,

PDF

___

J. Kaartinen, J. Hatonen, H, Hyotyniemi, J. Miettunen, “Machine-vision-based control of zinc flotation-a case study”, Control Engineering Practice, Vol. 14, pp.1455–1466, 2006.
C.H. Yang, C.H. Xu, W.H. Gui, K.J. Zhou, “Application of highlight removal and multivariate image analysis to color measurement of flotation bubble images”, International Journal of Imaging Systems and Technology, Vol. 19, pp. 316–322, 2009.
P.N. Holtham, K.K. Nguyen, “On-line analysis of froth surface in coal and mineral flotation using JKFrothCam”, International Journal of Mineral Processing, Vol. 63, pp. 164–180, 2002.
D.W. Moolman, C. Aldrich, G.P.J. Schmitz, J.S.J. Van Deventer, “The interrelationship between surface froth characteristics and industrial flotation performance”, Minerals Engineering, Vol. 19, pp. 837–854, 1996.
F. Nunez, A. Cipriano, “Visual information model based predictor for froth speed control in flotation process”, Minerals Engineering, Vol. 22, pp. 366–371, 2009.
B. J. Shean, J.J. Cilliers, “A review of froth flotation control”, International Journal of Mineral Processing, Vol. 100, pp. 57–71, 2011.
C. Aldrich, C. Marais, B.J. Shean, J.J. Cilliers, “Online monitoring and control of froth flotation systems with machine vision: a review”, International Journal of Mineral Processing, Vol. 96, pp. 1–13, 2011.
J.B. Xu, L.M. Po, C.K. Cheung, “Adaptive motion tracking block matching algorithms for video coding”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 10, pp. 1025–1029, 1999.
H. Wang, R. Mersereau, “Fast algorithms for the estimation of motion vectors”, IEEE Trans. on Image Processing, Vol. 8, pp. 435–438, 1999.
D.J. Kelly, E.U. Azeloglu, P.V. Kochupura, G.S. Sharma, G.R. Gaudette, “Accuracy and reproducibility of a subpixel extended phase correlation method to determine micron level displacements in the heart”, Medical Engineering & Physics, Vol. 29, pp. 154–162, 2007.
S.S. Gleason, “Subpixel measure of image features based on paraboloid surface fit”, SPIE Proceedings – Machine Vision Systems Integration in Industry, SPIE, Vol. 1386, pp. 135–144,1990.
B.S. Reddy, B.N. Chatterji, “An FFT-based technique for translation, rotation, and scale-invariant image registration”, IEEE Transactions on Image Processing, Vol. 5, pp. 1266–1271, 1996.
D.G. Lowe, “Object recognition from local scale-invariant features”, International Conference on Computer Vision, pp. 1150–1157, 1999.
D.G. Lowe, “Distinctive image features from scale-invariant keypoints”, International Journal of Computer Vision, Vol. 60, pp. 91–110, 2004.
Y. Ke, R. Sukthankar, “PCA-SIFT: a more distinctive representation for local image descriptors”, Computer Vision and Pattern Recognition, Vol. 2, pp. 506–513, 2004.
M.H. Bahari, A. Karsaz, N. Pariz, “High Maneuvering target tracking using a novel hybrid Kalman filter-fuzzy logic architecture”, International Journal of Innovative Computing, Information and Control, Vol. 7, pp. 501–510, 20 I.A. Hameed, “Using the extended Kalman filter to improve the efficiency of greenhouse climate control”, International Journal of Innovative Computing, Information and Control, Vol. 6, pp. 2671–2680, 2010.
N. Sadr-kazemi, J.J. Cilliers, “An image processing algorithm for measurement of flotation froth bubble size and shape distributions”, Minerals Engineering, Vol. 10, pp. 1075–1083, 1997.
T. Kurita, N. Otsu, N. Abdelmalek, “Maximum likelihood thresholding based on population mixture models”, Pattern Recognition, Vol. 25, pp. 1231–1240, 1992.
A. Yilmaz, O. Javed, M. Shah, “Object tracking: a survey”, ACM Computer Surveys, Vol. 38, pp. 1–45, 2006. J. Beis, G.L. David, “Shape indexing using approximate nearest-neighbour search in high-dimensional spaces”, Conference on Computer Vision and Pattern Recognition, pp. 1000–1006, 1997.
X.M. Mu, J.P. Liu, W.H. Gui, Z.H. Tang, J.Q. Li, “Flotation froth motion velocity extraction and analysis based on SIFT features registration”, Information and Control, Vol. 40, pp. 525–531, 2011.
R.E. Kalman, “A new approach to linear filtering and prediction problems”, Journal of Basic Engineering, Vol. 82, pp. 35 −46, 1960.
T.I. Fossen, T. Perez, “Kalman filtering for positioning and heading control of ships and offshore rigs”, IEEE Transactions on Control Systems, Vol. 29, pp. 32 −46, 2009.
Y.M. Kim, “Object tracking in a video sequence”, CS 229 Final Project Report of Stanford University, 2007. V. Noblet, C. Heinrich, F. Heitz, J.P. Armspach, “Retrospective evaluation of a topology preserving non-rigid registration method”, Medical Image Analysis, Vol. 10, pp. 366–384, 2006.
H. Zhou, Y. Yuan, C. Shi, “Object tracking using SIFT features and mean shift”, Computer Vision and Image Understanding, Vol. 113, pp. 345–352, 2009.