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Robust Block-Matching Motion Estimation of Flotation Froth Using Mutual Information
Anthony Amankwah , Chris Aldrich
Pages - 380 - 388     |    Revised - 15-09-2012     |    Published - 24-10-2012
Volume - 6   Issue - 5    |    Publication Date - October 2012  Table of Contents
Mutual Information, Mean Sum of Absolute Difference, Motion Estimation, Froth Flotation
In this paper, we propose a new method for the motion estimation of flotation froth using mutual information with a bin size of two as the block matching similarity metric. We also use three-step search and new-three-step-search as a search strategy. Mean sum of absolute difference (MAD) is widely considered in blocked based motion estimation. The minimum bin size selection of the proposed similarity metric also makes the computational cost of mutual information similar to MAD. Experimental results show that the proposed motion estimation technique improves the motion estimation accuracy in terms of peak signal-to-noise ratio of the reconstructed frame. The computational cost of the proposed method is almost the same as the standard machine vision methods used for the motion estimation of flotation froth.
CITED BY (1)  
1 Amankwah, A., & Aldrich, C. (2015, July). Motion estimation in flotation froth using the Kalman filter. In Geoscience and Remote Sensing Symposium (IGARSS), 2015 IEEE International (pp. 1897-1900). IEEE.
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1 C. Aldrich, C. Marais, B.J. Shean and J.J. Cilliers. “Online monitoring and control of froth flotation systems with machine vision: A review”. International Journal of Mineral Processing, 96(1-4), 1-13, 2010.
2 C. Marais and C. Aldrich. “Estimation of platinum grades from flotation froth images”.Minerals Engineering, 24(5), 433-441, 2010.
3 C.P. Botha. 1999. An Online Machine Vision Froth Flotation Analysis Platform.M.Sc.thesis, University of Stellenbosch, Stellenbosch, South Africa.
4 D.P. Kottke and Y. Sun. “Motion estimation via cluster matching”. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(11), 1128-1132, 1994.
5 N. Barbian, J.J. Cilliers, S.H. Morar, D.J. Bradshaw. “Froth imaging, air recovery and bubble loading to describe flotation bank performance”. International Journal of Mineral Processing 84(1-4), 81–88, 2007.
6 K.K. Nguyen and A.J. Thornton. “The application of texture based image analysis techniques in froth flotation”. In: Anthony Maeder & Brian Lovell (Eds.), Proceedings of the DICTA-95, the 3rd Conference on Digital Imaging Computing Techniques and Applications, 371–376, 1995, Brisbane, Australia, 6–8 December, St John's College,University of Queensland.
7 D.W. Moolman, C. Aldrich, J.S.J. van Deventer and W.W. Stange. “Digital image processing as a tool for on-line monitoring of froth in flotation plants”. Minerals Engineering 7 (9), 1149–1164, 1994.
8 J.M. Jou, P.-Y. Chen, and J.-M. Sun, “The gray prediction search algorithm for block motion estimation,” IEEE Trans. Circuits Syst. Video Technol., vol. 9, no. 6, pp. 843–848,Sep. 1999.
9 T. Koga, K. Linuma, A. Hirano, Y. Lijima, and T. Ishiguro, “Motion-compensated interframe coding for video conferencing”, in Proc. NTC’81, 1981, pp. G5.3.1–G5.3.5.
10 Renxiang Li, Bing Zeng, and Ming L. Liou, “A New Three-Step Search Algorithm for Block Motion Estimation”, IEEE Trans. Circuits And Systems For Video Technology, vol 4., no.4, pp. 438-442, August 1994.
11 L.M. Po and W.C. Ma, “A novel four-step search algorithm for fast block motion estimation”, IEEE Trans. Circuits Syst. Video Technol., vol.6, no. 3, pp. 313–317, Jun.1996.
12 J. Jain and A. Jain, “Displacement measurement and its application in internal image coding”, IEEE Trans. Commun., vol. 29, no. COM–12, pp. 1799–1808, Dec. 1981.
13 S. Zhu and K.-K. Ma, “A new diamond search algorithm for fast block matching motion estimation”, IEEE Trans. Image Process., vol. 9, no. 2,pp. 287–290,Feb.2000.
14 C.E. Shannon “A mathematical theory of communication,” Bell System technical Journal,vol.27, pp 379-423, 1948.
15 F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens, “Multimodality image registration by maximization of mutual information”, IEEE Trans. Med. Imag., vol.16, Apr. 1997.
16 W.M. Wells III, P. Viola, H. Atsumi, S. Nakajima, and R. Kikinis, “Multi-modal volume registration by maximization of mutual information”, Med. Imag. Anal., vol. 1, pp. 35–51,1996.
17 P. Thevenaz and M. Unser, “Optimization of mutual information for multiresolution image registration”, IEEE Trans. Image Processing, vol. 9, pp. 2083–2099, Dec. 2000.
18 J. C. Spall, “Multivariate stochastic approximation using a simultaneous perturbation gradient approximation,” IEEE Trans. Automat. Contr., vol.37, no. 3, pp. 332–341, 1992.
Anthony Amankwah
- South Africa
Mr. Chris Aldrich
Curtin University - Australia