A Survey of Image Registration Techniques Using Neural Networks
Keywords:
Image registration, neural networks, non-linear transformationsAbstract
The importance of using neural networks for image registration has increased since the enhancement in technology responsible for capturing images. Traditional methods rely on manual selection of control points and/or finding a suitable geometric transformation that maps two images. This approach is especially tedious and time consuming for registering multiple images. Further, traditional methods are not able to register images effectively if non-linear transformations are used to convert one image into another. To provide a robust and efficient way of registering images, neural networks provide a powerful alternative. They have proved to be highly reliable especially with medical and satellite imaging; making room for uncertainty and imprecision. This paper highlights the important image registration approaches that make use of neural networks and performs a comparative analysis of these approaches. It also suggests suitable areas in which research can be carried out to improve the efficacy and scalability of the techniques.
References
Abche, A.B.; Yaacoub, F.; Maalouf, A.; Karam, E., "Image Registration based on Neural Network and Fourier Transform," in Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE , vol., no., pp.4803-4806, Aug. 30 2006-Sept. 3 2006
B. S. Reddy and B. N. Chatterji, “An FFT-based technique for translation, rotation, and scale-invariant image registration,” IEEE Trans. Image Processing, vol. 5, no. 8, pp. 1266-1270, August 1996
H. G. Barrow, J. M. Tenenbaum, R. C. Bolles, and H. C. Wolf, Parametric correspondence and chamfer matching, Proc. 5th Int’l J. Conf. Artificial Intelligence, pp. 659–663, 1977.
D. I. Barnea and H. F. Silverman, A class of algorithms for fast digital image registration, IEEE Trans. Computers, vol. 21, no. 2, pp. 179–186, 1972.
D. P. Huttenlocher, G. A. Klanderman, and W. J. Rucklidge, Comparing images using the Housdroff
distance, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 9, pp. 850–863, 1993.
Elhanany, Itamar, Mati Sheinfeld, Arie Beck, Yagil Kadmon, Naftali Tal, and Dan Tirosh. "Robust image registration based on feedforward neural networks." In Systems, Man, and Cybernetics, 2000 IEEE International Conference on, vol. 2, pp. 1507-1511. IEEE, 2000.
G. Borgefors, Hierarchical chamfer matching: A parametric edge matching algorithm, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 10, no. 6, pp. 849–865, 1998.
Chalermwat, Prachya. "High performance automatic image registration for remote sensing." PhD diss., George Mason University, 1999.
Gajre, Rachna P., and Leena Ragha. "Comparison of Image Registration Methods for Satellite Images." International Journal of Scientific and Research Publications, ISSN: 2250-3153.
G. C. Stockman, S. Kopstein, and S. Bennet, Matching images to models for registration and object detection via clustering, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 4, pp. 229–241, 1982.
Goshtasby, A. Ardeshir. Image registration: Principles, tools and methods. Springer Science & Business Media, 2012.
Le Beux, S.; Cazuguel, G.; Solaiman, B.; Roux, C., "Automatic feature determination using unsupervised neural networks. Application to image registration," in Neural Networks, 1996., IEEE International Conference on , vol.3, no., pp.1406-1409 vol.3, 3-6 Jun 1996
Misra, I.; Moorthi, S.M.; Dhar, D.; Ramakrishnan, R., "An automatic satellite image registration technique based on Harris corner detection and Random Sample Consensus (RANSAC) outlier rejection model," in Recent Advances in Information Technology (RAIT), 2012 1st International Conference on , vol., no., pp.68-73, 15-17 March 2012
P. E. Anuta, Spatial registration of multispectral and multitemporal digital imagery using fast Fourier transform techniques, IEEE Trans. Geoscience Electronics, vol. 8, no. 4, pp. 353–368, 1970.
Piraino, D.; Kotsas, P.; Richmond, B.; Recht, M.; Kormos, D., "Three dimensional image registration using artificial neural networks," in Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on , vol.6, no., pp.4017-4021 vol.6, 27 Jun- 2 Jul 1994
Sarnel, H.; Senol, Y.; Sagirlibas, D., "Accurate and robust image registration based on radial basis neural networks," in Computer and Information Sciences, 2008. ISCIS '08. 23rd International Symposium on , vol., no., pp.1-5, 27-29 Oct. 2008
Zhebin Qian; Jie-Gu Li, "Use of Hopfield neural network for complex image registration," in Tools with Artificial Intelligence, 1997. Proceedings., Ninth IEEE International Conference on , vol., no., pp.204-207, 3-8 Nov 1997
W. K. Pratt, Correlation techniques of image registration, IEEE Trans. Aerospace and Electronic Systems, vol. 10, no. 3, pp. 353–358, 1974.
Jianzhen Wu; Jianying Xie, "Zernike moment-based image registration scheme utilizing feedforward neural networks," in Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on , vol.5, no., pp.4046-4048 Vol.5, 15-19 June 2004
Shuxiu Wang; Sheng Lei; Faliang Chang, "Image registration based on neural network," in Information Technology and Applications in Biomedicine, 2008. ITAB 2008. International Conference on , vol., no., pp.74-77, 30-31 May 2008
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