Abstract
In this paper, we propose a new method to improve the image registration accuracy in feedforward neural networks (FNN) based scheme. In the proposed method, Bayesian regularization is applied to improve the generalization capability of the FNN. The features extracted from the image sets by kernel independent component analysis (KICA) technique are input vectors of regularized FNN. The outputs of the neural network are those translation, rotation and scaling parameters with respect to reference and observed image sets. Comparative experiments are performed between FNN with regularization and without regularization under various conditions. The results show that the proposed method is much improved not only at accuracy but also remarkably at robust to noise.
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References
Elhanany, I., Sheinfeld, M., Beckl, A., et al.: Robust Image Registration Based on Feedforward Neural Networks. In: IEEE International Conference on System, Man and Cybernetics, vol. 2, pp. 1507–1511 (2000)
Wu, J., Xie, J.: Zernike Moment-based Image Registration Scheme Utilizing Feedforward Neural Networks. The 5th World Congress on Intelligent Control and Automation 5, 4046–4048 (2004)
Xu, A.B., Guo, P.: Isomap and Neural Networks based Image Registration Scheme. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3972, pp. 486–491. Springer, Heidelberg (2006)
Xu, A.B., Jin, X., Guo, P., Bie, R.F.: KICA Feature Extraction in Application to FNN based Image Registration. In: The 2006 International Joint Conference on Neural Networks (to appear)
Guo, P.: Studies of Model Selection and Regularization for Generalization in Neural Networks with Applications. PhD Thesis, the Chinese University of Hong Kong (2001)
Liu, Q.S., Cheng, J., Lu, H., Ma, S.: Modeling Face Appearance with Nonlinear Independent Component Analysis. In: Sixth IEEE International Conference on Automatic Face and Gesture Recognition (FGR 2004), vol. 2, pp. 761–766 (2004)
Cheng, J., Liu, Q.S., Lu, H.: Texture Classification Using Kernel Independent Component Analysis. In: Proceedings of the 17th Int. Conf. on Pattern Recognition, vol. 1, pp. 620–623 (2004)
MacKay, D.J.C.: Bayesian interpolation. Neural Computation 4(3), 415–447 (1992)
MacKay, D.J.C.: A practical Bayesian framework for backpropagation networks. Neural Computation 4(3), 448–472 (1992)
Marquardt, D.: An Algorithm for Least-squares Estimation of Nonlinear Parameters. SIAM Journal Applied Mathematics 2, 431–441 (1963)
Hagan, M.T., Menhaj, M.: Training Feedforward Networks with Marquardt Algorithm. IEEE Trans. Neural Networks 1(1), 113–118 (1994)
Foresee, F.D., Hagan, M.T.: Gauss-Newton approximation to Bayesian regularization. In: Proceedings of the 1997 International Joint Conference on Neural Networks, pp. 1930–1935 (1997)
Doan, C.D., Liong, S.Y.: Generalization for Multilayer Neural Network: Bayesian Regularization or Early Stopping. In: Proceedings of Asia Pacific Association of Hydrology and Water Resources 2nd Conference (2004)
Guo, P., Lyu, M.R., Chen, C.L.P.: Regularization Parameter Estimation for Feedforward Neural Networks. IEEE Trans. Neural Networks 33(1), 35–44 (2003)
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Xu, A., Guo, P. (2006). Image Registration with Regularized Neural Network. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_32
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DOI: https://doi.org/10.1007/11893257_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
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