Browse > Article
http://dx.doi.org/10.9708/jksci.2012.17.6.037

Multi-sensor Image Registration Using Normalized Mutual Information and Gradient Orientation  

Ju, Jae-Yong (Dept. of Electrical Engineering, Korea University)
Kim, Min-Jae (Dept. of Electrical Engineering, Korea University)
Ku, Bon-Hwa (Dept. of Visual Information Processing, Korea University)
Ko, Han-Seok (Dept. of Electrical Engineering, Korea University)
Publication Information
Journal of the Korea Society of Computer and Information / v.17, no.6, 2012 , pp. 37-48 More about this Journal
Abstract
Image registration is a process to establish the spatial correspondence between the images of same scene, which are acquired at different view points, at different times, or by different sensors. In this paper, we propose an effective registration method for images acquired by multi-sensors, such as EO (electro-optic) and IR (infrared) sensors. Image registration is achieved by extracting features and finding the correspondence between features in each input images. In the recent research, the multi-sensor image registration method that finds corresponding features by exploiting NMI (Normalized Mutual Information) was proposed. Conventional NMI-based image registration methods assume that the statistical correlation between two images should be global, however images from EO and IR sensors often cannot satisfy this assumption. Therefore the registration performance of conventional method may not be sufficient for some practical applications because of the low accuracy of corresponding feature points. The proposed method improves the accuracy of corresponding feature points by combining the gradient orientation as spatial information along with NMI attributes and provides more accurate and robust registration performance. Representative experimental results prove the effectiveness of the proposed method.
Keywords
Image registration; Multi-sensor images; Mutual information; Gradient orientation; Feature;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 C.R. Maurer Jr., R.J. Maciunas, J.M. Fitzpatrick, "Registration of Head CT Images to Physical Space using a Weighted Combination of Points and Surfaces," IEEE Transactions on Medical Imaging, Vol. 17, pp. 753-761, Oct. 1998   DOI   ScienceOn
2 J. Canny, "A Computational Approach to Edge Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 8, pp. 679-698, Nov. 1986   DOI   ScienceOn
3 M. Irani and P. Anandan, "Robust Multi-Sensor Image Alignment", Proceedings of IEEE International Conference on Computer Vision, pp. 959-966, Jan. 1998.
4 Fischler M A, Bolles R C, "Random Sample Consensus: A Paradigm For Model Fitting With Applications To Image Analysis And Automated Cartography," Communications of the ACM, pp. 381-395, June. 1981.
5 W. C. Choi, S. N. Choi, et al, "Automatic Image Registration Based on Extraction of Corresponding-Points for Multi-Sensor Image Fusion," Journal of the Korea Institute of Military Science and Technology, Vol. 12, No. 4, pp. 524-531, Aug. 2009.   과학기술학회마을
6 Juan Kang, et al, "Image Registration Based on Harris corner and Mutual Information," International Conference on Electronic & Mechanical Engineering and Information Technology, Aug. 2011.
7 C. Harris and M. J. Stephens, "A Combined Corner And Edge Detector," Alvey Vision Conference, pp.147-151, 1988.
8 O. Chum, J. Matas, and J. Kittler, "Locally Optimized RANSAC," Proc. Ann. Symp. German Assoc. for Pattern Recognition (DAGM '03), Sep. 2003.
9 S. M. Smith, J. M. Brady, "SUSAN-A New Approach to Low Level Image Processing," International Journal of Computer Vision 23, Vo. 23, No. 1, pp. 45-78, Nov. 1997.   DOI   ScienceOn
10 D. G. Lowe, "Distinctive Image Features from Scale- Invariant Keypoints," International Journal of Computer Vision, Vol. 60, No. 2, Oct. 2004.
11 Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing-3/E, PEARSON, 2009.
12 H. Farid, and E. P. Simoncelli, "Differentiation of Discrete Multidimensional Signals," IEEE Transactions on Image Processing, Vol. 13, No. 4, Apr. 2004.
13 J. P. W. Pluim, J. B. A. Maintz, M. A. Viergever, "Image Registration by Maximization of Combined Mutual Information and Gradient Information," IEEE Transactions on Medical Imaging Vol. 19, pp. 809-814, Feb. 2000.   DOI   ScienceOn
14 J. H. Lee, Y. S. Kim, et al, "Robust CCD and IR Image Registration Using Gradient-Based Statistical Information," IEEE Signal Processing Letters, Vol. 17, No. 4, Apr. 2010.
15 J. P. W. Pluim, J. B. A., Maintz, and M. A., Viergever, "Mutual-Information-Based Registration of Medical Images : A Survey," IEEE Transactions on Medical Imaging, Vol. 22, No. 8, pp. 986-1004, Aug. 2003.   DOI   ScienceOn
16 F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens, "Multimodality Image Registration by Maximization of Mutual Information," IEEE Transactions on Medical Imaging, Vol. 16, No. 2, pp. 187-198, Apr. 1997.   DOI   ScienceOn
17 H. Xie, L. E. Pierce, and F. T. Ulaby, "Mutual Information based Registration of SAR images," in Proc. of IEEE International Geoscience and Remote Sensing Symposium, Vol. 6, pp.4028-4031, July. 2003.
18 Zeger F. Knops, J. B. A. Maintz, M. A. Viergever and J. P. W. Pluim, "Registration using Segment Intensity Remapping and Mutual Information," International Conference on Medical Image Computing Computer-Assisted Intervention, pp. 805-812, Sep. 2004.
19 Y. S. Kim, J. H. Lee, and J. B. Ra, "Multi-sensor Image Registration based on Intensity and Edge Orientation Information," Pattern Recognition, Vol. 41, pp. 3356-3365, Apr. 2008.   DOI   ScienceOn
20 M. J. Kim, H. S. Ko, et al, "Robust Video Super Resolution Algorithm Using Measurement Validation Method and Scene Change Detection," EURASIP Journal on Advances in Signal Processing, Vol. 2011, No. 103, Nov. 2011.
21 B. Zitova and J. Flusser, "Image Registration Methods : A Survey," Image and Vision Computing, pp. 977-1000, June. 2003.