A Visual-Thermal Image Sequence Registration Method Based on Motion Status Statistic Feature Multi-Resolution Analysis

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Multi-sensor registration is an important and basic problem of intelligent surveillance. A novel visual-thermal image sequence registration method based on motion statistics feature multi-resolution analysis is proposed. In this method, motion statistics feature is utilized to select corresponding point pairs from visual-thermal synchronous video sequence. Then, multi-resolution analysis of motion statistic feature is done to choose proper scale. Finally, outliners are removed by RANSAC, and the geometry transformation parameters are optimized by LM algorithm. By using motion statistics feature, this method avoids the difficult problem of extracting invariant feature from two different image sensor and doesn’t depend on precise motion detection. Through multi-resolution analysis, the proposed approach can resolve the registration under the change of large scale. The performance was demonstrated on three groups of dataset, the results showed that our algorithm carried out precise image registration under the change of translation, scale and rotation.

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867-872

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June 2011

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© 2011 Trans Tech Publications Ltd. All Rights Reserved

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[1] K. Praveen, M. Ankush, K. Padamk: Study of Robust and Intelligent Surveillance in Visible and Multi-modal Framework. Informatica. Vol. 32 (2008), p.63.

Google Scholar

[2] J.P.W. Plum, J.B.A. Maintz, M.A. Viergever: Mutual-information-based Registration of Medical Images: A Survey. IEEE Transactions on Medical Imaging. Vol. 22 (2003), p.986.

DOI: 10.1109/tmi.2003.815867

Google Scholar

[3] X.S. Lu, S. ZHANG, H. Su, Y. Z Chen: Mutual Information-based Multimodal Image Registration Using A Novel Joint Histogram Estimation. Computerized Medical Imaging and Graphics. Vol. 32 (2008), p.202.

DOI: 10.1016/j.compmedimag.2007.12.001

Google Scholar

[4] F. Gao, G. J Wen, J.J. Lv : An Optimal Algorithm for IR/Visual image Registration Based on Main-line-pairs. Chinese Journal of Computers. Vol. 30 (2007), p.1014.

Google Scholar

[5] J.H. Lee, Y S Kim, D. Lee: Robust CCD and IR Image Registration Using Gradient-based Statistical Information. IEEE Signal Processing Letters. Vol. 17 (2010), p.347.

DOI: 10.1109/lsp.2010.2040928

Google Scholar

[6] T. Hrkac, Z. Kalafatic, J. Krapac: Infrared-visual Image Registration Based on Corners and Hausdorff Distance. In: Proc. of the 15th Scandinavian Conference. Vol. 4522 (2007), p.383.

DOI: 10.1007/978-3-540-73040-8_39

Google Scholar

[7] Y.S. Kim, J.H. Lee, J.B. Ra: Multi-sensor Image Registration Based on Intensity and Edge Orientation Information. Pattern Recognition. Vol. 41(2008), p.3356.

DOI: 10.1016/j.patcog.2008.04.017

Google Scholar

[8] L. Lei, Y.M. Jiang, G.Y. Kuang: A Method of the Remote Sensing Image Registration Based on Image Classification. Journal of National University of Defense Technology. Vol. 26(2004), p.35.

Google Scholar

[9] Y. Caspi, M. Irani: Spatio-temporal Alignment of Sequences. IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 24( 2002), p.1409.

DOI: 10.1109/tpami.2002.1046148

Google Scholar

[10] L. Lee, R. Romanos, G. Stein: Monitoring Activities from Multiple Video Streams: Establishing A Common Coordinate Frame. IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 22(2000), p.758.

DOI: 10.1109/34.868678

Google Scholar

[11] H. Ju, B. Bhanu: Fusion of Color and Infrared Video for Moving Human Detection. The Journal of the Pattern Recognition Society. Vol. 40 (2007), p.1771.

DOI: 10.1016/j.patcog.2006.11.010

Google Scholar

[12] Z. Szlavik, L. Havasi, S. Tamas: Estimation of Common Groundplane Based on Co-motion Statistics. In: Proc. of the International Conference on Image Analysis and Recognition. 2004, p.347.

DOI: 10.1007/978-3-540-30126-4_43

Google Scholar

[13] M.A. Fischler, R.C. Bolles: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communication of the ACM. Vol. 24 (1981), p.381.

DOI: 10.1145/358669.358692

Google Scholar

[14] K. Levenberg: A Method for the Solution of Certain Nonlinear Problems in Least Squares. Quarterly of Applied Mathematics. Vol. 2(1994), p.164.

Google Scholar

[15] D.W. Marquardt: An Algorithm for Least-squares Estimation of Nonlinear Inequalities. SIAM Journal on Applied Mathematics. Vol. 11 (1963), p.431.

Google Scholar