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Acknowledgments Xiaobo Deng would like to acknowledge his useful discussion with Dr. Xuezhi Wang (Department of EEE, The University of Melbourne), and thank the China Scholarship Council for supporting his visit at the Department of EEE, The University of Melbourne. References: [1] Reed, I., Gagliardi, R. and Stotts, L.: ‘A recursive moving-target-indication algorithm for optical image sequences’, IEEE Trans. Aerosp. Electron. Syst., 1990, 26, (1), pp. 434-440 [2] Barniv, Y.: ‘Dynamic programming solution for detecting dim moving targets’. IEEE Trans. Aerosp. Electron. Syst., 1985, 21, (1), pp. 141-155 [3] Arnold, J., Shaw, S., and Pasternack, H.: ‘Efficient target tracking using dynamic programming. IEEE Trans. Aerosp. Electron. Syst., 1993, 29, (1), pp. 44-56 [4] Xiong. Y, Peng, J. X., Ding, M. Y., and Xue, D. H.: ‘An extended track-before-detect algorithm for infrared target detection’, IEEE Trans. Aerosp. Electron. Syst., 1997, 33, (3), pp. 1087-1092 [5] Tonissen, S. M., and Evans, R. J.: ‘Performance of dynamic programming techniques for trackbefore-detect’, IEEE Trans. Aerosp. Electron. Syst., 1996, 32, (4), pp. 1440-1451 [6] Wallace, W. R.: ‘The use of track-before-detect in pulse-Doppler radar’. Proc. Int. Conf. Radar 2002, pp. 315-319 [7] Buzzi, S., Lops, M., and Venturino, L.: ‘Track-before-detect procedures for early detection of moving target from airborne radars’, IEEE Trans. Aerosp. Electron. Syst., 2005, 41, (3), pp. 937- 954 [8] Buzzi, S., Lops, M., and Ferri, M.: ‘Track-before-detect procedures in a Multi-target environment’, IEEE Trans. Aerosp. Electron. Syst., 2008, 44, (3), pp. 1135-1148 [9] Seah, C. and Hwang, I.: ‘State estimation for stochastic linear hybrid systems with continuousstate-dependent transitions: an IMM approach’, IEEE Trans. Aerosp. Electron. Syst., 2009, 45, (1), pp. 376-392 [10] Blom, H., and Bloem, E.: ‘Exact Bayesian and particle filtering of stochastic hybrid systems’, IEEE Trans. Aerosp. Electron. Syst., 2007, 43, (1), pp. 50-70 [11] Johnston, L. A., and Krishnamurthy, V.: ‘Performance analysis of a dynamic programming track before detect algorithm’, IEEE Trans. Aerosp. Electron. Syst., 2002, 38, (1), pp. 228-242 26
Fast realization of Automatic optical and infrared image registration Weiping Yang 1 2 * , Xuezhi Wang 2 , Bill Moran 2 * Presenter 1. National University of Defence Technology, Hunan, 410073, China 2. The University of Melbourne, Parkville, VIC 3010, Australia Because of the requirement of practical applications, it is necessary for image registration between different source images. However, the image characteristics from different imaging sensors are different, so the wanted results can not be obtained if the intensive images are directly used. In order to solve this problem, edge detection and extraction methods should be applied, then the correlation tactics is used. But because the standard cross correlation method is time expensive, fast realization method should be adopted. Here we use the changeable resolution method to do this, and get good results with only less than one fortieth time consuming of the standard algorithm. Keywords automatic image registration, edge detection, changeable resolution, normalized cross correlation 1. Introduction Water resource is the key factor of human being social development, and paid more attention by the world. The issue of how to use the limited freshwater effectively is one of the subjects in all over the world. In order to study automatic water saving irrigation technique, we need to know the water status of the plants. To say further, if we want to know the plant water status, we need to know the canopy temperature at first. Therefore, researchers have done many fruitful results [1-5] . Using nondestructive optical image sensing technique to obtain the canopy temperature is a challenging project. The first step is to automatically register the infrared image with the optical image, then extract the temperature of the interested regions, and finally can get the temperature estimation of the canopy. As we all know, researchers have presented many algorithms on image registration and image match, for example, cross correlation [6] , mutual information [7] , correlation ratio [8] , and SIFT based methods [9] . However, for image registration between different source image pairs, there are some practical problems if we directly use the intensive images to do the image registration processing, in reference [10] we have pointed out, here we only discuss its fast realization. It is because of the existing differences between optical and infrared images that we should edge the images at first, then using certain image registration method to process the edge images. Because of the time consuming, standard cross correlation( normalized cross correlation) method is improper, the changeable resolution method based normalized cross correlation may be the best choice. Experiment tests show that the changeable resolution normalized cross correlation method has not only the same good registration results, but also fast running speed with less than one fortieth time consuming of the standard cross correlation method. The paper is organized as follows: In Section 2 the algorithm is described and the flow diagram is given. Section 3 is the implementation of the above algorithm. The experiment results are presented in Section 4, then followed by the discussion and conclusion. 2. Changeable resolution algorithm based on normalized cross correlation When we want to register the IR image to the reference optical image, we should obtain its location and rotation angle. In general, the IR image is wholly overlapped with the reference optical image. Moreover, we assume that the space resolution of the two images is the same. And then the changeable resolution algorithm based on normalized cross correlation can be shown as Fig 1. Because it needs to calculate the correlation coefficient pixel by pixel, normalized cross correlation (NCC) algorithm is time expensive. In order to solve the problem of the slowly running 27
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