Abstract
Image registration is an important step for a great variety of applications such as remote sensing, medical imaging, and multi-sensor fusion-based target recognition. The objective is to find, in a huge search space of geometric transformations, an acceptable accurate solution in a reasonable time to provide better registered images for high quality products. In the broad area of global optimization methods, Genetic Algorithms form a widely accepted trade-off between global and local search strategies. They are well-investigated and have proven their applicability in many fields. In this paper, we present an efficient 2D point based rigid image registration method integrating the advantage of the robustness of GAs in finding the best transformation between two images. The algorithm is applied for registering SPOT images and the results show the effectiveness of this approach.
Chapter PDF
Similar content being viewed by others
References
Besl, P.J., McKay, N.D.: A method for registration of 3D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(2), 239–256 (1992)
Stoddart, A.J., Hilton, A.: Registration of multiple point sets. In: Proc. ICPR, pp. 40–44 (1996)
Goldberg, D.E.: Genetic Algorithm in search, optimization and machine learning. Addison Wesley (1989)
Jacq, J., Roux, C.: Registration of 3D images by genetic optimization. Pattern Recognition Letters 16, 823–841 (1995)
Brunnström, K., Stoddart, A.J.: Genetic algorithms for free-form surface matching. In: Proc. 13th International Conference on Pattern Recognition, vol. 4, pp. 689–693 (1996)
Chetverikov, D., Stepanov, D., Krsek, P.: Robust Euclidean alignment of 3D point sets: the trimmed iterative closest point algorithm. Image and Vision Computing 23(3), 299–309 (2005)
Holland, J.H.: Adaptation in Natural and Artificial System. University of Michigan Press (1975)
Coley, D.: An Introduction to Genetic Algorithms for Scientists and Engineers. World Scientific Press (1999)
Chow, C.K., Tsui, H.T., Lee, T.: Fast Free-form Surface Registration by A New Genetic Algorithm. In: The Fifth Asian Conference on Computer Vision, Melbourne (2002)
da Cunha, A.L., Zhou, J., Do, M.N.: The Nansubsampled Contourlet Transform: Theory, design, and applications. IEEE Trans. on Image Processing 15(10), 3089–3101 (2006)
Serief, C., Barkat, M., Bentoutou, Y., Benslam, M.: Robust feature points extraction for image registration based on the nonsubsampled contourlet transform. International Journal of Electronics and Communications (63), 148–152 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Meskine, F., Taleb, N., Almhdie-Imjabber, A. (2012). A 2D Rigid Point Registration for Satellite Imaging Using Genetic Algorithms. In: Elmoataz, A., Mammass, D., Lezoray, O., Nouboud, F., Aboutajdine, D. (eds) Image and Signal Processing. ICISP 2012. Lecture Notes in Computer Science, vol 7340. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31254-0_50
Download citation
DOI: https://doi.org/10.1007/978-3-642-31254-0_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31253-3
Online ISBN: 978-3-642-31254-0
eBook Packages: Computer ScienceComputer Science (R0)