Abstract
In this paper, we develop a fast global registration method using random projection to reduce the dimensionality of images. By generating many transformed images from the reference, the nearest neighbour based image registration detects the transformation which establishes the best matching from generated transformations. To reduce computational cost of the nearest nighbour search without significant loss of accuracy, we first use random projection. To reduce computational complexity of random projection, we second use spectrum-spreading technique and circular convolution.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Healy, D.M., Rohde, G.K.: Fast global image registration using random projection. In: Proc. Biomedical Imaging: From Nano to Macro, pp. 476–479 (2007)
Sakai, T.: An efficient algorithm of random projection by spectrum spreading and circular convolution, Inner Report IMIT Chiba University (2009)
Sakai, T., Imiya, A.: Practical algorithms of spectral clustering: Toward large-scale vision-based motion analysis. In: Wang, L., Zhao, G., Cheng, L., Pietikäinen, M. (eds.) Machine Learning for Vision-Based Motion Analysis Theory and Techniques. Advances in Pattern Recognition. Springer, Heidelberg (2011)
Zitová, B., Flusser, J.: Image registration methods: A Survey. Image Vision and Computing 21, 977–1000 (2003)
Modersitzki, J.: Numerical Methods for Image Registration. In: CUP (2004)
Vempala, S.S.: The Random Projection Method. DIMACS, vol. 65 (2004)
Johnson, W., Lindenstrauss, J.: Extensions of Lipschitz maps into a Hilbert space. Contemporary Mathematics 26, 189–206 (1984)
Frankl, P., Maehara, H.: The Johnson-Lindenstrauss lemma and the sphericity of some graphs. Journal of Combinatorial Theory Series A 44, 355–362 (1987)
van Ginneken, B., Stegmann, M.B., Loog, M.: Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database. Medical Image Analysis 10, 19–40 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Itoh, H., Lu, S., Sakai, T., Imiya, A. (2011). Global Image Registration by Fast Random Projection. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2011. Lecture Notes in Computer Science, vol 6938. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24028-7_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-24028-7_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24027-0
Online ISBN: 978-3-642-24028-7
eBook Packages: Computer ScienceComputer Science (R0)