Abstract
We propose a three-dimensional global image registration method for a sparse dictionary. To achieve robust and accurate registration, which based on template matching, a large number of transformed images are prepared and stored in the dictionary. To reduce the spatial complexity of this image dictionary, we introduce a method of generating a new template image from a collection of images stored in the image dictionary. This generated template image allows us to achieve accurate image registration even if the population of the image dictionary is relatively small and the template has a small pattern perturbation. To further reduce the complexity, we compute a matching process in a low-dimensional Euclidean space projected by a random projection.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Nishino, K., Ikeuchi, K.: Robust Simultaneous Registration of Multiple Range Images. In: Digitally Archiving Cultural Objects, pp. 71–88. Springer, New York (2008)
Salvi, J., Matabosch, C., Fofi, D., Forest, J.: A review of recent range image registration methods with accuracy evaluation. Image Vis. Comput. 25, 578–596 (2007)
Besl, P., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Analy. Mach. Intell. 14, 239–256 (1992)
Daniel, F.H., Hebert, M.: Fully automatic registration of multiple 3D data sets. Image Vis. Comput. 21, 637–650 (2003)
Markelj, P., Tomaẑevič, D., Likar, B., Pernus, F.: A review of 3D/2D registration methods for image-guided interventions. Med. Image Anal. 16, 642–661 (2012)
Klein, A., Andersson, J., Ardekani, B.A., Ashburner, J., Avants, B.B., Chiang, M.C., Christensen, G.E., Collins, D.L., Gee, J.C., Hellier, P., Song, J.H., Jenkinson, M., Lepage, C., Rueckert, D., Thompson, P.M., Vercauteren, T., Woods, R.P., Mann, J.J., Parsey, R.V.: Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. NeuroImage 46, 786–802 (2009)
Capekm, M.: Optimisation strategies applied to global similarity based image registration methods. In: Proceedings of the 7th International Congerence in Central Europoe on Computer Graphic, pp. 369–374 (1999)
Itoh, H., Lu, S., Sakai, T., Imiya, A.: Global image registration by fast random projection. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Wang, S., Kyungnam, K., Benes, B., Moreland, K., Borst, C., Di Verdi, S., Yi-Jen, C., Ming, J. (eds.) ISVC 2011, Part I. LNCS, vol. 6938, pp. 23–32. Springer, Heidelberg (2011)
Itoh, H., Lu, S., Sakai, T., Imiya, A.: Interpolation of reference images in sparse dictionary for global image registration. In Proceedings of the 8th International Symposium on Visual Computing, pp. 657–667 (2012)
Itoh, H., Sakai, T., Kawamoto, K., Imiya, A.: Global image registration using random projection and local linear method. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds.) CAIP 2013, Part I. LNCS, vol. 8047, pp. 564–571. Springer, Heidelberg (2013)
Cock, K.D., Moor, B.D.: Subspace angles between ARMA models. Syst. Control Lett. 46, 265–270 (2002)
Hamm, J., Lee, D.D.: Grassmann discriminant analysis: a unifying view on subspace-based learning. In: Proceedings of the International Conference on Machine Learning, pp. 376–383 (2008)
Altman, N.S.: An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 46, 175–185 (1992)
Vempala, S.S.: The Random Projection Method, vol. 65. American Mathematical Society, Providence (2004)
Arya, S., Mount, D.M., Netanyahu, N.S., Silverman, R., Wu, A.Y.: An optimal algorithm for approximate nearest neighbor searching in fixed dimensions. In Proceedings of ACM-SIAM Symposium on Discrete Algorithms, pp. 573–582 (1994)
Baraniuk, R.G., Wakin, M.B.: Random projections of smooth manifolds. Found. Comput. Math. 9, 51–77 (2009)
Bingham, E., Mannila, H.: Random projection in dimensionality reduction: applications to image and text data. In: Proceedings of the International Conference on Knowledge Discovery and Data Mining, pp. 245–250 (2001)
Johnson, W., Lindenstrauss, J.: Extensions of Lipschitz maps into a Hilbert space. Contemp. Math. 26, 189–206 (1984)
Frankl, P., Maehara, H.: The Johnson-Lindenstrauss lemma and the sphericity of some graphs. Comb. Theory Ser. B 44, 355–362 (1988)
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, pp. 3–26. Springer, London (2011)
Cocosco, C., Kollokian, V., Kwan, R.S., Evans, A.: Brainweb. Online interface to a 3D MRI simulated brain database. NeuroImage 5, 425 (1997)
Boye, D., Samei, G., Schmidt, J., Székely, G., Tanner, C.: Population based modeling of respiratory lung motion and prediction from partial information. In: Proceedings of SPIE, vol. 8669, Medical Imaging 2013: Image Processing 8669 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Itoh, H., Imiya, A., Sakai, T. (2015). Global Volumetric Image Registration Using Local Linear Property of Image Manifold. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9008. Springer, Cham. https://doi.org/10.1007/978-3-319-16628-5_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-16628-5_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16627-8
Online ISBN: 978-3-319-16628-5
eBook Packages: Computer ScienceComputer Science (R0)