Abstract
Over the years, image registration has been largely employed in medical applications, robotics and geophysics. More recently, it has increasingly drawn attention of security and defense industries, particularly aiming at threat detection automation. This paper first introduces a short overview of mathematical methods for image registration, with a focus on variational approaches. In a second part, a specific registration task is presented: the optimal alignment between X-ray scans of an inspected vehicle and an empty reference of the same car model. Indeed, while being scanned by dedicated imaging systems, the car speed is not necessarily constant which may entail non-rigid deformations in the resulting image. The paper simply addresses this issue by applying a rigid transform on the reference image before using the variational framework solved in one dimension. For convergence and speed purposes, line-search techniques and a multiscale approach are used.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Haber, E., Heldmann, S., Modersitzki, J.: A computational framework for image-based constrained registration. Linear Algebra Appl. 431(3), 459–470 (2009)
Haber, E., Modersitzki, J.: A multilevel method for image registration. SIAM J. Sci. Comput. 27(5), 1594–1607 (2006)
Modersitzki, J.: FAIR: Flexible Algorithms for Image Registration, vol. 6. SIAM, New Delhi (2009)
Fischer, B., Modersitzki, J.: Fast image registration: a variational approach. In: Psihoyios, G. (ed.) Proceedings of the International Conference on Numerical Analysis and Computational Mathematics, pp. 69–74. Wiley, Hoboken (2003)
Fischer, B., Modersitzki, J.: A unified approach to fast image registration and a new curvature based registration technique. Linear Algebra Appl. 380, 107–124 (2004)
Haber, E., Ascher, U.M., Oldenburg, D.: On optimization techniques for solving nonlinear inverse problems. Inverse Probl. 16(5), 1263 (2000)
Modersitzki, J.: Numerical Methods for Image Registration. Oxford University Press on Demand, Oxford (2004)
Haber, E., Modersitzki, J.: Intensity gradient based registration and fusion of multi-modal images. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 726–733. Springer, Heidelberg (2006). doi:10.1007/11866763_89
Crum, W.R., Hartkens, T., Hill, D.L.G.: Non-rigid image registration: theory and practice. Br. J. Radiol. (2004)
Christensen, G.E.: Deformable shape models for anatomy (Doctoral dissertation, Washington University) (1994)
Broit, C.: Optimal registration of deformed images (1981)
Thirion, J.P.: Image matching as a diffusion process: an analogy with Maxwell’s demons. Med. Image Anal. 2(3), 243–260 (1998)
Haber, E., Modersitzki, J.: Numerical methods for volume preserving image registration. Inverse Probl. 20(5), 1621 (2004)
Modersitzki, J.: FLIRT with rigidity—image registration with a local non-rigidity penalty. Int. J. Comput. Vis. 76(2), 153–163 (2008)
Staring, M., Klein, S., Pluim, J.P.: Nonrigid registration using a rigidity constraint. In: Medical Imaging, p. 614413. International Society for Optics and Photonics, March 2006
Nocedal, J., Wright, S.: Numerical Optimization. Springer Science and Business Media, New York (2006)
Chumchob, N., Chen, K.: A robust multigrid approach for variational image registration models. J. Comput. Appl. Math. 236(5), 653–674 (2011)
Bay, H., Tuytelaars, T., Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). doi:10.1007/11744023_32
Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Marciano, A., Cohen, L.D., Gadi, N. (2017). Vehicle X-ray Scans Registration: A One-Dimensional Optimization Problem. In: Lauze, F., Dong, Y., Dahl, A. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2017. Lecture Notes in Computer Science(), vol 10302. Springer, Cham. https://doi.org/10.1007/978-3-319-58771-4_46
Download citation
DOI: https://doi.org/10.1007/978-3-319-58771-4_46
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-58770-7
Online ISBN: 978-3-319-58771-4
eBook Packages: Computer ScienceComputer Science (R0)