Abstract
Image registration is definitely one of the most prominent techniques at the heart of computer vision research. Applications range from medical image analysis, remote sensing or robotics to security-related tasks such as surveillance or motion tracking. In our previous work, a solution was provided to address the registration problem involving top-view radiographic images of vehicles. A unidimensional minimization scheme was formulated along with a column-wise constancy constraint on the displacement field.
In this paper, we show that the proposed method is not sufficient in case of significant vertical shifts between the cars of both moving and static images. In fact, the radiated beam is triangular, thus any translated object is projected differently according to its distance to the x-ray source. We therefore add a 1D unconstrained optimization to the previous scheme for a y-direction correction. We also demonstrate that applying the vertical correction following our 1D optimization in the x-axis yields better results than performing a simultaneous minimization on both components.
Finally, the possible apparition of artefacts in the deformed image throughout the optimization process is analyzed. Diffusion and volume-preserving schemes are considered and compared in this regard.
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References
Modersitzki, J.: Numerical Methods for Image Registration. Oxford University Press on Demand, Oxford (2004)
Modersitzki, J.: FAIR: Flexible Algorithms for Image Registration, vol. 6. SIAM, Philadelphia (2009)
Marciano, A., Cohen, L.D., Gadi, N.: Vehicle X-ray scans registration: a one-dimensional optimization problem. In: Lauze, F., Dong, Y., Dahl, A.B. (eds.) SSVM 2017. LNCS, vol. 10302, pp. 578–589. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58771-4_46
Fischer, B., Modersitzki, J.: Fast image registration: a variational approach. In: Psihoyios, G. (ed.) Proceedings of the International Conference on Numerical Analysis & Computational Mathematics, pp. 69–74. Wiley (2003)
Haber, E., Modersitzki, J.: A multilevel method for image registration. SIAM J. Sci. Comput. 27(5), 1594–1607 (2006)
Sotiras, A., Davatzikos, C., Paragios, N.: Deformable medical image registration: a survey. IEEE Trans. Med. Imaging 32(7), 1153–1190 (2013)
Haber, E., Modersitzki, J.: Numerical methods for volume preserving image registration. Inverse Probl. 20(5), 1621 (2004)
Haber, E., Modersitzki, J.: Image registration with guaranteed displacement regularity. Int. J. Comput. Vis. 71(3), 361–372 (2007)
Thirion, J.P.: Image matching as a diffusion process: an analogy with Maxwell’s demons. Med. Image Anal. 2(3), 243–260 (1998)
Rohlfing, T., Maurer, C.R., Bluemke, D.A., Jacobs, M.A.: Volume-preserving nonrigid registration of MR breast images using free-form deformation with an incompressibility constraint. IEEE Trans. Med. Imaging 22(6), 730–741 (2003)
Christensen, G.E., Johnson, H.J.: Consistent image registration. IEEE Trans. Med. Imaging 20(7), 568–582 (2001)
Rueckert, D., Aljabar, P.: Non-rigid registration using free-form deformations. In: Paragios, N., Duncan, J., Ayache, N. (eds.) Handbook of Biomedical Imaging, pp. 277–294. Springer, Boston, MA (2015). https://doi.org/10.1007/978-0-387-09749-7_15
Younes, L.: Invariance, déformations et reconnaissance de formes, vol. 44. Springer Science & Business Media, Heidelberg (2003)
Beg, M.F., Miller, M.I., Trouvé, A., Younes, L.: Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int. J. Comput. Vis. 61(2), 139–157 (2005)
Christensen, G.E., Rabbitt, R.D., Miller, M.I.: Deformable templates using large deformation kinematics. IEEE Trans. Image Process. 5(10), 1435–1447 (1996)
Mang, A., Biros, G.: An inexact Newton-Krylov algorithm for constrained diffeomorphic image registration. SIAM J. Imaging Sci. 8(2), 1030–1069 (2015)
Mang, A., Biros, G.: Constrained H1-regularization schemes for diffeomorphic image registration. SIAM J. Imaging Sci. 9(3), 1154–1194 (2016)
Hameeteman, R., Veenland, J.F., Niessen, W.J.: Volume preserving image registration via a post-processing stage. In: Proceedings of SPIE, vol. 6288 (2006)
Fischer, B., Modersitzki, J.: Ill-posed medicine—an introduction to image registration. Inverse Probl. 24(3), 034008 (2008)
Rühaak, J., König, L., Tramnitzke, F., Köstler, H., Modersitzki, J.: A matrix-free approach to efficient affine-linear image registration on CPU and GPU. J. Real-Time Image Process. 13(1), 205–225 (2017)
Albrecht, T., Dedner, A., Lüthi, M., Vetter, T.: Finite element surface registration incorporating curvature, volume preservation, and statistical model information. Computational and Mathematical Methods in Medicine 2013, 14 p. (2013). http://doi.org/10.1155/2013/674273. Article no. 674273
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Marciano, A., Cohen, L.D., Gadi, N. (2018). Vehicle X-Ray Images Registration. In: Pelillo, M., Hancock, E. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2017. Lecture Notes in Computer Science(), vol 10746. Springer, Cham. https://doi.org/10.1007/978-3-319-78199-0_19
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