Abstract
In this paper, we extend our previous work on presenting a registration model for images having arbitrarily-shaped locally variant illuminations from shadows to multiple shading levels. These variations tend to degrade the performance of geometric registration and impact subsequent processing. Often, traditional registration models use a least-squares estimator that is sensitive to outliers. Instead, we propose using a robust Huber M-estimator to increase the geometric registration accuracy (GRA). We demonstrate the proposed model and compare it to other models on simulated and real data. This modification shows clear improvements in terms of GRA and illumination correction.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Zitová, B., Flusser, J.: Image registration methods: A Survey. Image & Vis. Comp. 21, 977–1000 (2003)
Gevrekci, M., Gunturk, B.: Superresolution under photometric diversity of images. Advances in Signal Proc. (2007)
Szeliski, R.: Image alignment and stitching: A tutorial. Found. and Trends in Comp. Graphics and Vision 2 (2006)
Lou, L., Zhang, F., Xu, C., Li, F., Xue, M.: Automatic registration of aerial image series using geometric invariance. In: IEEE ICAL, pp. 1198–1203 (2005)
Aylward, S., Jomier, J., Weeks, S., Bullitt, E.: Registration of vascular images. Comp. Vis. 55, 123–138 (2003)
Xu, D., Kasparis, T.: Robust image registration under spatially non-uniform brightness changes. In: IEEE ICASSP, vol. 2, pp. 945–948 (2005)
Ke, Y., Sukthankar, R.: PCA-SIFT: a more distinctive representation for local image descriptors. Comp. Vis. and Patt. Recog. 2, 506–513 (2004)
Battiato, S., Gallo, G., Puglisi, G., Scellato, S.: SIFT features tracking for video stabilization. In: Proc. 14th ICIAP, pp. 825–830 (2007)
Fischler, M., Bolles, R.: Random Sample Consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Comm. Of the ACM 24, 381–395 (1981)
Hartley, R., Zisserman, A.: Multiple view geometry in computer vision, 2nd edn. Cambridge University Press, Cambridge (2003)
Periaswamy, S., Farid, H.: Elastic registration in the presence of intensity variations. IEEE Trans. Med. Imag. 22, 865–874 (2003)
Aguiar, P.: Unsupervised simultaneous registration and exposure correction. In: IEEE ICIP, pp. 361–364 (2006)
Altunbasak, Y., Mersereau, R., Patti, A.: A fast parametric motion estimation algorithm with illumination and lens distortion correction. IEEE Trans. Image Proc. 12, 395–408 (2003)
Bartoli, A.: Groupwise geometric and photometric direct image registration. IEEE Trans. Patt. Ana. & Mach. Intel. 30, 2098–2108 (2008)
Fouad, M., Dansereau, R., Whitehead, A.: Geometric registration of images with arbitrarily-shaped local intensity variations from shadows. In: IEEE ICIP, pp. 201–204 (2009)
Huber, P.: Robust Statistics, 1st edn. Wiley, New York (1981)
Kanungo, T., Mount, D., Netanyahu, N., Piatko, C., Silverman, R.: An efficient K-means clustering algorithm: Analysis and implementation. IEEE Trans. Patt. Ana. & Mach. Intel. 24, 881–892 (2002)
Mangasarian, O., Musicant, D.: Robust linear and support vector regression. IEEE Trans. Patt. Ana. & Mach. Intel. 22, 950–955 (2000)
Jiang, J., Zheng, S., Toga, A., Tu, Z.: Learning based coarse-to-fine image registration. In: IEEE ICCVPR, pp. 1–7 (2008)
Nocedal, J., Wright, S.: Numerical optimization. Springer, New York (1999)
http://vision.ece.ucsb.edu/registration/satellite/ (accessed January 2009)
http://users.isr.ist.utl.pt/~aguiar/mosaics/ (accessed January 2009)
Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Proc. 13, 600–612 (2004)
Barnea, D., Silverman, H.: A class of algorithms of fast digital image registration. IEEE Trans. Comp. 21, 179–186 (1972)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fouad, M.M., Dansereau, R.M., Whitehead, A.D. (2010). Geometric Image Registration under Locally Variant Illuminations Using Huber M-estimator. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D., Meunier, J. (eds) Image and Signal Processing. ICISP 2010. Lecture Notes in Computer Science, vol 6134. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13681-8_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-13681-8_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13680-1
Online ISBN: 978-3-642-13681-8
eBook Packages: Computer ScienceComputer Science (R0)