Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30184
Hidden State Probabilistic Modeling for Complex Wavelet Based Image Registration

Authors: F. C. Calnegru

Abstract:

This article presents a computationally tractable probabilistic model for the relation between the complex wavelet coefficients of two images of the same scene. The two images are acquisitioned at distinct moments of times, or from distinct viewpoints, or by distinct sensors. By means of the introduced probabilistic model, we argue that the similarity between the two images is controlled not by the values of the wavelet coefficients, which can be altered by many factors, but by the nature of the wavelet coefficients, that we model with the help of hidden state variables. We integrate this probabilistic framework in the construction of a new image registration algorithm. This algorithm has sub-pixel accuracy and is robust to noise and to other variations like local illumination changes. We present the performance of our algorithm on various image types.

Keywords: Complex wavelet transform, image registration, modeling using hidden state variables, probabilistic similaritymeasure.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1082691

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1009

References:


[1] J. Modersitzki, Numerical Methods for Image Registration. New York, Oxford University Press, 2004, pp 1-74.
[2] B. Zitova, J. Flusser, Image registration methods: a survey. Image and Vision Computing, Vol. 21, No. 11, pp. 977-1000, 2003
[3] A. Goshtasby, 2-D and 3-D Image Registration for medical, remote sensing, and industrial applications. New Jersey, John Wiley & Sons, Inc., Hoboken, 2005.
[4] J. L. Moigne, W. J. Campbell., R. F. Cromp, "An automated parallel image registration technique based on the correlation of wavelet features," IEEE Trans. On Geoscience and Remote Sensing, 40(8), pp. 1849-1864, 2002.
[5] N. M. Alpert, J. F Bradshaw, D. Kennedy, and J. A Correia,. "The principal axes transformation - A method for image registration,". Journal of Nuclear Medicine 31(10), pp. 1717-1722, 1990.
[6] P. Viola, W. M Wells III, "Alignment by maximization of mutual information,". in International Conference on Computer Vision, pp. 16-23, 1995.
[7] N. Ritter, R. Owens., J. Cooper., R.. H. Eikelboom., P. van Saarloos, "Registration of Stereo and Temporal Images of the Retina," IEEE Trans. Medical Imaging, Vol.. 18, No. 5, 1999.
[8] O. Pauly., N. Padoy., H. Poppert., L. Esposito., N. Navab, "Wavelet energy map: A robust support for multi-modal registration of medical images," in: IEEE Conference on Computer Vision and Pattern Recognition, pp.2184-2191, 2009.
[9] S. Li., J. Peng., J. T Kwok., J. Zhang, "Multimodal registration using the discrete wavelet frame transform," Proc. of ICPR Conf., pp. 877-880, 2006.
[10] I. W. Selesnick, R. G. Barniuk., N. G Kingsbury, "The Dual-Tree Complex Wavelet Transform," IEEE Signal Processing Magazine, 2005.
[11] R. Gonzalez., R. Woods, Digital Image Processing. New Jersey, Prentice Hall Upper Saddle River, 2002, pp. 350-402.
[12] M. S. Crouse, R. D. Nowak, and R. G. Baraniuk, "Wavelet-based statistical signal processing using hidden Markov models," IEEE Trans. Signal Proc., vol. 46, pp. 886-902, Apr. 1998.
[13] F. C. Calnegru. "A probabilistic framework for complex wavelet based image registration," Lecture Notes in Computer Science, 2011, vol 6978, pp 9-18, 2011
[14] Indian Institute of Information Technology, Allahabad, http://mtech.iiita.ac.in/A grade/Sukriti - Medical Image Registration using Next Generation Wavelets.pdf
[15] BrainWeb database http://www.bic.mni.mcgill.ca/brainweb/