Hidden State Probabilistic Modeling for Complex Wavelet Based Image Registration
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.
[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/
[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/
@article{"International Journal of Information, Control and Computer Sciences:63285", author = "F. C. Calnegru", title = "Hidden State Probabilistic Modeling for Complex Wavelet Based Image Registration", 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.", volume = "5", number = "11", pages = "1447-7", }
