Fast image registration with non-stationary Gauss-Markov random field templates

Authors:
Karthikeyan Natesan Ramamurthy;Jayaraman J. Thiagarajan;Andreas Spanias
Affiliations:
School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe;School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe;School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

Non-stationary Gauss-Markov random fields are required in modeling images with complex patterns. In this paper, we propose a framework for registering images to a nonstationary Gauss-Markov random field template in an M × M lattice, with a complexity of order M2 log M, considering only global translations. We simplify the likelihood computation by expressing it as a scalar product and we estimate the maximal likelihood translation using 2-D FFTs. We demonstrate the utility of this framework by applying it to image registration in a wavelet-domain template learning application. Results reveal that significant complexity reduction is achieved in image registration compared to straightforward registration in the wavelet domain.