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Efficient image registration using fast principal component analysis

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3 Author(s)
Parminder Singh Reel ; Department of Communication and Systems, The Open University, Milton Keynes, United Kingdom ; Laurence S Dooley ; Patrick Wong

Incorporating spatial features with mutual information (MI) has demonstrated superior image registration performance compared with traditional MI-based methods, particularly in the presence of noise and intensity non-uniformities (INU). This paper presents a new efficient MI-based similarity measure which applies Expectation Maximisation for Principal Component Analysis (EMPCA-MI), to afford significantly lower computational complexity, while providing analogous image registration performance with other feature-based MI solutions. Experimental analysis corroborates both the improved robustness and faster runtimes of EMPCA-MI, for different test datasets containing both INU and noise artefacts.

Published in:

2012 19th IEEE International Conference on Image Processing

Date of Conference:

Sept. 30 2012-Oct. 3 2012