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Efficient Image Registration using Fast Principal Component Analysis

Reel, Parminder Singh; Dooley, Laurence S. and Wong, Patrick (2012). Efficient Image Registration using Fast Principal Component Analysis. In: IEEE International Conference on Image Processing (ICIP'12), 30 Sep to 3 Oct 2012, Lake Buena Vista, Orlando, Florida, USA.

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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.

Item Type: Conference or Workshop Item
Copyright Holders: 2012 IEEE
Extra Information: ISBN: 978-1-4673-2532-5, pp. 1661–1664
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
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Item ID: 33551
Depositing User: Laurence Dooley
Date Deposited: 08 May 2012 09:51
Last Modified: 07 Dec 2018 23:08
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