Abstract
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.
Original language | English |
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Title of host publication | 19th IEEE International Conference on Image Processing |
Publisher | IEEE |
Pages | 1661-1664 |
Number of pages | 4 |
ISBN (Electronic) | 9781467325332 |
ISBN (Print) | 9781467325349 |
DOIs | |
Publication status | Published - 21 Feb 2013 |
Event | 19th IEEE International Conference on Image Processing (ICIP 2012) - Orlando, United States Duration: 30 Sept 2012 → 3 Oct 2012 |
Conference
Conference | 19th IEEE International Conference on Image Processing (ICIP 2012) |
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Country/Territory | United States |
City | Orlando |
Period | 30/09/12 → 3/10/12 |