Efficient image registration using fast principal component analysis

Parminder Singh Reel, Laurence S. Dooley, Patrick Wong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

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 languageEnglish
Title of host publication19th IEEE International Conference on Image Processing
PublisherIEEE
Pages1661-1664
Number of pages4
ISBN (Electronic)9781467325332
ISBN (Print)9781467325349
DOIs
Publication statusPublished - 21 Feb 2013
Event19th IEEE International Conference on Image Processing (ICIP 2012) - Orlando, United States
Duration: 30 Sep 20123 Oct 2012

Conference

Conference19th IEEE International Conference on Image Processing (ICIP 2012)
CountryUnited States
CityOrlando
Period30/09/123/10/12

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