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Multimodal image registration technique based on improved local feature descriptors

[+] Author Affiliations
Shyh Wei Teng

Federation University Australia, Faculty of Science and Technology, Northways Road, Churchill, Victoria, 3842, Australia

Md. Tanvir Hossain

Monash University, Faculty of I.T., Northways Road, Churchill, Victoria, 3842, Australia

Guojun Lu

Federation University Australia, Faculty of Science and Technology, Northways Road, Churchill, Victoria, 3842, Australia

J. Electron. Imaging. 24(1), 013013 (Jan 12, 2015). doi:10.1117/1.JEI.24.1.013013
History: Received April 16, 2014; Accepted December 11, 2014
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Abstract.  Multimodal image registration has received significant research attention over the past decade, and the majority of the techniques are global in nature. Although local techniques are widely used for general image registration, there are only limited studies on them for multimodal image registration. Scale invariant feature transform (SIFT) is a well-known general image registration technique. However, SIFT descriptors are not invariant to multimodality. We propose a SIFT-based technique that is modality invariant and still retains the strengths of local techniques. Moreover, our proposed histogram weighting strategies also improve the accuracy of descriptor matching, which is an important image registration step. As a result, our proposed strategies can not only improve the multimodal registration accuracy but also have the potential to improve the performance of all SIFT-based applications, e.g., general image registration and object recognition.

© 2015 SPIE and IS&T

Citation

Shyh Wei Teng ; Md. Tanvir Hossain and Guojun Lu
"Multimodal image registration technique based on improved local feature descriptors", J. Electron. Imaging. 24(1), 013013 (Jan 12, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.1.013013


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