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Journal of Electronic Imaging

Multimodal image registration technique based on improved local feature descriptors
Author(s): Shyh Wei Teng; Md. Tanvir Hossain; Guojun Lu
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Paper 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.

Paper Details

Date Published: 12 January 2015
PDF: 17 pages
J. Electron. Imag. 24(1) 013013 doi: 10.1117/1.JEI.24.1.013013
Published in: Journal of Electronic Imaging Volume 24, Issue 1
Show Author Affiliations
Shyh Wei Teng, Federation Univ. Australia (Australia)
Md. Tanvir Hossain, Monash Univ. (Australia)
Guojun Lu, Federation Univ. Australia (Australia)

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