Improved symmetric-SIFT for Multi-modal image registration

Title: Improved symmetric-SIFT for Multi-modal image registration
Creator: Hossain, Md. Tanvir; Lv, Guohua; Teng, Shyh; Lu, Guojun; Lackmann, Martin
Date: 2011
Type: Text; Conference paper
Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/160926
Identifier: vital:12355
Identifier: https://doi.org/10.1109/DICTA.2011.40
Abstract: Multi-modal image registration has received significant research attention over the past decade. SymmetricSIFT is a recently proposed local description technique that can be used for registering multi-modal images. It is based on a well-known general image registration technique named Scale Invariant Feature Transform (SIFT). Symmetric-SIFT, however, achieves this invariance to multi-modality at the cost of losing important information. In this paper, we show how this loss may adversely affect the accuracy of registration results. We then propose an improvement to Symmetric-SIFT to overcome the problem. Our experimental results show that the proposed technique can improve the number of true matches by up to 10 times and overall matching accuracy by up to 30%.
Publisher: IEEE
Relation: 2011 International Conference on Digital Image Computing: Techniques and Applications p. 197-202
Rights: This metadata is freely available under a CCO license
Subject: 0801 Artificial Intelligence and Image Processing
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