Improved SIFT-based image registration using belief propagation

Cheng, S. and Stankovic, Vladimir M. and Stankovic, L.; (2009) Improved SIFT-based image registration using belief propagation. In: IEEE International Conference on Acoustics, Speech and Signal Processing, 2009. IEEE, pp. 2909-2912. ISBN 978-1-4244-2353-8 (http://dx.doi.org/10.1109/ICASSP.2009.4960232)

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Abstract

Scale Invariant Feature Transform (SIFT) is a very powerful technique for image registration. While SIFT descriptors accurately extract invariant image characteristics around keypoints, the commonly used matching approach for registration is overly simplified, because it completely ignores the geometric information among descriptors. In this paper, we formulate keypoint matching as a global optimization problem and provide a suboptimum solution using belief propagation. Experimental results show significant improvement over previous approaches.

ORCID iDs

Cheng, S., Stankovic, Vladimir M. ORCID logoORCID: https://orcid.org/0000-0002-1075-2420 and Stankovic, L. ORCID logoORCID: https://orcid.org/0000-0002-8112-1976;
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