Improved sift-based image registration using belief propagation

Authors:
Samuel Cheng;Vladimir Stankovic;Lina Stankovic
Affiliations:
University of Oklahoma, Dept. Electrical and Computer Engineering, Tulsa, 74135-2512, USA;University of Strathclyde, Dept. Electronics and Electrical Engineering, Glasgow G1 1XW UK;University of Strathclyde, Dept. Electronics and Electrical Engineering, Glasgow G1 1XW UK
Venue:
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Year:
2009

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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.