Improved SIFT-based image registration using belief propagation

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

12 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationIEEE International Conference on Acoustics, Speech and Signal Processing, 2009
PublisherIEEE
Pages2909-2912
Number of pages3
ISBN (Print)978-1-4244-2353-8
DOIs
Publication statusPublished - 30 Apr 2009

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Keywords

  • belief networks
  • image matching
  • image registration
  • optimisation
  • transforms
  • belief propagation
  • descriptors
  • invariant image characteristics
  • scale invariant feature transform

Cite this

Cheng, S., Stankovic, V. M., & Stankovic, L. (2009). Improved SIFT-based image registration using belief propagation. In IEEE International Conference on Acoustics, Speech and Signal Processing, 2009 (pp. 2909-2912). IEEE. https://doi.org/10.1109/ICASSP.2009.4960232