Decentralized Multi-Agent Algorithm for Translational 2D Image Alignment
We present a novel multi-agent algorithm applied to the problem of image alignment. Our method operates with multiple concurrent solutions held by agents who each attempt to reach the lowest error function score by trying to place a segment from a translated image to an unsegmented fixed image. Agents borrow and return segments of the translated image from a shared repository and iteratively suggest and evaluate their particular solutions. Finally, the global solution is determined by clustering of agents’ individual results. Experiments show that our approach provides results of high reliability and performance compared with traditional intensity based registration methods that rely on global optimization of a single error function given by translation of whole image.
KeywordsParticle Swarm Optimization Image Registration Segment Size Image Alignment Video Stabilization
Unable to display preview. Download preview PDF.
- 1.Zittová, B., Flusser, J., Šroubek, F.: Image registration: A survey and recent advances (2005)Google Scholar
- 2.Szeliski, R.: Computer Vision: Algorithms and Applications. Texts in Computer Science. Springer (2010)Google Scholar
- 4.Holia, M., Thakar, V.K.: Image registration for recovering affine transformation using nelder mead simplex method for optimization. Computer Science Journals 2009 (2009)Google Scholar
- 5.Čapek, M.: Optimisation strategies applied to global similarity based image registration methods (1999)Google Scholar
- 7.Machálek, T.: Application of particle swarm optimization in 2d image alignment, Master’s thesis, University of Hradec Králové (in Czech) (2011)Google Scholar
- 9.Amine Jallouli, M., Zagrouba, E., et al.: Decomposition of an alignment problem of two 3d images by a multi-agent approach. Innovations in Information Technology, 680–684 (2007)Google Scholar