A PSO/Snake Hybrid Algorithm for Determining Differential Rotation of Coronal Bright Points

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

Abstract

Particle swarm optimization (PSO) algorithm is a successful general problem solver, thanks to its computationally inexpensive mechanisms. On the other hand, snake model is a specialized image processing algorithm widely used in applications such as boundary delineation, image segmentation, and object tracking. In this paper we discuss the suitability of a hybrid PSO/Snake algorithm for determining the differential rotation of the Sun’s coronal bright points. In the Snake/PSO hybrid algorithm each particle in the population represents only a portion of the solution and the whole population altogether will converge to the final complete solution. In this model a one-to-one relation between Snake model snaxels and PSO particles have been created and PSO’s evolution equations have been modified with snake model concepts. This hybrid model is tested for tracking the coronal bright points (CBPs) along time, on a set of full-disc solar images obtained with the Atmospheric Imaging Assembly (AIA) instrument, onboard the Solar Dynamics Observatory (SDO) satellite. The algorithm results are then used for determining the differential rotation of CBPs. These final results are compared with those already reported in the literature, to assess the versatility of the PSO/Snake hybrid approach.
Original languageUnknown
Title of host publicationProceedings of the 5th International Joint Conference on Computational Intelligence (IJCCI 2013)
Pages56-63
Publication statusPublished - 1 Jan 2013
EventIJCCI 2013: 5th International Joint Conference on Computational Intelligence -
Duration: 1 Jan 2013 → …

Conference

ConferenceIJCCI 2013: 5th International Joint Conference on Computational Intelligence
Period1/01/13 → …

Cite this

Fonseca, J. M. M. R. D., Ribeiro, M. R. S. D. A., & DEE Group Author (2013). A PSO/Snake Hybrid Algorithm for Determining Differential Rotation of Coronal Bright Points. In Proceedings of the 5th International Joint Conference on Computational Intelligence (IJCCI 2013) (pp. 56-63)