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

E. Shahamatnia, I. Dorotovic, R. A. Ribeiro, J. M. Fonseca

2013

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.

References

  1. Amini, A. A., Tehrani, S. & Weymouth, T.E., 1988. Using dynamic programming for minimizing the energy of active contours in the presence of hard constraints. In Computer Vision., Second International Conference on. pp. 95-99.
  2. Asl, M. A., 2006. Active Contour Optimization Using Particle Swarm Optimizer. , pp.522-523.
  3. Ballerini, L., 1999. Genetic snakes for medical images segmentation. In Evolutionary Image Analysis, Signal Processing and Telecommunications. Springer, pp. 59-73.
  4. Ballerini, L. & Bocchi, L., 2003. Multiple genetic snakes for bone segmentation. In Applications of Evolutionary Computing. Springer, pp. 346-356.
  5. Van den Bergh, F., 2002. An analysis of particle swarm optimizers. University of Pretoria, South Africa,.
  6. Brajsa, R. et al., 2001. Solar differential rotation determined by tracing coronal bright points in SOHOEIT images I . Interactive and automatic methods of data reduction. Astronomy and astrophysics, 374, pp.309-315.
  7. Bresson, X. et al., 2007. Fast global minimization of the active contour/snake model. Journal of Mathematical Imaging and vision, 28(2), pp.151-167.
  8. Cohen, L. D. & Cohen, I., 1993. Finite-element methods for active contour models and balloons for 2-D and 3- D images. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 15(11), pp.1131-1147.
  9. Davatzikos, C. & Prince, J. L., 1994. Convexity analysis of active contour models. In Proc. Conf. on Info. Sci. and Sys. pp. 581-587.
  10. Horng, M.-H., Liou, R.-J. & Wu, J., 2010. Parametric active contour model by using the honey bee mating optimization. Expert Systems with Applications, 37(10), pp.7015-7025.
  11. Karlsson, A., Stråhlén, K. & Heyden, A., 2003. A fast snake segmentation method applied to histopathological sections. In Energy Minimization Methods in Computer Vision and Pattern Recognition. pp. 261-274.
  12. Kass, M., Witkin, A. & Terzopoulos, D., 1988. Snakes: Active contour models. International journal of computer vision, 1(4), pp.321-331.
  13. Kennedy, J. & Eberhart, R., 1995. Particle swarm optimization. In Neural Networks, 1995. Proceedings., IEEE International Conference on. pp. 1942-1948.
  14. Lam, K.-M. & Yan, H., 1994. Fast greedy algorithm for active contours. Electronics Letters, 30(1), pp.21-23.
  15. Leroy, B., Herlin, I. L. & Cohen, L. D., 1996. Multiresolution algorithms for active contour models. In ICAOS'96. Springer, pp. 58-65.
  16. Li, R. et al., 2009. A Novel Multi-Swarm Particle Swarm Optimization algorithm Applied in Active Contour Model. , (1).
  17. Lorenc, M., Rybanský, M. & Dorotovic, I., 2012. On Rotation of the Solar Corona. Solar Physics. Available at: http://www.springerlink.com/index/10.1007/ s11207-012-0105-7 (Accessed September 11, 2012).
  18. Mun, K.-J. et al., 2004. Active contour model based object contour detection using genetic algorithm with wavelet based image preprocessing. International Journal of Control Automation and Systens, 2, pp.100-106.
  19. Nebti, S., 2009. Predator prey optimization for snakebased contour detection.
  20. Niu, X., 2006. A Geometric Active Contour Model for Highway Extraction.
  21. Park, H., Schoepflin, T. & Kim, Y., 2001. Active contour model with gradient directional information: directional snake. Circuits and Systems for Video Technology, IEEE Transactions on, 11(2), pp.252- 256.
  22. Shahamatnia, E. et al., 2012. Towards an automatic sunspot tracking?: Swarm intelligence and snake model hybrid. Acta Futura, 5, pp.153-161.
  23. Shahamatnia, E. & Ebadzadeh, M.M., 2011. Application of particle swarm optimization and snake model hybrid on medical imaging. In 2011 IEEE Third International Workshop On Computational Intelligence In Medical Imaging. Paris, France: IEEE, pp. 1-8.
  24. Tsechpenakis, G. et al., 2004. A snake model for object tracking in natural sequences. , 19, pp.219-238.
  25. Tseng, C., Hsieh, J. & Jeng, J., 2009. Expert Systems with Applications Active contour model via multipopulation particle swarm optimization. Expert Systems With Applications, 36(3), pp.5348-5352. Available at: http://dx.doi.org/10.1016/j.eswa. 2008.06.114.
  26. Ulrich, R. K. & Boyden, J. E., 2005. The solar surface toroidal magnetic field. The Astrophysical Journal Letters, 620(2), p.L123.
  27. Wildenauer, H. et al., 2006. Motion detection using an improved colour model. In Advances in visual computing. Springer, pp. 607-616.
  28. Zeng, D. & Zhou, Z., 2008. Invariant Topology Snakes Driven by Particle Swarm Optimizer. In 2008 3rd International Conference on Innovative Computing Information and Control. IEEE, pp. 38-38.
Download


Paper Citation


in Harvard Style

Shahamatnia E., Dorotovic I., A. Ribeiro R. and M. Fonseca J. (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 - Volume 1: ECTA, (IJCCI 2013) ISBN 978-989-8565-77-8, pages 56-63. DOI: 10.5220/0004576100560063


in Bibtex Style

@conference{ecta13,
author={E. Shahamatnia and I. Dorotovic and R. A. Ribeiro and J. M. Fonseca},
title={A PSO/Snake Hybrid Algorithm for Determining Differential Rotation of Coronal Bright Points},
booktitle={Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2013)},
year={2013},
pages={56-63},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004576100560063},
isbn={978-989-8565-77-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2013)
TI - A PSO/Snake Hybrid Algorithm for Determining Differential Rotation of Coronal Bright Points
SN - 978-989-8565-77-8
AU - Shahamatnia E.
AU - Dorotovic I.
AU - A. Ribeiro R.
AU - M. Fonseca J.
PY - 2013
SP - 56
EP - 63
DO - 10.5220/0004576100560063