J. Space Weather Space Clim.
Volume 6, 2016
Statistical Challenges in Solar Information Processing
Article Number A16
Number of page(s) 12
Published online 08 March 2016
  • Agrawal, J., and S. Agrawal. Acceleration based Particle Swarm Optimization (APSO) for RNA secondary structure prediction, Progress in Systems Engineering, Volume 330 of the series Advances in Intelligent Systems and Computing, Springer International Publishing, Switzerland,, 741–746, 2015, DOI: 10.1007/978-3-319-08422-0_106. [CrossRef]
  • Amini, A., S. Tehrani, and T.E. Weymouth. Using dynamic programming for minimizing the energy of active contours in the presence of hard constraints, Second International Conference on Computer Vision, Tampa, FL, 95–99, 1988, DOI: 10.1109/CCV.1988.589976.
  • Asl, M.A., and S.A. Seyedin. Active Contour Optimization Using Particle Swarm Optimizer, 2nd International Conference on Information & Communication Technologies, Damascus, 1, 522–523, 2006, DOI: 10.1109/ICTTA.2006.1684608.
  • Ballerini, L. Genetic snakes for medical images segmentation. Evolutionary Image Analysis, Signal Processing and Telecommunications, 1596, 59–73, 1999, DOI: 10.1007/10704703_5. [CrossRef]
  • Ballerini, L., and L. Bocchi. Multiple genetic snakes for bone segmentation, Applications of Evolutionary Computing, Essex, UK, 346–356, 2003, DOI: 10.1007/3-540-36605-9_32.
  • Brajša, R., H. Wöhl, V. Ruždjak, F. Clette, and J.F. Hochedez. Solar differential rotation determined by tracing coronal bright points in SOHO-EIT images I. Interactive and automatic methods of data reduction. A&A, 374, 309–315, 2001, DOI: 10.1051/0004-6361:20010694. [NASA ADS] [CrossRef] [EDP Sciences]
  • Brajša, R., D. Sudar, I. Skokić, and S.H. Saar. Preliminary results on the solar rotation determined tracing SDO/AIA coronal bright points. Cent. Eur. Aphys. Bull., 38, 105–110, 2014.
  • Brajša, R., H. Wöhl, B. Vršnak, V. Ruždjak, F. Clette, J.F. Hochedez, and D. Roša. Height correction in the measurement of solar differential rotation determined by coronal bright points. A&A, 414, 707–715, 2004, DOI: 10.1051/0004-6361:20034082. [NASA ADS] [CrossRef] [EDP Sciences]
  • Bresson, X., S. Esedoḡlu, P. Vandergheynst, J.P. Thiran, and S. Osher. Fast global minimization of the active contour/snake model. J. Math. Imaging Vis., 28, 151–167, 2007, DOI: 10.1007/s10851-007-0002-0. [CrossRef]
  • Brown, D.S., C.E. Parnell, E.E. Deluca, L. Golub, and R.A. McMullen. The magnetic structure of a coronal X-ray bright point. Sol. Phys., 201, 305–321, 2001, DOI: 10.1023/A:1017907406350. [NASA ADS] [CrossRef]
  • Caselles, V., R. Kimmel, and G. Sapiro. Geodesic active contours. Int. J. Comput. Vision, 22, 61–79, 1997, DOI: 10.1023/A:1007979827043. [CrossRef]
  • Cohen, L.D., and I. Cohen. Finite-element methods for active contour models and balloons for 2-D and 3-D images. IEEE Trans. Pattern Anal. Mach. Intell., 15, 1131–1147, 1993, DOI: 10.1109/34.244675. [CrossRef]
  • Davatzikos, C., and J.L. Prince. Convexity analysis of active contour models, Proc. Conf. Info. Sci. Sys., Princeton, NJ, 581–587, 1994.
  • Eberhart, R.C., and Y. Shi. Tracking and optimizing dynamic systems with particle swarms, Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2001), Seoul, 94–97, 2001, DOI: 10.1109/CEC.2001.934376.
  • Eberhart, R.C., and X. Hu. Human tremor analysis using particle swarm optimization, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99, Washington, DC, 3, 1999, DOI: 10.1109/CEC.1999.785508.
  • Ethni, S.A., B. Zahawi, D. Giaouris, and P.P. Acarnley. Comparison of particle swarm and simulated annealing algorithms for induction motor fault identification, IEEE International Conference on Industrial Informatics (INDIN), Cardiff, Wales, 470–474, 2009, DOI: 10.1109/INDIN.2009.5195849.
  • Habbal, S.R., and G.L. Withbroe. Spatial and temporal variations of EUV coronal bright points. Sol. Phys., 69, 77–97, 1981, DOI: 10.1007/BF00151257. [NASA ADS] [CrossRef]
  • Habib, S.J., and B.S. Al-kazemi. Comparative study between the internal behavior of GA and PSO through problem-specific distance functions, IEEE Congress on Evolutionary Computation, Edinburgh, Scotland, UK, 2005, DOI: 10.1109/CEC.2005.1554966.
  • Hanasoge, S., M.S. Miesch, M. Roth, J. Schou, and M.J. Thompson. Solar dynamics, rotation, convection and overshoot. Space Sci. Rev., 196, 79–99, 2015, DOI: 10.1007/s11214-015-0144-0. [CrossRef]
  • Hara, H. Differential rotation rate of X-ray bright points and source region of their magnetic fields. Astrophys. J., 697, 980, 2009, DOI: 10.1088/0004-637X/697/2/980. [NASA ADS] [CrossRef]
  • Hassan, R., B.E. Cohanim, O.L. de Weck, and G. Venter. A comparison of particle swarm optimization and the genetic algorithm, Proceedings of the 1st AIAA Multidisciplinary Design Optimization Specialist Conference, Austin, Texas, American Institute of Aeronautics and Astronautics, 2005, DOI: 10.2514/6.2005-1897.
  • He, N., P. Zhang, and K. Lu. A geometric active contours model for multiple objects segmentation, Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues, Springer-Verlag, Berlin Heidelberg, 1141–1148, 2008, DOI: 10.1007/978-3-540-87442-3_141. [CrossRef]
  • Holden, N., and A.A. Freitas. Hybrid particle swarm/ant colony algorithm for the classification of hierarchical biological data, Proc. IEEE Int. Symp. Swarm Intelligence, Pasadena, CA, USA, 100–107, 2005, DOI: 10.1109/SIS.2005.1501608.
  • Hoos, H.H., and T. Stützle. Stochastic Local Search: Foundations & Applications, Elsevier, San Francisco, CA, 2004.
  • Horng, M.H., R.J. Liou, and J. Wu. Parametric active contour model by using the honey bee mating optimization. Expert Syst. Appl., 37, 7015–7025, 2010, DOI: 10.1016/j.eswa.2010.03.017. [CrossRef]
  • Howard, R. Solar rotation. Ann. Rev. Astron. Astrophys., 22, 131–155, 1984, DOI: 10.1146/annurev.aa.22.090184.001023. [NASA ADS] [CrossRef]
  • Howard, R., and J. Harvey. Spectroscopic determinations of solar rotation. Sol. Phys., 12, 23–51, 1970, DOI: 10.1007/BF02276562. [NASA ADS] [CrossRef]
  • Howard, R., and B.J. LaBonte. The Sun is observed to be a torsional oscillator with a period of 11 years. Astrophys. J., 239, L33–36, 1980. [NASA ADS] [CrossRef]
  • Hu, X., Y. Shi, and R. Eberhart. Recent advances in particle swarm, Proceedings of the 2004 Congress on Evolutionary Computation, Portland, OR, USA, 1, 2004, DOI: 10.1109/CEC.2004.1330842.
  • Karlsson, A., K. Stråhlén, and A. Heyden. A fast snake segmentation method applied to histopathological sections, Energy Minimization Methods in Computer Vision and Pattern Recognition, Lisbon, Portugal, 261–274, 2003, DOI: 10.1007/978-3-540-45063-4_17.
  • Kass, M., A. Witkin, and D. Terzopoulos. Snakes: active contour models. Int. J. Comput. Vision, 1, 321–331, 1988, DOI: 10.1007/BF00133570. [CrossRef]
  • Kennedy, J., and R. Eberhart. Particle swarm optimization, IEEE International Conference on Neural Networks, Perth, WA, 4, 1942–48, 1995, DOI: 10.1109/ICNN.1995.488968.
  • Lam, K.M., and H. Yan. Fast Greedy algorithm for active contours. Electron. Lett., 30, 21–23, 1994, DOI: 10.1049/el:19940040. [CrossRef]
  • Lemen, J.R., D.J. Akin, P.F. Boerner, C. Chou, J.F. Drake, et al. The Atmospheric Imaging Assembly (AIA) on the Solar Dynamics Observatory (SDO). Sol. Phys., 275, 17–40, 2012, DOI: 10.1007/s11207-011-9776-8. [NASA ADS] [CrossRef]
  • Leroy, B., I.L. Herlin, and L.D. Cohen. Multi-resolution algorithms for active contour models. ICAOS’96, 219, 58–65, 1996, DOI: 10.1007/3-540-76076-8_117.
  • Li, B., and S.T. Acton. Active contour external force using vector field convolution for image segmentation. IEEE Trans. Image Process., 16, 2096–2106, 2007, DOI: 10.1109/TIP.2007.899601. [CrossRef]
  • Li, R., Y. Guo, Y. Xing, and M. Li. A novel multi-swarm particle swarm optimization algorithm applied in active contour model, WRI Global Congress on Intelligent Systems, GCIS ‘09, 139–143, 2009, DOI: 10.1109/GCIS.2009.57.
  • Lorenc, M., M. Rybanský, and I. Dorotovič. On rotation of the solar corona. Sol. Phys., 281, 611–619, 2012, DOI: 10.1007/s11207-012-0105-7. [NASA ADS] [CrossRef]
  • Marinakis, Y., M. Marinaki, and G. Dounias. A hybrid particle swarm optimization algorithm for the vehicle routing problem. Eng. Appl. Artif. Intell., 23, 463–472, 2010, DOI: 10.1016/j.engappai.2010.02.002. [CrossRef]
  • McInerney, T., and D. Terzopoulos. Deformable models in medical image analysis: a survey. Med. Image Anal., 1, 91–108, 1996, DOI: 10.1016/S1361-8415(96)80007-7. [CrossRef]
  • McIntosh, S.W., and J.B. Gurman. Nine years of EUV bright points. Sol. Phys., 228, 285–299, 2005, DOI: 10.1007/s11207-005-4725-z. [NASA ADS] [CrossRef]
  • Mun, K.J., H.T. Kang, H.S. Lee, Y.S. Yoon, C.M. Lee, and J.H. Park. Active contour model based object contour detection using genetic algorithm with wavelet based image preprocessing. Int. J. Control Autom. Syst., 2, 100–106, 2004.
  • Nebti, S., and S. Meshoul. Predator prey optimization for snake-based contour detection. International Journal of Intelligent Computing and Cybernetics, 2, 228–242, 2009, DOI: 10.1108/17563780910959884. [CrossRef]
  • Niu, X. A geometric active contour model for highway extraction, Proceedings of ASPRS 2006 Annual Conference, Reno, Nevada, 2006.
  • Panda, S., and N.P. Padhy. Comparison of particle swarm optimization and genetic algorithm for FACTS-based controller design. Applied Soft Computing, 8, 1418–1427, 2008, DOI: 10.1016/j.asoc.2007.10.009. [CrossRef]
  • Paragios, N., and R. Deriche. Geodesic active contours and level sets for the detection and tracking of moving objects. IEEE Trans. Pattern Anal. Mach. Intell., 22, 266–280, 2000, DOI: 10.1109/34.841758. [CrossRef]
  • Park, H.W., T. Schoepflin, and Y. Kim. Active contour model with gradient directional information: directional snake. IEEE Trans. Circuits Syst. Video Technol., 11, 252–256, 2001, DOI: 10.1109/76.905991. [CrossRef]
  • Peli, E. Contrast in complex images. J. Opt. Soc. Am., A7, 2032–2040, 1990, DOI: 10.1364/JOSAA.7.002032. [CrossRef]
  • Pesnell, W.D., B.J. Thompson, and P.C. Chamberlin. The Solar Dynamics Observatory (SDO). Sol. Phys., 275, 3–15, 2012, DOI: 10.1007/s11207-011-9841-3. [NASA ADS] [CrossRef]
  • Poli, R. Analysis of the publications on the applications of particle swarm optimisation. JAEA, 2008, 1–10, 2008, DOI: 10.1155/2008/685175.
  • Prince, J.L. Gradient vector flow: a new external force for snakes. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Juan, 2, 66–71, 1997, DOI: 10.1109/CVPR.1997.609299.
  • Ram, G., D. Mandal, R. Kar, and S.P. Ghosal. Synthesis of time modulated linear antenna arrays using particle swarm optimization, IEEE Region 10 Conference, TENCON’14, Bangkok, 1–4, 2014, DOI: 10.1109/TENCON.2014.7022315.
  • Robinson, J., S. Sinton, and Y. Rahmat-Samii. Particle swarm, genetic algorithm, and their hybrids: optimization of a profiled corrugated horn antenna, IEEE Antennas and Propagation Society International Symposium, 314–317, 2002, DOI: 10.1109/APS.2002.1016311.
  • Scherrer, P.H., J. Schou, R.I. Bush, A.G. Kosovichev, R.S. Bogart, et al. The Helioseismic and Magnetic Imager (HMI) investigation for the Solar Dynamics Observatory (SDO), The Solar Dynamics Observatory, Springer, US, 207–227, 2011, DOI: 10.1007/978-1-4614-3673-7_10. [CrossRef]
  • Shahamatnia, E., I. Dorotovič, R.A. Ribeiro, and J.M. Fonseca. Towards an automatic sunspot tracking: swarm intelligence and snake model hybrid. Acta Futura, 5, 153–61, 2012, DOI: 10.2420/AF05.2012.153.
  • Shahamatnia, E., and M.M. Ebadzadeh. Application of particle swarm optimization and snake model hybrid on medical imaging, IEEE Third International Workshop on Computational Intelligence In Medical Imaging, Paris, France, 2011, DOI: 10.1109/CIMI.2011.5952043.
  • Sudar, D., I. Skokic, R. Brajša, and H. Saar. Steps toward a high precision solar rotation profile: results from SDO/AIA coronal bright point data. A&A, 575, A63, 2015, DOI: 10.1051/0004-6361/201424929. [NASA ADS] [CrossRef] [EDP Sciences]
  • Thompson, M.J., J. Toomre, E.R. Anderson, H.M. Antia, G. Berthomieu, et al. Differential rotation and dynamics of the solar interior. Science, 272, 1300–1305, 1996, DOI: 10.1126/science.272.5266.1300. [NASA ADS] [CrossRef] [PubMed]
  • Trelea, I.C. The particle swarm optimization algorithm: convergence analysis and parameter selection. Inform. Process. Lett., 85, 317–325, 2003, DOI: 10.1016/S0020-0190(02)00447-7. [CrossRef]
  • Tseng, C.C., J. Hsieh, and J. Jeng. Active contour model via multi-population particle swarm optimization. Expert Syst. Appl., 36, 5348–5352, 2009, DOI: 10.1016/j.eswa.2008.06.114. [CrossRef]
  • Van den Bergh, F. An analysis of particle swarm optimizers, Ph.D. dissertation, University of Pretoria, 2002.
  • Wildenauer, H., P. Blauensteiner, A. Hanbury, and M. Kampel. Motion detection using an improved colour model, Advances in Visual Computing, Springer, Berlin Heidelberg, 607–616, 2006, DOI: 10.1007/11919629_61.
  • Wöhl, H., R. Brajša, A. Hanslmeier, and S.F. Gissot. A precise measurement of the solar differential rotation by tracing small bright coronal structures in SOHO-EIT images. A&A, 520, A29, 2010, DOI: 10.1051/0004-6361/200913081. [NASA ADS] [CrossRef] [EDP Sciences]
  • Xu, C., and J.L. Prince. Snakes, shapes, and gradient vector flow. IEEE Trans. Image Process., Pacific Grove, CA, USA, 7, 359–369, 1998, DOI: 10.1109/83.661186.
  • Xu, C., A. Yezzi, and J.L. Prince. On the relationship between parametric and geometric active contours, Conference Record of the Thirty-Fourth Asilomar Conference on Signals, Systems and Computers, 1, 483–489, 2000, DOI: 10.1109/ACSSC.2000.911003.
  • Yang, X.S., Editor. Recent Advances in Swarm Intelligence and Evolutionary Computation, 1st ed., Vol. 585, Springer International Publishing, Switzerland, 2015, DOI: 10.1007/978-3-319-13826-8.
  • Yang, F., C. Zhang, and T. Sun. Comparison of particle swarm optimization and genetic algorithm for HMM training, 19th International Conference on Pattern Recognition, Tampa, FL, 2008, DOI: 10.1109/ICPR.2008.4761282.
  • Zeng, D., and Z. Zhou. Invariant topology snakes driven by particle swarm optimizer, 3rd International Conference on Innovative Computing Information and Control, Dalian, Liaoning, 38–38, 2008, DOI: 10.1109/ICICIC.2008.332.

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.