Navegar

Colección

Datos Estadísticas

Artículo

La versión final de este artículo es de uso interno de la institución.
Consulte el artículo en la página del editor
Consulte la política de Acceso Abierto del editor

Abstract:

Coronal mass ejection (CME) events refer to the appearance of a new, discrete, white-light feature (with outward velocity) in a coronagraph. The huge amount of data provided by the pertinent instruments onboard the Solar and Heliospheric Observatory (SOHO) and, most recently, the Solar Terrestrial Relations Observatory (STEREO) makes the human-based detection of such events excessively time consuming. Although several algorithms have been proposed to address this issue, there is still lack of universal consensus about their reliability. This work presents a novel method for the detection and tracking of CMEs as recorded by the LASCO instruments onboard SOHO. The algorithm we developed is based on level set and region competition methods, the CMEs texture being characterized by their co-occurrence matrix. The texture information is introduced in the region competition motion equations, and in order to evolve the curve, a fast level set implementation is used. © 2009 Elsevier B.V. All rights reserved.

Registro:

Documento: Artículo
Título:Detection and tracking of coronal mass ejections based on supervised segmentation and level set
Autor:Goussies, N.A.; Mejail, M.E.; Jacobo, J.; Stenborg, G.
Filiación:Departamento de Computación, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Argentina
Interferometrics Inc., Herndon, VA 20171, United States
Palabras clave:Coronal mass ejections; Level sets; Supervised segmentation; Texture; Co-occurrence-matrix; Coronal mass ejection; Detection and tracking; Level Set; Level set implementation; Motion equations; Novel methods; Region competition; Solar and heliospheric observatories; Solar-terrestrial relations; Supervised segmentation; Texture information; White light; Boundary layer flow; Buildings; Competition; Equations of motion; Observatories; Textures; Level measurement
Año:2010
Volumen:31
Número:6
Página de inicio:496
Página de fin:501
DOI: http://dx.doi.org/10.1016/j.patrec.2009.07.011
Título revista:Pattern Recognition Letters
Título revista abreviado:Pattern Recogn. Lett.
ISSN:01678655
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01678655_v31_n6_p496_Goussies

Referencias:

  • Berger, M., (1991) Les contours actifs: Modelisation, comportement et convergence, , Ph.D. Thesis, Institut National Polytechnique de Lorraine
  • Blake, A., Zisserman, A., (1987) Visual Reconstruction, , MIT Press
  • Brueckner, G.E., Howard, R.A., Koomen, M.J., Korendyke, C.M., Michels, D.J., Moses, J.D., Socker, D.G., Eyles, C.J., The large angle spectroscopic coronagraph (LASCO) (1995) Solar Phys, pp. 357-402
  • Chan, T.F., Vese, L.A., Active contours without edges (2001) IEEE Trans. Image Process., 10 (2), pp. 266-277
  • Cohen, L., On active contour models and balloons (1991) CVGIP: Image Understan., 53, pp. 211-218
  • Domingo, V., Fleck, B., Poland, A.I., SOHO: The solar and heliospheric observatory (1995) Space Sci. Rev., 72, pp. 81-84
  • Duda, R.O., Hart, P.E., Stork, D.G., (2000) Pattern Classification, , second ed. Wiley Interscience
  • Fua, P., Leclerc, Y., Model driven edge detection (1990) Mach. Vision Appl.
  • Goussies, N., Mejail, M., Jacobo, J.C., Stenborg, G., Detection and tracking of coronal mass ejections (2008) Lecture Notes in Computer Science, 5197, pp. 716-723. , CIARP. Ruiz-Shulcloper J., and Kropatsch W.G. (Eds), Springer
  • Goussies, N., Mejail, M., Jacobo, J.C., Stenborg, G., Detection of coronal mass ejections (2008) ICIP, pp. 1065-1068. , IEEE
  • Kass, M., Witkin, A., Terzopoulos, D., Snakes: Active contour models (1987) First International Conference on Computer Vision, pp. 259-268
  • Kim, J., Fisher, J.W., Yezzi, A., Etin, M., Willsky, A.S., A nonparametric statistical method for image segmentation using information theory and curve evolution (2005) IEEE Trans. Image Process., 14, pp. 1486-1502
  • Mumford, D., Shah, J., Optimal approximations by piecewise smooth functions and associated variational problem (1989) Commun. Pure Appl. Math., 42, pp. 577-684
  • Osher, S., Sethian, J.A., Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations (1988) J. Comput. Phys., 79, pp. 12-49
  • Paragios, N., Deriche, R., Geodesic active contours for supervised texture segmentation (1999) IEEE Conference on Computer Vision and Pattern Recognition, 1, pp. 1034-1040
  • Paragios, N., Deriche, R., Geodesic active regions and level set methods for supervised texture segmentation (2002) Internat. J. Comput. Vision, pp. 223-247
  • Press, W.H., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T., (1992) Numerical Recipes: The Art of Scientific Computing. second ed., , Cambridge University Press, Cambridge (UK) and New York
  • Robbrecht, E., Berghmans, D., Automated recognition of coronal mass ejections (CMEs) in near-real-time data (2004) A&A, 425, pp. 1097-1106
  • Robbrecht, E., Berghmans, D., Entering the era of automated CME recognition: A review of existing tools (2005) Solar Phys., 225, pp. 239-251
  • Rousson, M., (2004) Cue integration and front evolution in image segmentation, , Ph.D. Thesis, Universite de Nice, Sophia Antipolis
  • Rousson, M., Brox, T., Deriche, R., Active unsupervised texture segmentation on a diffusion based feature space (2003) IEEE Conference on Computer Vision and Pattern Recognition, 2, pp. 699-704
  • Samson, C., Blanc-F'eraud, L., Aubert, G., Zerubia, J., A level set model for image classification (2000) Internat. J. Comput. Vision, 40 (3), pp. 187-197
  • Sethian, J.A., (2007) Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science, , Cambridge University Press
  • Shi, Y., Karl, W.C., Real-time tracking using level sets (2005) CVPR, pp. 34-41. , IEEE Computer Society
  • Terzopoulos, D., Witkin, A.P., Kass, M., Constraints on deformable models: Recovering 3d shape and nonrigid motion (1988) Artif. Intell., 36 (1), pp. 91-123
  • Yezzi, J.A., Tsai, A., Willsky, A.S., A statistical approach to snakes for bimodal and trimodal imagery (1999) IEEE International Conference on Computer Vision, pp. 898-903
  • Zhu, S.C., Lee, T.S., Yuille, A.L., Region competition: Unifying snakes, region growing, energy/bayes/MDL for multi-band image segmentation (1995) ICCV, pp. 416-423

Citas:

---------- APA ----------
Goussies, N.A., Mejail, M.E., Jacobo, J. & Stenborg, G. (2010) . Detection and tracking of coronal mass ejections based on supervised segmentation and level set. Pattern Recognition Letters, 31(6), 496-501.
http://dx.doi.org/10.1016/j.patrec.2009.07.011
---------- CHICAGO ----------
Goussies, N.A., Mejail, M.E., Jacobo, J., Stenborg, G. "Detection and tracking of coronal mass ejections based on supervised segmentation and level set" . Pattern Recognition Letters 31, no. 6 (2010) : 496-501.
http://dx.doi.org/10.1016/j.patrec.2009.07.011
---------- MLA ----------
Goussies, N.A., Mejail, M.E., Jacobo, J., Stenborg, G. "Detection and tracking of coronal mass ejections based on supervised segmentation and level set" . Pattern Recognition Letters, vol. 31, no. 6, 2010, pp. 496-501.
http://dx.doi.org/10.1016/j.patrec.2009.07.011
---------- VANCOUVER ----------
Goussies, N.A., Mejail, M.E., Jacobo, J., Stenborg, G. Detection and tracking of coronal mass ejections based on supervised segmentation and level set. Pattern Recogn. Lett. 2010;31(6):496-501.
http://dx.doi.org/10.1016/j.patrec.2009.07.011