2D Feature Recognition And 3d Reconstruction In Solar Euv Images |
| |
Authors: | Markus J Aschwanden |
| |
Institution: | (1) Department L9-41, Lockheed Martin Advanced Technology Center, Solar and Astrophysics Laboratory, Building 252, 3251 Hanover St., Palo Alto, CA, 94304, U.S.A. |
| |
Abstract: | EUV images show the solar corona in a typical temperature range of T >rsim 1 MK, which encompasses the most common coronal structures: loops, filaments, and other magnetic structures in active regions,
the quiet Sun, and coronal holes. Quantitative analysis increasingly demands automated 2D feature recognition and 3D reconstruction,
in order to localize, track, and monitor the evolution of such coronal structures. We discuss numerical tools that “fingerprint”
curvi-linear 1D features (e.g., loops and filaments). We discuss existing finger-printing algorithms, such as the brightness-gradient
method, the oriented-connectivity method, stereoscopic methods, time-differencing, and space–time feature recognition. We
discuss improved 2D feature recognition and 3D reconstruction techniques that make use of additional a priori constraints, using guidance from magnetic field extrapolations, curvature radii constraints, and acceleration and velocity
constraints in time-dependent image sequences. Applications of these algorithms aid the analysis of SOHO/EIT, TRACE, and STEREO/SECCHI
data, such as disentangling, 3D reconstruction, and hydrodynamic modeling of coronal loops, postflare loops, filaments, prominences,
and 3D reconstruction of the coronal magnetic field in general. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|