16,897 research outputs found
Coronal Mass Ejection Detection using Wavelets, Curvelets and Ridgelets: Applications for Space Weather Monitoring
Coronal mass ejections (CMEs) are large-scale eruptions of plasma and
magnetic feld that can produce adverse space weather at Earth and other
locations in the Heliosphere. Due to the intrinsic multiscale nature of
features in coronagraph images, wavelet and multiscale image processing
techniques are well suited to enhancing the visibility of CMEs and supressing
noise. However, wavelets are better suited to identifying point-like features,
such as noise or background stars, than to enhancing the visibility of the
curved form of a typical CME front. Higher order multiscale techniques, such as
ridgelets and curvelets, were therefore explored to characterise the morphology
(width, curvature) and kinematics (position, velocity, acceleration) of CMEs.
Curvelets in particular were found to be well suited to characterising CME
properties in a self-consistent manner. Curvelets are thus likely to be of
benefit to autonomous monitoring of CME properties for space weather
applications.Comment: Accepted for publication in Advances in Space Research (3 April 2010
Investigating the Kinematics of Coronal Mass Ejections with the Automated CORIMP Catalog
Studying coronal mass ejections (CMEs) in coronagraph data can be challenging
due to their diffuse structure and transient nature, compounded by the
variations in their dynamics, morphology, and frequency of occurrence. The
large amounts of data available from missions like the Solar and Heliospheric
Observatory (SOHO) make manual cataloging of CMEs tedious and prone to human
error, and so a robust method of detection and analysis is required and often
preferred. A new coronal image processing catalog called CORIMP has been
developed in an effort to achieve this, through the implementation of a dynamic
background separation technique and multiscale edge detection. These algorithms
together isolate and characterise CME structure in the field-of-view of the
Large Angle Spectrometric Coronagraph (LASCO) onboard SOHO. CORIMP also applies
a Savitzky-Golay filter, along with quadratic and linear fits, to the
height-time measurements for better revealing the true CME speed and
acceleration profiles across the plane-of-sky. Here we present a sample of new
results from the CORIMP CME catalog, and directly compare them with the other
automated catalogs of Computer Aided CME Tracking (CACTus) and Solar Eruptive
Events Detection System (SEEDS), as well as the manual CME catalog at the
Coordinated Data Analysis Workshop (CDAW) Data Center and a previously
published study of the sample events. We further investigate a form of
unsupervised machine learning by using a k-means clustering algorithm to
distinguish detections of multiple CMEs that occur close together in space and
time. While challenges still exist, this investigation and comparison of
results demonstrates the reliability and robustness of the CORIMP catalog,
proving its effectiveness at detecting and tracking CMEs throughout the LASCO
dataset.Comment: 23 pages, 11 figures, 1 tabl
The Multiscale Morphology Filter: Identifying and Extracting Spatial Patterns in the Galaxy Distribution
We present here a new method, MMF, for automatically segmenting cosmic
structure into its basic components: clusters, filaments, and walls.
Importantly, the segmentation is scale independent, so all structures are
identified without prejudice as to their size or shape. The method is ideally
suited for extracting catalogues of clusters, walls, and filaments from samples
of galaxies in redshift surveys or from particles in cosmological N-body
simulations: it makes no prior assumptions about the scale or shape of the
structures.}Comment: Replacement with higher resolution figures. 28 pages, 17 figures. For
Full Resolution Version see:
http://www.astro.rug.nl/~weygaert/tim1publication/miguelmmf.pd
Fast Detection of Curved Edges at Low SNR
Detecting edges is a fundamental problem in computer vision with many
applications, some involving very noisy images. While most edge detection
methods are fast, they perform well only on relatively clean images. Indeed,
edges in such images can be reliably detected using only local filters.
Detecting faint edges under high levels of noise cannot be done locally at the
individual pixel level, and requires more sophisticated global processing.
Unfortunately, existing methods that achieve this goal are quite slow. In this
paper we develop a novel multiscale method to detect curved edges in noisy
images. While our algorithm searches for edges over a huge set of candidate
curves, it does so in a practical runtime, nearly linear in the total number of
image pixels. As we demonstrate experimentally, our algorithm is orders of
magnitude faster than previous methods designed to deal with high noise levels.
Nevertheless, it obtains comparable, if not better, edge detection quality on a
variety of challenging noisy images.Comment: 9 pages, 11 figure
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