22,189 research outputs found
Automatic Detection and Verification of Solar Features
YesA fast hybrid system for the automated detection and verification of active regions (plages) and filaments in solar images is presented in this paper. The system combines automated image processing with machine learning. The imaging part consists of five major stages. The solar disk is detected in the first stage, using a morphological hit-miss transform, watershed transform and Filling algorithm. An image-enhancement technique is introduced to remove the limb-darkening effect and intensity filtering is implemented followed by a modified region-growing technique to detect the regions of interest (RoI). The algorithms are tested on H- and CA II K3-line solar images that are obtained from Meudon Observatory, covering the period from July 2, 2001 till August 4, 2001. The detection algorithm is fast and it achieves false acceptance rate (FAR) error rate of 67% and false rejection rate (FRR) error rate of 3% for active regions, and FAR error rate of 19% and FRR error rate of 14% for filaments, when compared with the manually detected filaments in the synoptic maps. The detection performance is enhanced further using a neural network (NN), which is trained on statistical features extracted from the RoI and non-RoI. With the use of this combination the FAR has dropped to 2% for active regions and 4% for filaments.© 2006 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 15, 199-210, 200
Automated Coronal Hole Identification via Multi-Thermal Intensity Segmentation
Coronal holes (CH) are regions of open magnetic fields that appear as dark
areas in the solar corona due to their low density and temperature compared to
the surrounding quiet corona. To date, accurate identification and segmentation
of CHs has been a difficult task due to their comparable intensity to local
quiet Sun regions. Current segmentation methods typically rely on the use of
single EUV passband and magnetogram images to extract CH information. Here, the
Coronal Hole Identification via Multi-thermal Emission Recognition Algorithm
(CHIMERA) is described, which analyses multi-thermal images from the
Atmospheric Image Assembly (AIA) onboard the Solar Dynamics Observatory (SDO)
to segment coronal hole boundaries by their intensity ratio across three
passbands (171 \AA, 193 \AA, and 211 \AA). The algorithm allows accurate
extraction of CH boundaries and many of their properties, such as area,
position, latitudinal and longitudinal width, and magnetic polarity of
segmented CHs. From these properties, a clear linear relationship was
identified between the duration of geomagnetic storms and coronal hole areas.
CHIMERA can therefore form the basis of more accurate forecasting of the start
and duration of geomagnetic storms
Developing Experimental Models for NASA Missions with ASSL
NASA's new age of space exploration augurs great promise for deep space
exploration missions whereby spacecraft should be independent, autonomous, and
smart. Nowadays NASA increasingly relies on the concepts of autonomic
computing, exploiting these to increase the survivability of remote missions,
particularly when human tending is not feasible. Autonomic computing has been
recognized as a promising approach to the development of self-managing
spacecraft systems that employ onboard intelligence and rely less on control
links. The Autonomic System Specification Language (ASSL) is a framework for
formally specifying and generating autonomic systems. As part of long-term
research targeted at the development of models for space exploration missions
that rely on principles of autonomic computing, we have employed ASSL to
develop formal models and generate functional prototypes for NASA missions.
This helps to validate features and perform experiments through simulation.
Here, we discuss our work on developing such missions with ASSL.Comment: 7 pages, 4 figures, Workshop on Formal Methods for Aerospace (FMA'09
Signature Verification Approach using Fusion of Hybrid Texture Features
In this paper, a writer-dependent signature verification method is proposed.
Two different types of texture features, namely Wavelet and Local Quantized
Patterns (LQP) features, are employed to extract two kinds of transform and
statistical based information from signature images. For each writer two
separate one-class support vector machines (SVMs) corresponding to each set of
LQP and Wavelet features are trained to obtain two different authenticity
scores for a given signature. Finally, a score level classifier fusion method
is used to integrate the scores obtained from the two one-class SVMs to achieve
the verification score. In the proposed method only genuine signatures are used
to train the one-class SVMs. The proposed signature verification method has
been tested using four different publicly available datasets and the results
demonstrate the generality of the proposed method. The proposed system
outperforms other existing systems in the literature.Comment: Neural Computing and Applicatio
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