Fractal-Based Fuzzy Technique For Detection Of Active Regions From Solar Images

Revathy, K.; Lekshmi, S.; Nayar, S. R. Prabhakaran

doi:10.1007/s11207-005-6880-7

Cited by 30 publications

(15 citation statements)

References 16 publications

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“…Coronal ARs are segmented using either local thresholding, region-growing methods (Benkhalil et al 2006), supervised techniques (Dudok de Wit 2006;Colak & Qahwaji 2013), or unsupervised techniques (Barra et al 2009). Revathy et al (2005) compares segmentation results of pixelwise fractal dimension of EIT images using thresholding, region-growing techniques, and supervised classification.…”

Section: Introductionmentioning

confidence: 99%

The SPoCA-suite: Software for extraction, characterization, and tracking of active regions and coronal holes on EUV images

Verbeeck

Delouille

Mampaey

et al. 2013

A&A

122

121

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Context. Precise localization and characterization of active regions (AR) and coronal holes (CH) as observed by extreme ultra violet (EUV) imagers are crucial for a wide range of solar and helio-physics studies. Aims. We introduce a set of segmentation procedures (known as the SPoCA-suite) that allows one to retrieve AR and CH properties on EUV images taken from SOHO-EIT, STEREO-EUVI, PROBA2-SWAP, and SDO-AIA. Methods. We build upon our previous work on the Spatial Possibilistic Clustering Algorithm (SPoCA), that we have improved substantially in several ways. Results. We apply our algorithm on the synoptic EIT archive from 1997 to 2011 and decompose this dataset into regions that can clearly be identified as AR, quiet Sun, and CH. An antiphase between AR and CH filling factor is observed, as expected. The SPoCAsuite is next applied to datasets from EUVI, SWAP, and AIA. The time series pertaining to ARs or CHs are presented. Conclusions. The SPoCA-suite enables the extraction of several long time series of AR and CH properties from the data files of EUV imagers and also allows tracking individual ARs or CHs over time. For AIA images, AR and CH catalogs are available in near-real time from the Heliophysics Events Knowledgebase. The full code, which allows processing any EUV images, is available upon request to the authors.

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Section: Introductionmentioning

confidence: 99%

The SPoCA-suite: Software for extraction, characterization, and tracking of active regions and coronal holes on EUV images

Verbeeck

Delouille

Mampaey

et al. 2013

A&A

122

121

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“…Fuzzy-based segmentation method shows better effect when processing solar images as the boundaries are not always well-defined and the images may be polluted with noise. K. Revathy et al [86] proposed a fractalbased fuzzy technique to get the active regions. Then V. Barra et al [87] succeeded in automatically segmenting EUV solar images into Coronal Holes, Quiet Sun and Active Regions via a multichannel spatially constrained fuzzy clustering algorithm.…”

Section: Solar Activitymentioning

confidence: 99%

Computational Intelligence in Astronomy: A Survey

Wang¹,

Guo²,

Yu³

et al. 2018

IJCIS

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With explosive growth of the astronomical data, astronomy has become a representative data-rich discipline so as to defy traditional research methodologies and paradigm to analyze data and discover new knowledge from the data. How to effectively process and analyze the astronomical data is a fundamental work while a key scientific requirement of modern astronomical surveys. This situation has motivated needs for fostering of a wide range of cooperation with the astronomers and computer scientists. Computational intelligence, an important research direction of artificial intelligence and information sciences, has been shown to be promising to solve complex problems in scientific research and engineering. This paper presents a review of the current state of the application of computational intelligence in astronomy. We believe that computational intelligence is expected to provide powerful tools for addressing challenges in astronomical data analysis.

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“…Region growing and thresholding are perhaps the most commonly employed and simplistic of feature detection methods and are well suited to features that appear as a reasonably coherent collection of lighter or darker pixels, such as sunspots (Chapman and Groisman, 1984;Steinegger et al, 1990;Chapman, Cookson, and Hoyt, 1994;Preminger, Walton, and Chapman, 2001;Curto, Blanca, and Martínez, 2008;Zharkov, Zharkova, and Ipson, 2005;Colak and Qahwaji, 2008) or solar filaments (Qu et al, 2005;Aboudarham et al, 2008;Scholl and Habbal, 2008;Shih and Kowalski, 2003;Gao, Wang, and Zhou, 2002). More advanced methods are needed where the features being analysed are not well defined or homogeneous; for example active regions (Shih and Kowalski, 2003;Revathy, Lekshmi, and Nayar, 2005;Delouille et al, 2005). Feature tracking and monitoring algorithms are logical extensions of the detection and classification tasks and also fall into this group.…”

Section: Image Processing In Solar Physicsmentioning

confidence: 99%

Using Active Contours for Semi-Automated Tracking of UV and EUV Solar Flare Ribbons

2010

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Solar-flare UV and EUV images show elongated bright "ribbons" that move over time. If these ribbons are assumed to locate the footpoints of magnetic-field lines reconnecting in the corona, then it is clear that studying their evolution can provide important insight into the reconnection process. An image-processing method based on active contours (commonly referred to as "snakes") is proposed as a method for tracking UV and EUV flare ribbons and is tested on images from the Transition Region and Coronal Explorer (TRACE). This paper introduces the basic concepts of such an approach with a brief overview of the history and theory behind active contours. It then details the specifics of the snake algorithm developed for this work and shows the results of running the algorithm on test images. The results from the application of the developed algorithm are reported for six different TRACE flares (five in UV and one in EUV). The discussion of these results uses the output from an expert tracking the same ribbons by eye as a benchmark, and against these the snake algorithm is shown to compare favourably in certain conditions, but less so in others. The applicability of the automated snake algorithm to the general problem of ribbon tracking is discussed and suggestions for ways to improve the snake algorithm are proposed.

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Fractal-Based Fuzzy Technique For Detection Of Active Regions From Solar Images

Cited by 30 publications

References 16 publications

The SPoCA-suite: Software for extraction, characterization, and tracking of active regions and coronal holes on EUV images

The SPoCA-suite: Software for extraction, characterization, and tracking of active regions and coronal holes on EUV images

Computational Intelligence in Astronomy: A Survey

Using Active Contours for Semi-Automated Tracking of UV and EUV Solar Flare Ribbons

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