Solar image segmentation by use of mean field fast annealing

Bratsolis, E.; Sigelle, Marc

doi:10.1051/aas:1998274

Cited by 16 publications

(7 citation statements)

References 13 publications

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“…Research is currently ongoing into automating these classification schemes, with some success (Bratsolis and Sigelle, 1998;Turmon, Pap, and Mukhtar, 2002;Dudok de Wit, 2006). The notable feature about both the Mt.…”

Section: Introductionmentioning

confidence: 99%

Multiresolution Analysis of Active Region Magnetic Structure and its Correlation with the Mount Wilson Classification and Flaring Activity

et al. 2008

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Two different multiresolution analyses are used to decompose the structure of active region magnetic flux into concentrations of different size scales. Lines separating these opposite polarity regions of flux at each size scale are found. These lines are used as a mask on a map of the magnetic field gradient to sample the local gradient between opposite polarity regions of given scale sizes. It is shown that the maximum, average and standard deviation of the magnetic flux gradient for α, β, βγ and βγδ active regions increase in the order listed, and that the order is maintained over all length-scales. Since magnetic flux gradient is strongly linked to active region activity, such as flares, this study demonstrates that, on average, the Mt. Wilson classification encodes the notion of activity over all length-scales in the active region, and not just those length-scales at which the strongest flux gradients are found. Further, it is also shown that the average gradients in the field, and the average length-scale at which they occur, also increase in the same order. Finally, there are significant differences in the gradient distribution, between flaring and nonflaring active regions, which are maintained over all length-scale. It is also shown that the average gradient content of active regions that have large flares (GOES class 'M' and above) is larger than that for active regions containing flares of all flare sizes; this difference is also maintained at all length-scale. All the reported results are independent of the multiresolution transform used. The implications for the Mt. Wilson classification of active regions in relation to the multiresolution gradient content and flaring activity are discussed.

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Section: Introductionmentioning

confidence: 99%

Multiresolution Analysis of Active Region Magnetic Structure and its Correlation with the Mount Wilson Classification and Flaring Activity

et al. 2008

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“…Sunspots, filaments, active regions, flares, coronal mass ejections and magnetic neutral lines are the prominent features of the solar activity that can be analyzed (Zharkova et al, 2005a). Many proposals have appeared, from Bayesian image segmentation (Turmon, Pap, and Mukhtar, 2002) to mean-field fast annealing (Bratsolis and Sigelle, 1998) and even neural networks (Fernandez Borda et al, 2002).…”

Section: Features Of the Solar Imagesmentioning

confidence: 99%

Automatic Sunspots Detection on Full-Disk Solar Images using Mathematical Morphology

2008

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Sunspots are solar features located in active regions of the Sun, whose number is an indicator of the Sun's magnetic activity. Therefore accurate detection and classification of sunspots are fundamental for the elaboration of solar activity indices such as the Wolf number. However, irregularities in the shape of the sunspots and their variable intensity and contrast with the surroundings, make their automated detection from digital images difficult. Here, we present a morphological tool that has allowed us to construct a simple and automatic procedure to treat digital photographs obtained from a solar telescope, and to extract the main features of sunspots. Comparing the solar indices computed with our algorithm against those obtained with the previous method exhibit an obvious improvement. A favorable comparison of the Wolf sunspot number time series obtained with our methodology and from other reference observatories is also presented. Finally, we compare our sunspot and group detection to that of other observatories.

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“…An approach to segmenting sunspots in H α images from the Sacremento Peak Observatory based on these ideas was proposed by Bratsolis and Sigelle (1998). Segmentation is separated into two stages in the first of which the image is sharpened and requantised to a reduced number of q intensity labels.…”

Section: Simulated Annealingmentioning

confidence: 99%

Feature Recognition in Solar Images

et al. 2005

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Despite several decades of research and development in the field of pattern recognition, the general problem of recognizing complex patterns with arbitrary orientations, locations, and scales remains unsolved and normally is applied using iterative manual evaluation of the detection results. This problem is becoming increasingly important with the growing number of massive archives of solar images produced by instruments located at ground-based observatories and aboard current satellites such as YOHKOH, SOHO and TRACE, with future satellites such as SOLAR B, SDO and STEREO in prospect. The size of expected archives requires a new automated approach to digital image processing and data extraction with robust and efficient pattern recognition techniques to be developed and implemented. This review evaluates techniques for the standardisation in shape and intensity of solar images and summarises the existing manual and semi-automated feature recognition techniques applied to a representative range of solar features, including sunspots, filaments, active regions, flares, coronal mass ejections and magnetic neutral lines. The review also surveys the most recent fully-automated detection techniques developed for the creation of Solar Feature Catalogues of sunspots, active regions and filaments for the European Grid of Solar Observations. The survey is aimed to help researchers and students to learn about the recognition techniques applied to astrophysical images with different levels of noise and distortions and to work effectively with the Solar Feature Catalogue.

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Solar image segmentation by use of mean field fast annealing

Cited by 16 publications

References 13 publications

Multiresolution Analysis of Active Region Magnetic Structure and its Correlation with the Mount Wilson Classification and Flaring Activity

Multiresolution Analysis of Active Region Magnetic Structure and its Correlation with the Mount Wilson Classification and Flaring Activity

Automatic Sunspots Detection on Full-Disk Solar Images using Mathematical Morphology

Feature Recognition in Solar Images

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