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Feature Recognition in Solar Images

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Abstract

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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  1. School of Informatics, Department of Cybernetics, Internet and Virtual Systems, University of Bradford, Richmond Road, Bradford, BD7 1DP, UK

    VALENTINA ZHARKOVA, STANLEY IPSON, ALI BENKHALIL & SERGEI ZHARKOV

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  1. VALENTINA ZHARKOVA
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  2. STANLEY IPSON
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  3. ALI BENKHALIL
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  4. SERGEI ZHARKOV
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Correspondence to VALENTINA ZHARKOVA.

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ZHARKOVA, V., IPSON, S., BENKHALIL, A. et al. Feature Recognition in Solar Images. Artif Intell Rev 23, 209–266 (2005). https://doi.org/10.1007/s10462-004-4104-4

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