Abstract
We describe a new neural-network technique developed for an automated recognition of solar filaments visible in the hydrogen H-alpha line full disk spectroheliograms. This technique deploys the artificial neural network (ANN) with one input and one output neurons and the two hidden neurons associated either with the filament or with background pixels in this fragment. The ANN learns to recognize the filament depicted on a local background from a single image fragment labelled manually. The trained neural network has properly recognized filaments in the testing image fragments depicted on backgrounds with various brightness caused by the atmospherics distortions. Using a parabolic activation function this technique was extended for recognition of multiple solar filaments occasionally appearing in selected fragments.
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References
Zharkova, V.V., Ipson, S.S., Zharkov, S.I., Benkhalil, A., Aboudarham, J., Bentley, R.D.: A full disk image standardisation of the synoptic solar observations at the Meudon Observatory. Solar Physics (2002) (accepted)
Bentley, R.D., et al.: The European grid of solar observations. In: Proceedings of the 2nd Solar Cycle and Space Weather Euro-Conference, Vico Equense, Italy, 603 (2001)
Qahwaji, R., Green, R.: Detection of closed regions in digital images. The International Journal of Computers and Their Applications 8(4), 202–207 (2001)
Bader, D.A., Jaja, J., Harwood, D., Davis, L.S.: Parallel algorithms for image enhancement and segmentation by region growing with experimental study. In: The IEEE Proceedings of IPPS 1996, p. 414 (1996)
Turmon, M., Pap, J., Mukhtar, S.: Automatically finding solar active regions using SOHO/MDI photograms and magnetograms (2001)
Turmon, M., Mukhtar, S., Pap, J.: Bayesian inference for identifying solar active regions (2001)
Gao, J., Zhou, M., Wang, H.: A threshold and region growing method for filament disappearance area detection in solar images. The Johns Hopkins University (2001)
Bishop, C.M.: Neural networks for pattern recognition. Oxford University Press, Oxford (1995)
Nabney, I.T.: NETLAB: Algorithms for pattern recognition. Springer, Heidelberg (1995)
Schetinin, V.: A Learning Algorithm for Evolving Cascade Neural Networks, vol. 1. Kluwer Academic Publishers, Dordrecht (2003)
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Zharkova, V.V., Schetinin, V. (2003). A Neural-Network Technique for Recognition of Filaments in Solar Images. In: Palade, V., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science(), vol 2773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45224-9_22
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DOI: https://doi.org/10.1007/978-3-540-45224-9_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40803-1
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