Stellar Image Interpretation System using Artificial Neural Networks: Unipolar Function Case

Authors

  • F. I. Younis
  • A. El-Bassuny Alawy
  • B. Šimák
  • M. S. Ella
  • M. A. Madkour

DOI:

https://doi.org/10.14311/284

Keywords:

neural networks, knowledge-based system, stellar images, image processing

Abstract

An artificial neural network based system for interpreting astronomical images has been developed. The system is based on feed-forward Artificial Neural Networks (ANNs) with error back-propagation learning. Knowledge about images of stars, cosmic ray events and noise found in images is used to prepare two sets of input patterns to train and test our approach. The system has been developed and implemented to scan astronomical digital images in order to segregate stellar images from other entities. It has been coded in C language for users of personal computers. An astronomical image of a star cluster from other objects is undertaken as a test case. The obtained results are found to be in very good agreement with those derived from the DAOPHOTII package, which is widely used in the astronomical community. It is proved that our system is simpler, much faster and more reliable. Moreover, no prior knowledge, or initial data from the frame to be analysed is required.

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Author Biographies

  • F. I. Younis
  • A. El-Bassuny Alawy
  • B. Šimák
  • M. S. Ella
  • M. A. Madkour

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Published

2001-01-06

Issue

Vol. 41 No. 6 (2001)

Section

Articles

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How to Cite

Younis, F. I., Alawy, A. E.-B., Šimák, B., Ella, M. S., & Madkour, M. A. (2001). Stellar Image Interpretation System using Artificial Neural Networks: Unipolar Function Case. Acta Polytechnica, 41(6). https://doi.org/10.14311/284