Classification of optical galaxies using a PCNN

Details Classification of optical galaxies using a PCNN Classification of optical galaxies using a PCNN

Apr 2000

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2000spie.3962..138r&link_type=abstract

Proc. SPIE Vol. 3962, p. 138-147, Applications of Artificial Neural Networks in Image Processing V, Nasser M. Nasrabadi; Aggelos

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Hubble's pioneering discovery of the distance to the Andromeda galaxy opened the frontiers of extragalactic research. According to estimates computed from the Hubble Deep Field, astronomers predict that the universe may potentially contain over 50 billion galaxies. Recognition/Classification of galaxies is an important issue in the large-scale study of the Universe and is not a simple task. Several techniques have been reported for the classification of galaxies. Artificial neural networks are being successfully used for classification in various applications. Recently, the Pulse-Coupled Neural Network (PCNN) has been shown to be useful for image pre-processing. When a digital image is applied to the input of a PCNN, the network groups image pixels based on spatial proximity and brightness similarity. A time signal can be obtained by computing the number of `on pixels' for each iteration; the time signal being an encoded 1D signature of a 2D image. There is a one-to-one correspondence between images and their time signatures. In the current study, we exploit this property to obtain time signatures of optical galaxies. Our results on spirals, ellipticals and irregulars are very promising and the work is being extended towards the development of an efficient and robust computer automated classifier.

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