Classification of optical galaxies using a PCNN

Soonil D. D. V. Rughooputh; Radhakhrishna Somanah; Harry Coomar Shumsher Rughooputh

doi:10.1117/12.382907

14 April 2000 Classification of optical galaxies using a PCNN

Soonil D. D. V. Rughooputh, Radhakhrishna Somanah, Harry Coomar Shumsher Rughooputh

Proceedings Volume 3962, Applications of Artificial Neural Networks in Image Processing V; (2000) https://doi.org/10.1117/12.382907
Event: Electronic Imaging, 2000, San Jose, CA, United States

Abstract

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.

Citation Download Citation

Soonil D. D. V. Rughooputh, Radhakhrishna Somanah, and Harry Coomar Shumsher Rughooputh "Classification of optical galaxies using a PCNN", Proc. SPIE 3962, Applications of Artificial Neural Networks in Image Processing V, (14 April 2000); https://doi.org/10.1117/12.382907

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available