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Galaxies, Human Eyes, and Artificial Neural Networks

Lahav, O. and Naim, A. and Buta, R. J. and Corwin, H. G. and de Vaucouleurs, G. and Dressler, A. and Huchra, J. P. and van den Bergh, S. and Raychaudhury, S. and Sodré, L., Jr. and Storrie-Lombardi, M. C. (1995) Galaxies, Human Eyes, and Artificial Neural Networks. Science, 267 (5199). pp. 859-862. ISSN 0036-8075. https://resolver.caltech.edu/CaltechAUTHORS:20141215-142153377

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Abstract

The quantitative morphological classification of galaxies is important for understanding the origin of type frequency and correlations with environment. However, galaxy morphological classification is still mainly done visually by dedicated individuals, in the spirit of Hubble's original scheme and its modifications. The rapid increase in data on galaxy images at low and high redshift calls for a re-examination of the classification schemes and for automatic methods. Here are shown results from a systematic comparison of the dispersion among human experts classifying a uniformly selected sample of more than 800 digitized galaxy images. These galaxy images were then classified by six of the authors independently. The human classifications are compared with each other and with an automatic classification by an artificial neural network, which replicates the classification by a human expert to the same degree of agreement as that between two human experts.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1126/science.267.5199.859 DOIArticle
http://www.sciencemag.org/content/267/5199/859PublisherArticle
http://www.jstor.org/stable/2886148JSTORArticle
Additional Information:© 1995 American Association for the Advancement of Science. Received 16 August 1994; accepted 1 December 1994. We thank the Schmidt Telescope unit of the Royal Observatory of Edinburgh for the plate material, the APM group at the Royal Greenwich Observatory, Cambridge, for scanning support, M. Irwin and D. Lynden-Bell for helpful discussions. We are also grateful to the anonymous referee for helpful comments.
Issue or Number:5199
Record Number:CaltechAUTHORS:20141215-142153377
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20141215-142153377
Official Citation:Galaxies, Human Eyes, and Artificial Neural Networks O. Lahav, A. Naim, R. J. Buta, H. G. Corwin, G. de Vaucouleurs, A. Dressler, J. P. Huchra, S. van den Bergh, S. Raychaudhury, L. Sodré Jr., and M. C. Storrie-Lombardi Science 10 February 1995: 267 (5199), 859-862. [DOI:10.1126/science.267.5199.859]
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:52824
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:15 Dec 2014 23:16
Last Modified:03 Oct 2019 07:44

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