Hostname: page-component-cd9895bd7-dzt6s Total loading time: 0 Render date: 2024-12-25T18:33:09.216Z Has data issue: false hasContentIssue false
  • English
  • Français

Feature Detection in Radio Astronomy using the Circle Hough Transform

Published online by Cambridge University Press:  02 January 2013

C. Hollitt  and
M. Johnston-Hollitt
C. Hollitt*
Affiliation:
School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington 6140, New Zealand
M. Johnston-Hollitt
Affiliation:
School of Chemical and Physical Sciences, Victoria University of Wellington, PO Box 600, Wellington 6140, New Zealand
*
CCorresponding author. Email: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

While automatic detection of point sources in astronomical images has experienced a great degree of success, less effort has been directed towards the detection of extended and low-surface-brightness features. At present, existing telescopes still rely on human expertise to reduce the raw data to usable images and then to analyse the images for non-pointlike objects. However, the next generation of radio telescopes will generate unprecedented volumes of data making manual data reduction and object extraction infeasible. Without developing new methods of automatic detection for extended and diffuse objects such as supernova remnants, bent-tailed galaxies, radio relics and halos, a wealth of scientifically important results will not be uncovered. In this paper we explore the response of the Circle Hough Transform to a representative sample of different extended circular or arc-like astronomical objects. We also examine the response of the Circle Hough Transform to input images containing noise alone and inputs including point sources.

Keywords

radio continuum: generaltechniques: image processing
Type
Research Article
Information
Publications of the Astronomical Society of Australia , Volume 29 , Special Issue 3: Source Finding and Visualisation , 2012 , pp. 309 - 317
Copyright
Copyright © Astronomical Society of Australia 2012

References

Ballester, P., 1994, ASPC, 61, 319Google Scholar
Ballester, P., 1996, VA, 40, 479Google Scholar
Bonafede, A., Giovannini, G., Feretti, L., Govoni, F. & Murgia, M., 2009, A&A, 494, 429Google Scholar
Brogan, C. L., Gelfand, J. D., Gaensler, B. M., Kassim, N. E. & Lazio, T. J. W., 2006, ApJL, 639, 25CrossRefGoogle Scholar
Chiu, S. H., Lin, K. H. & Liaw, J. J., 2010, International Journal of Pattern Recognition and Artificial Intelligence, 24, 457CrossRefGoogle Scholar
Condon, J. J., Cotton, W. D., Greisen, E. W., Yin, Q. F., Perley, R. A., Taylor, G. B. & Broderick, J. J., 1998, AJ, 115, 1693CrossRefGoogle Scholar
Davies, E. R., 1988, PaRe, 7, 37Google Scholar
Dehghan, S., Johnston-Hollitt, M., Mao, M., Norris, R. P., Miller, N. A. & Huynh, M., 2011, JApA, 32, 491Google Scholar
Duda, R. O. & Hart, P. E., 1972, Commun. ACM, 15, 11CrossRefGoogle Scholar
Fanaroff, B. L. & Riley, J. M., 1974, MNRAS, 167, 31CrossRefGoogle Scholar
Feain, I., et al. , 2011, ApJ, 740, 17CrossRefGoogle Scholar
Filipović, M. D., Stupar, M., Jones, P. A. & White, G. L., 2002, ASPC, 271, 387Google Scholar
Freeland, E., 2010, ASPC, 423, 93Google Scholar
Freeland, E., Cardoso, R. F. & Wilcots, E., 2008, ApJ, 685, 858CrossRefGoogle Scholar
Gaensler, B. M. & Slane, P. O., 2006, ARA&A, 44, 17Google Scholar
Green, D. A., 2004, BASI, 32, 335Google Scholar
Green, A. J., Cram, L. E., Large, M. I. & Ye, T., 1999, ApJS, 122, 207CrossRefGoogle Scholar
Gunn, J. E. & Gott, J. R. III, 1972, ApJ, 176, 1CrossRefGoogle Scholar
Hollitt, C., 2009, in Proc. 24th Int. Conf. Image and Vision Comput. New Zealand (IVCNZ 09), ed. Bailey, D. (Wellington: IEEE), 373Google Scholar
Huynh, M., Hopkins, A., Norris, R., Hancock, P., Murphy, T., Jurek, R. & Whiting, M., 2012, PASA, Special Issue on Source Finding and VisualizationGoogle Scholar
Illingworth, J. & Kittler, J., 1988, CVGIP, 44, 87Google Scholar
Ioannou, D., Huda, W. & Laine, A. F., 1999, Image and Vision Comput., 17, 15CrossRefGoogle Scholar
Jahn, H., 1994, SPIE, 2357, 427Google Scholar
Johnston-Hollitt, M., 2003, PhD Thesis, Univ. AdelaideGoogle Scholar
Johnston-Hollitt, M., Fleenor, M., Rose, J., Christiansen, W. & Hunstead, R. W., 2004, IAUC, 195, 423Google Scholar
Kerbyson, D. J. & Atherton, T. J., 1995, in 5th Int. Conf. on Image Processing and its Applications (Edinburgh: IEEE), 370Google Scholar
Kimme, C., Ballard, D. & Sklansky, J., 1975, Commun. ACM, 18, 120CrossRefGoogle Scholar
Li, Z., Wheeler, J. C., Bash, F. N. & Jefferys, W. H., 1991, ApJ, 378, 93CrossRefGoogle Scholar
Mao, M. Y., Johnston-Hollitt, M., Stevens, J. B. & Wotherspoon, S. J., 2009, MNRAS, 392, 1070CrossRefGoogle Scholar
Mineshige, S. & Shibata, K., 1990, ApJL, 355, 47CrossRefGoogle Scholar
Murphy, T., Mauch, T., Green, A., Hunstead, R. W., Piestrzynska, B., Kels, A. P. & Sztajer, P., 2007, MNRAS, 382, 382CrossRefGoogle Scholar
Norris, R. P., et al. , 2011, PASA, 28, 215CrossRefGoogle Scholar
Orlando, S., Bocchino, F., Reale, F., Peres, G. & Petruk, O., 2007, A&A, 470, 927Google Scholar
Rad, A. A., Faez, K. & Qaragozlou, N., 2003, in VIIth Digital Image Computing: Techniques and Applications, ed. Sun, C., Talbot, H., Ourselin, S. & Adriaansen, T. (Melbourne: CSIRO), 879Google Scholar
Schilizzi, R. T., Dewdney, P. E. F. & Lazio, T. J. W., 2008, SPIE, 7012, 52Google Scholar
Storkey, A. J., Hambly, N. C., Williams, C. K. I. & Mann, R. G., 2004, MNRAS, 347, 36CrossRefGoogle Scholar
Tammann, G. A., Loeffler, W. & Schroeder, A., 1994, ApJS, 92, 487CrossRefGoogle Scholar
Whiteoak, J. B. Z. & Green, A. J., 1996, A&AS, 118, 329Google Scholar
Whiting, M. T., 2012, MNRAS, in press (astroph/1201.2710)Google Scholar
Yuen, H. K., Princen, J., Illingworth, J. & Kittler, J., 1990, Image and Vision Comput., 8, 71CrossRefGoogle Scholar