Feature Detection in Radio Astronomy using the Circle Hough Transform

Abstract

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.

A Large-Scale, Low-Frequency Murchison Widefield Array Survey of Galactic H ii Regions between 260 < l < 340

“…detection algorithms that are able to robustly characterise complex or occluded sources (see Hollitt & Johnston-Hollitt 2012 for a discussion). Regarding fellwalker, the gradientbased approach allows it to deal reasonably well with complex topologies and it allows easy manual manipulation of the identified regions if necessary.…”

The ASKAP/EMU Source Finding Data Challenge

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“…Real data comprised 25 randomly selected 500 × 500 pixel windows of two large astronomical images 1 . Each window was treated as a whole image for purposes of evaluation of DMR; windows had an average of 36 sources each.…”

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