Fast Algorithms for Matching CCD Images to a Stellar Catalogue

Abstract

Two new algorithms are described for matching two dimensional coordinate lists of point sources that are significantly faster than previous methods. By matching rarely occurring triangles (or more complex shapes) in the two lists, and by ordering searches by decreasing probability of success, it is demonstrated that very few candidates need be considered to find a successful match. Moreover, by immediately testing the suitability of a potential match using an efficient mechanism, the need to process the entire candidate set is avoided, yielding considerable performance improvements. Triangles are described by a cosine metric that reduces the density of triangle space, permitting efficient searches. An alternative shape characterization method that reduces computational overhead in the construction phase is discussed. The algorithms are tested on a set of 10 063 wide-field survey images, with fields-of-view up to 4.8 • × 3.6 • , successfully matching 100% of the images in a mean elapsed time of 6 ms (2.4 GHz Athlon CPU). The elapsed time of the searching phase is shown to vary by less than 1 ms for list sizes between 10 and 200 points, demonstrating that fast, robust searches may be completed in nearly constant time, independent of list size.

VaST: A variability search toolkit

“…23 The correct transformation is found by identifying similar triangles constructed from the brightest stars detected on the current and reference images. When we created this algorithm, we were unaware that analogous star lists matching algorithms based on finding similar triangles were proposed by other authors (Groth 1986, Valdes et al 1995, Pál and Bakos 2006, Tabur 2007 24 . A similar algorithm has also been independently proposed by P. B. Stetson 25 .…”

“…Before the release of the Astrometry.net software (Lang et al 2010, Hogg et al 2008 there was no easy way to assign WCS to "random" images having no a priori information in FITS header about the image center, scale and orientation (which was often the case for images obtained with non-robotic telescopes). 24 A C implementation of Tabur (2007) algorithm may be found at http://spiff.rit.edu/match/ 25 https://ned.ipac.caltech.edu/level5/Stetson/Stetson5_2. html the algorithm differ in how the lists of triangles are constructed from the input star lists, how the triangles are matched to find the initial guess of the coordinate frame transformation and how this initial transformation is further refined.…”

CONSTRAINING THE AGE AND DISTANCE OF THE GALACTIC SUPERNOVA REMNANT G156.2+5.7 BY Hα EXPANSION MEASUREMENTS

“…Overscan subtraction, bias pattern subtraction, and flat fielding with domeflat were performed in a standard manner. Then, an astrometric solution was obtained with the USNO-B1.0 catalogue using the Optimistic Pattern Matching algorithm (Tabur 2007), implemented by Dr. N. Matsunaga for the data reduction for KWFC data. For each CCD chip, we picked up bad frames suffering from bad seeing, moon light, and cloud occultation.…”

Image resolution enhancement techniques for galaxy images

“…Tabur [15] proposed a triangle-based fast star matching algorithm to match observed CCD images to a stellar catalogue. This method constructs index triangles by centered stars in images and stars in stellar catalogue respectively, and then matches stars in images to a stellar catalogue by sorting and searching these triangles.…”