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Proceedings Paper

A fast contour descriptor algorithm for supernova image classification
Author(s): Cecilia R. Aragon; David Bradburn Aragon
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Paper Abstract

We describe a fast contour descriptor algorithm and its application to a distributed supernova detection system (the Nearby Supernova Factory) that processes 600,000 candidate objects in 80 GB of image data per night. Our shapedetection algorithm reduced the number of false positives generated by the supernova search pipeline by 41% while producing no measurable impact on running time. Fourier descriptors are an established method of numerically describing the shapes of object contours, but transform-based techniques are ordinarily avoided in this type of application due to their computational cost. We devised a fast contour descriptor implementation for supernova candidates that meets the tight processing budget of the application. Using the lowest-order descriptors (F1 and F-1) and the total variance in the contour, we obtain one feature representing the eccentricity of the object and another denoting its irregularity. Because the number of Fourier terms to be calculated is fixed and small, the algorithm runs in linear time, rather than the O(n log n) time of an FFT. Constraints on object size allow further optimizations so that the total cost of producing the required contour descriptors is about 4n addition/subtraction operations, where n is the length of the contour.

Paper Details

Date Published: 26 February 2007
PDF: 12 pages
Proc. SPIE 6496, Real-Time Image Processing 2007, 649607 (26 February 2007); doi: 10.1117/12.703666
Show Author Affiliations
Cecilia R. Aragon, Lawrence Berkeley National Lab. (United States)
David Bradburn Aragon, DCA (United States)


Published in SPIE Proceedings Vol. 6496:
Real-Time Image Processing 2007
Nasser Kehtarnavaz; Matthias F. Carlsohn, Editor(s)

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