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A fast contour descriptor algorithm for supernova image classification Cecilia R. Aragon*a
 

Summary: A fast contour descriptor algorithm for supernova image classification
Cecilia R. Aragon*a
, David Bradburn Aragonb
a
Lawrence Berkeley National Laboratory, One Cyclotron Rd., Berkeley, CA, USA 94720;
b
DCA, 1563 Solano Rd. #434, Berkeley, CA USA 94707
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 shape-
detection 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.

  

Source: Aragon, Cecilia R. - Computational Research Division, Lawrence Berkeley National Laboratory
Lawrence Berkeley National Laboratory, Computational Research Division, High Performance Computing Research Department, Visualization Group

 

Collections: Computer Technologies and Information Sciences; Multidisciplinary Databases and Resources