A fast contour descriptor algorithm for supernova image classification

Cecilia R. Aragon; David Bradburn Aragon

doi:10.1117/12.703666

26 February 2007 A fast contour descriptor algorithm for supernova image classification

Cecilia R. Aragon, David Bradburn Aragon

Proceedings Volume 6496, Real-Time Image Processing 2007; 649607 (2007) https://doi.org/10.1117/12.703666
Event: Electronic Imaging 2007, 2007, San Jose, CA, United States

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.

Citation Download Citation

Cecilia R. Aragon and David Bradburn Aragon "A fast contour descriptor algorithm for supernova image classification", Proc. SPIE 6496, Real-Time Image Processing 2007, 649607 (26 February 2007); https://doi.org/10.1117/12.703666

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available