Standard Methods of Characterizing Performance of Fan FilterUnits, Version 3.0
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 (F{sub 1} and F{sub -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.
- Research Organization:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE. Assistant Secretary for Energy Efficiency andRenewable Energy. Office of Building Technology
- DOE Contract Number:
- DE-AC02-05CH11231
- OSTI ID:
- 925526
- Report Number(s):
- LBNL-62118; R&D Project: E12015; BnR: 600305000; TRN: US200807%%408
- Country of Publication:
- United States
- Language:
- English
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