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BEGIN:VEVENT
DTSTAMP:20210402T160558Z
LOCATION:Poster Module
DTSTART;TZID=America/New_York:20201118T083000
DTEND;TZID=America/New_York:20201118T170000
UID:submissions.supercomputing.org_SC20_sess340_spostu119@linklings.com
SUMMARY:Massively Parallel Exact Histogram Equalization
DESCRIPTION:ACM Student Research Competition: Graduate Poster, ACM Student
  Research Competition: Undergraduate Poster, Posters\n\nMassively Parallel
  Exact Histogram Equalization\n\nKirkpatrick\n\nHistogram specification is
  performed by transforming an image so that the image's histogram matches 
 a target histogram. Exact methods of histogram specification result in les
 s loss of information but are orders of magnitudes slower than the classic
 al methods. Applications such as real-time medical imaging that require hi
 stogram equalization currently must use the classical method. This project
  adapts exact histogram equalization methods to run on GPUs to greatly inc
 rease their speed and make the use of exact histogram equalization methods
  viable for real-time imaging applications along with increasing the speed
  of large-scale machine learning applications. Some of the techniques are 
 applicable to any exact histogram specification method, but two of the mos
 t popular methods (local means and varational) are particularly optimized.
  The optimized algorithms running on a GPU provide super-real-time speeds 
 on very large images while maintaining the quality of the results. Compari
 sons of speedups will be shown.\n\nTag: Student Program\n\nRegistration Ca
 tegory: Tech Program Reg Pass, Exhibits Reg Pass
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