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DTSTART:19700308T020000
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DTSTAMP:20260522T150117Z
LOCATION:D168
DTSTART;TZID=America/Chicago:20181111T155800
DTEND;TZID=America/Chicago:20181111T160100
UID:submissions.supercomputing.org_SC18_sess165_ws_eduhpca105@linklings.co
 m
SUMMARY:Optimization of an Image Processing Algorithm: Histogram Equalizat
 ion
DESCRIPTION:Julian Gutierrez, David Kaeli, and Fritz Previlon (Northeaster
 n University)\n\nMany textbooks rely on classical linear algebra examples 
 to illustrate best practices in parallel programming (e.g., matrix multipl
 ication and vector add). Despite their common use in class, these examples
  lack sophistication of a complete application. We have found that student
 s seem to be more motivated to work with imaging processing algorithms, wh
 ere the student can view the before and after image, visually inspecting t
 he results of their processing.\n\nThis assignment focuses on improving th
 e performance of the histogram equalization algorithm applied to an image.
  Histogram equalization is a popular image processing algorithm used to in
 crease the contrast of an image to better highlight its features. It is a 
 common algorithm used in many scientific applications such as x-ray imagin
 g, thermal imaging and as a pre-processing task for multiple computer visi
 on/deep learning algorithms.\n\nTag: Diversity, Education\n\nRegistration 
 Category: Workshop Reg Pass\n\n
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