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PRODID:IEEE vTools.Events//EN
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DTSTART:20210314T030000
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DTSTAMP:20210329T221307Z
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DTSTART;TZID=Canada/Pacific:20210329T120000
DTEND;TZID=Canada/Pacific:20210329T130000
DESCRIPTION:Graph signal processing (GSP) is the study of computational too
 ls for correlated data residing on irregular kernels described abstractly 
 by graphs. I overview recent progress in GSP theories and their applicatio
 ns to image and more general signal estimation tasks. I first overview app
 roaches in learning appropriate graph structures given limited observed da
 ta. I illustrate how good graphs can lead to competitive and robust graph-
 based classification results in a semi-supervised learning setting. I then
  discuss fast graph sampling schemes--generalizing Nyquist sampling in tra
 ditional signal processing to the graph domain--and their applications to 
 practical engineering scenarios\, such as matrix completion and 3D point c
 loud sub-sampling. Finally\, I discuss how analytical graph filters can be
  combined with deep learning tools in a hybrid framework for robust\, expl
 ainable and memory-efficient implementations of image denoising algorithms
 .\n\nSpeaker(s): Prof. Gene Cheung\, \n\nVirtual: https://events.vtools.ie
 ee.org/m/265293
LOCATION:Virtual: https://events.vtools.ieee.org/m/265293
ORGANIZER:ivan_bajic@ieee.org
SEQUENCE:2
SUMMARY:Graph learning\, sampling and filtering for image and signal estima
 tion
URL;VALUE=URI:https://events.vtools.ieee.org/m/265293
X-ALT-DESC:Description: <br /><p>Graph signal processing (GSP) is the study
  of computational tools for correlated data residing on irregular kernels 
 described abstractly by graphs. I overview recent progress in GSP theories
  and their applications to image and more general signal estimation tasks.
  I first overview approaches in learning appropriate graph structures give
 n limited observed data. I illustrate how good graphs can lead to competit
 ive and robust graph-based classification results in a semi-supervised lea
 rning setting. I then discuss fast graph sampling schemes--generalizing Ny
 quist sampling in traditional signal processing to the graph domain--and t
 heir applications to practical engineering scenarios\, such as matrix comp
 letion and 3D point cloud sub-sampling. Finally\, I discuss how analytical
  graph filters can be combined with deep&nbsp\;learning tools in a hybrid 
 framework for robust\, explainable and memory-efficient implementations of
  image denoising algorithms.</p>
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