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SUMMARY:Wavelets on Graphs: Theory and Applications
DTSTART:20110621T110000
DTSTAMP:20260407T020720Z
UID:ff2a5797b2d38b4ae98e9103b63ececf53037af8c3487ad9827f9f97
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Antonio Ortega\, Uni. of Southern California \nWavelet t
 ransforms have become popular tools for numerous signal processing tasks\,
  from compression to analysis or denoising. These transforms provide a cla
 ss of signal representations with flexible time (or space) and frequency l
 ocalization. Recent extensions of these transforms have been targeted to i
 ncorporate arbitrary directionality in the transform (e.g.\, Bandelets\, C
 ontourlets). In this presentation we focus on wavelet-like\, multiresoluti
 on transforms for datasets that are defined on arbitrary graphs. This is a
 n area that has started to attract some interest only very recently and ye
 t has the potential to have significant impact in a number of applications
 . Examples of datasets that could be seen as graphs include data distribut
 ed in a sensor network\, image data traversed in arbitrary fashion\, or da
 ta available in online social networks. We first provide an overview of ou
 r recent work in the development of wavelets for graphs data. In particula
 r we show constructions based on lifting as well as an example design base
 d simple graph filters. These are among the first critically sampled wavel
 et representations that have been proposed for arbitrary graph data. We th
 en provide an overview of two potential applications of these transforms i
 n i) distributed data gathering in a sensor network and ii) image compress
 ion.
LOCATION:SG 0211 https://plan.epfl.ch/?room==SG%0200211
STATUS:CONFIRMED
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