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SUMMARY:Joint Reconstruction-Segmentation with Graph PDEs - Jeremy Budd (D
 elft University of Technology)
DTSTART:20211103T160000Z
DTEND:20211103T173000Z
UID:TALK164686@talks.cam.ac.uk
DESCRIPTION:<p>In most practical image segmentation tasks\, the image to b
 e segmented will need to first be reconstructed from indirect\, damaged\, 
 and/or noisy observations. Traditionally\, this reconstruction-segmentatio
 n task would be done in sequence: first apply the reconstruction method\, 
 and then the segmentation method. Joint reconstruction-segmentation is a m
 ethod for using segmentation and reconstruction techniques simultaneously\
 , to use information from the segmentation to guide the reconstruction\, a
 nd vice versa. Past work on this has employed relatively simple segmentati
 on algorithms\, such as the Chan&ndash\;Vese algorithm. In this talk\, we 
 will demonstrate how joint reconstruction-segmentation can be done using t
 he graph-PDE-based segmentation techniques developed by Bertozzi & Flenner
  (2012) and Merkurjev\, Kostic\, & Bertozzi (2013)\, with ideas drawn from
  Budd & van Gennip (2020) and Budd\, van Gennip\, & Latz (2021).</p>\n<p>T
 his work is joint with Yves van Gennip\, Carola Schonlieb\, Simone Parisot
 to\, and Jonas Latz.&nbsp\;</p>
LOCATION:Seminar Room 2\, Newton Institute
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