Call for papers

We invite you to participate in the Eighth International Conference on Scale Space and Variational Methods in Computer Vision (SSVM). The conference will be held in Cabourg, France, from Sunday May 16 evening to Thursday May 20 2021, at the Sweet-Home Hotel

 

Important dates

  • Paper submission: Friday, January 15, 2021, 23:59 CET
  • Notification of acceptance: Sunday, February 28, 2021

  • Conference: May 16 - May 20, 2021

 

Plenary speakers

Daniel Cremers (Professor, Technische Universität München)

Julie Delon (Professor at MAP5, Université Paris Descartes)

‪Carola-Bibiane Schönlieb (Professor and head of the Cambridge Image Analysis, University of Cambridge)

Jean-Luc Starck (Director of CosmoStat, CEA) 

 

Organizing Committee

Abderrahim Elmoataz, Univ. of Caen Normandy (GREYC)

Jalal FadiliUniv. of Caen Normandy (GREYC)

Yvain Quéau, CNRS (GREYC)

Julien RabinUniv. of Caen Normandy (GREYC)

Loïc Simon, Univ. of Caen Normandy (GREYC)

 

Submission

CMT submission webpage : http://cmt3.research.microsoft.com/SSVM2021/

Contributions are in the form of full papers, 12 pages in Springer LNCS format including bibliography.

Papers accepted for the conference will appear in the conference proceedings that will be published in Springer's Lecture Notes in Computer Science series. The proceedings will be available at the conference. Prospective authors are invited to submit a full-length twelve-page paper electronically via the SSVM'21 Paper Submission Web Page. All papers will undergo a double-blind peer-review procedure. At the conference the papers will be presented as posters or talks.

The conference will award a best student paper prize.

 

 

 

Conference topics

SSVM is a biannual meeting within the area of Computer Vision and Image Analysis. SSVM focuses especially on multiscale analysis of image content, partial differential equations, geometric and level-set methods, variational methods, and optimization.

Conference topics include the following areas:

  • 3D vision 
  • Approximation methods in imaging
  • Compressed sensing
  • Convex and non-convex variational models
  • Cross-scale structure 
  • Differential geometry and invariants 
  • Image- and feature analysis 
  • Imaging modalities 
  • Implicit surfaces 
  • Inverse problems in imaging 
  • Machine learning in imaging
  • Manifold data processing 
  • Mathematics of novel imaging methods 
  • Medical imaging and other applications 
  • Motion estimation and tracking 
  • Multi-orientation and multi-scale modelling and analysis 
  • Optical flow 
  • Optimization methods in imaging 
  • PDEs in data processing 
  • Perceptual grouping 
  • Registration 
  • Restoration and reconstruction 
  • Scale-space methods 
  • Segmentation 
  • Selection of salient scales 
  • Shape from X 
  • Stereo and multi-view reconstruction 
  • (Sub-)Riemannian geometry in imaging
  • Surface and shape modelling and analysis
  • Variational methods 

Affiliated Organisations and Sponsors

CNRSNormandie University & ENSICAENGREYCGdR MIAFédération Normastic

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