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Scale Space and Variational Methods in Computer Vision

7th International Conference, SSVM 2019, Hofgeismar, Germany, June 30 – July 4, 2019, Proceedings

  • Jan Lellmann
  • Martin Burger
  • Jan Modersitzki
Conference proceedings SSVM 2019

Part of the Lecture Notes in Computer Science book series (LNCS, volume 11603)

Also part of the Image Processing, Computer Vision, Pattern Recognition, and Graphics book sub series (LNIP, volume 11603)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. 3D Vision and Feature Analysis

    1. Front Matter
      Pages 1-1
    2. Valentin De Bortoli, Agnès Desolneux, Bruno Galerne, Arthur Leclaire
      Pages 13-24
    3. Shay Deutsch, Iacopo Masi, Stefano Soatto
      Pages 25-37
    4. Moshe Lichtenstein, Gautam Pai, Ron Kimmel
      Pages 38-50
    5. Jean Mélou, Yvain Quéau, Fabien Castan, Jean-Denis Durou
      Pages 51-63
  3. Inpainting, Interpolation and Compression

    1. Front Matter
      Pages 65-65
    2. Matthias Augustin, Joachim Weickert, Sarah Andris
      Pages 67-78
    3. Laurent Hoeltgen, Michael Breuß, Georg Radow
      Pages 79-91
    4. Pascal Peter, Jan Contelly, Joachim Weickert
      Pages 92-103
    5. Arthur Renaudeau, François Lauze, Fabien Pierre, Jean-François Aujol, Jean-Denis Durou
      Pages 104-116
  4. Inverse Problems in Imaging

    1. Front Matter
      Pages 117-117
    2. Julianne Chung, Matthias Chung, J. Tanner Slagel
      Pages 119-130
    3. Charles-Alban Deledalle, Nicolas Papadakis, Joseph Salmon, Samuel Vaiter
      Pages 131-143
    4. Limei Huo, Shousheng Luo, Yiqiu Dong, Xue-Cheng Tai, Yang Wang
      Pages 144-155
    5. Nicolai André Brogaard Riis, Yiqiu Dong
      Pages 156-167
  5. Optimization Methods in Imaging

    1. Front Matter
      Pages 169-169
    2. Alexander Effland, Erich Kobler, Thomas Pock, Martin Rumpf
      Pages 171-182
    3. Johannes Hertrich, Miroslav Bačák, Sebastian Neumayer, Gabriele Steidl
      Pages 183-195
  6. PDEs and Level-Set Methods

    1. Front Matter
      Pages 197-197
    2. Remco Duits, Etienne St-Onge, Jim Portegies, Bart Smets
      Pages 211-223
    3. Lingfeng Li, Shousheng Luo, Xue-Cheng Tai, Jiang Yang
      Pages 224-235
    4. Martin Welk, Joachim Weickert
      Pages 236-248
  7. Registration and Reconstruction

    1. Front Matter
      Pages 249-249
    2. Kai Brehmer, Hari Om Aggrawal, Stefan Heldmann, Jan Modersitzki
      Pages 251-262
    3. Veronica Corona, Angelica I. Aviles-Rivero, Noémie Debroux, Martin Graves, Carole Le Guyader, Carola-Bibiane Schönlieb et al.
      Pages 263-274
    4. Kento Hosoya, Atsushi Imiya
      Pages 275-287
  8. Scale-Space Methods

    1. Front Matter
      Pages 289-289
    2. Leon Bungert, Martin Burger, Daniel Tenbrinck
      Pages 291-302
    3. Marcelo Cárdenas, Pascal Peter, Joachim Weickert
      Pages 303-314
    4. Ido Cohen, Adi Falik, Guy Gilboa
      Pages 315-327
    5. Arthur Leclaire, Julien Rabin
      Pages 341-353
  9. Segmentation and Labeling

    1. Front Matter
      Pages 355-355
    2. Vedrana Andersen Dahl, Anders Bjorholm Dahl
      Pages 357-368
    3. Jacob Daniel Kirstejn Hansen, François Lauze
      Pages 369-380
    4. Yuchen He, Sung Ha Kang
      Pages 381-392
    5. Ruben Hühnerbein, Fabrizio Savarino, Stefania Petra, Christoph Schnörr
      Pages 393-405
    6. Lukas Kiefer, Stefania Petra, Martin Storath, Andreas Weinmann
      Pages 406-418
    7. Jozsef Molnar, Ervin Tasnadi, Peter Horvath
      Pages 419-431
    8. Matthias Zisler, Artjom Zern, Stefania Petra, Christoph Schnörr
      Pages 432-444
  10. Variational Methods

    1. Front Matter
      Pages 445-445
    2. Luis Alvarez, Daniel Santana-Cedrés, Pablo G. Tahoces, José M. Carreira
      Pages 447-458
    3. Thomas Batard, Eduard Ramon Maldonado, Gabriele Steidl, Marcelo Bertalmío
      Pages 459-471
    4. Marcelo Bertalmío, Luca Calatroni, Valentina Franceschi, Benedetta Franceschiello, Dario Prandi
      Pages 472-484
    5. Martin Burger, Yury Korolev, Carola-Bibiane Schönlieb, Christiane Stollenwerk
      Pages 485-497
    6. Raymond H. Chan, Damiana Lazzaro, Serena Morigi, Fiorella Sgallari
      Pages 498-509
    7. Clemens Kirisits, Otmar Scherzer, Eric Setterqvist
      Pages 510-521

About these proceedings

Introduction

This book constitutes the proceedings of the 7th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2019, held in Hofgeismar, Germany, in June/July 2019. 
The 44 papers included in this volume were carefully reviewed and selected for inclusion in this book. They were organized in topical sections named: 3D vision and feature analysis; inpainting, interpolation and compression; inverse problems in imaging; optimization methods in imaging; PDEs and level-set methods; registration and reconstruction;  scale-space methods; segmentation and labeling; and variational methods. 

Keywords

artificial intelligence computer vision estimation image coding image compression image processing image reconstruction image registration image segmentation inverse problems motion estimation neural networks numerical experiments numerical methods optical flows partial differential equations reconstruction regularization signal processing variational methods

Editors and affiliations

  1. 1.University of LübeckLübeckGermany
  2. 2.University of Erlangen-Nuremberg (FAU)ErlangenGermany
  3. 3.University of LübeckLübeckGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-22368-7
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-030-22367-0
  • Online ISBN 978-3-030-22368-7
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site