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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

  • Conference proceedings
  • © 2019

Overview

Editors:
  1. Jan Lellmann

    1. University of Lübeck, Lübeck, Germany

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  2. Martin Burger

    1. University of Erlangen-Nuremberg (FAU), Erlangen, Germany

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    You can also search for this editor in PubMed   Google Scholar

  3. Jan Modersitzki

    1. University of Lübeck, Lübeck, Germany

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

Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)

Included in the following conference series:

Conference proceedings info: SSVM 2019.

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Table of contents (44 papers)

  1. Front Matter

    Pages i-xvii
    Download chapter PDF

  2. 3D Vision and Feature Analysis

    1. Front Matter

      Pages 1-1
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    2. The Fractional Harris-Laplace Feature Detector

      • Matthew Adams
      Pages 3-12

    3. Macrocanonical Models for Texture Synthesis

      • Valentin De Bortoli, Agnès Desolneux, Bruno Galerne, Arthur Leclaire
      Pages 13-24

    4. Finding Structure in Point Cloud Data with the Robust Isoperimetric Loss

      • Shay Deutsch, Iacopo Masi, Stefano Soatto
      Pages 25-37

    5. Deep Eikonal Solvers

      • Moshe Lichtenstein, Gautam Pai, Ron Kimmel
      Pages 38-50

    6. A Splitting-Based Algorithm for Multi-view Stereopsis of Textureless Objects

      • Jean Mélou, Yvain Quéau, Fabien Castan, Jean-Denis Durou
      Pages 51-63

  3. Inpainting, Interpolation and Compression

    1. Front Matter

      Pages 65-65
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    2. Pseudodifferential Inpainting: The Missing Link Between PDE- and RBF-Based Interpolation

      • Matthias Augustin, Joachim Weickert, Sarah Andris
      Pages 67-78

    3. Towards PDE-Based Video Compression with Optimal Masks and Optic Flow

      • Laurent Hoeltgen, Michael Breuß, Georg Radow
      Pages 79-91

    4. Compressing Audio Signals with Inpainting-Based Sparsification

      • Pascal Peter, Jan Contelly, Joachim Weickert
      Pages 92-103

    5. Alternate Structural-Textural Video Inpainting for Spot Defects Correction in Movies

      • 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
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    2. Iterative Sampled Methods for Massive and Separable Nonlinear Inverse Problems

      • Julianne Chung, Matthias Chung, J. Tanner Slagel
      Pages 119-130

    3. Refitting Solutions Promoted by \(\ell _{12}\) Sparse Analysis Regularizations with Block Penalties

      • Charles-Alban Deledalle, Nicolas Papadakis, Joseph Salmon, Samuel Vaiter
      Pages 131-143

    4. An Iteration Method for X-Ray CT Reconstruction from Variable-Truncation Projection Data

      • Limei Huo, Shousheng Luo, Yiqiu Dong, Xue-Cheng Tai, Yang Wang
      Pages 144-155

    5. A New Iterative Method for CT Reconstruction with Uncertain View Angles

      • Nicolai André Brogaard Riis, Yiqiu Dong
      Pages 156-167

  5. Optimization Methods in Imaging

    1. Front Matter

      Pages 169-169
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    2. Time Discrete Geodesics in Deep Feature Spaces for Image Morphing

      • Alexander Effland, Erich Kobler, Thomas Pock, Martin Rumpf
      Pages 171-182

    3. Minimal Lipschitz Extensions for Vector-Valued Functions on Finite Graphs

      • Johannes Hertrich, Miroslav Bačák, Sebastian Neumayer, Gabriele Steidl
      Pages 183-195
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  1. Scale Space and Variational Methods in Computer Vision

Keywords

About this book

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. 

Editors and Affiliations

  • University of Lübeck, Lübeck, Germany

    Jan Lellmann, Jan Modersitzki

  • University of Erlangen-Nuremberg (FAU), Erlangen, Germany

    Martin Burger

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