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Scale Space and Variational Methods in Computer Vision

6th International Conference, SSVM 2017, Kolding, Denmark, June 4-8, 2017, Proceedings

  • Conference proceedings
  • © 2017

Overview

Editors:
  1. François Lauze

    1. University of Copenhagen , Copenhagen, Denmark

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  2. Yiqiu Dong

    1. Technical University of Denmark , Kongens Lyngby, Denmark

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  3. Anders Bjorholm Dahl

    1. Technical University of Denmark , Kongens Lyngby, Denmark

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 10302)

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 2017.

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Table of contents (55 papers)

  1. Restoration and Reconstruction

    1. Directional Total Generalized Variation Regularization for Impulse Noise Removal

      • Rasmus Dalgas Kongskov, Yiqiu Dong
      Pages 221-231

  2. Tomographic Reconstruction

    1. Front Matter

      Pages 233-233
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    2. A Novel Convex Relaxation for Non-binary Discrete Tomography

      • Jan Kuske, Paul Swoboda, Stefania Petra
      Pages 235-246

    3. Image Reconstruction by Multilabel Propagation

      • Matthias Zisler, Freddie Åström, Stefania Petra, Christoph Schnörr
      Pages 247-259

    4. User-Friendly Simultaneous Tomographic Reconstruction and Segmentation with Class Priors

      • Hans Martin Kjer, Yiqiu Dong, Per Christian Hansen
      Pages 260-270

    5. An Optimal Transport-Based Restoration Method for Q-Ball Imaging

      • Thomas Vogt, Jan Lellmann
      Pages 271-282

    6. Nonlinear Flows for Displacement Correction and Applications in Tomography

      • Guozhi Dong, Otmar Scherzer
      Pages 283-294

    7. Performance Bounds for Cosparse Multichannel Signal Recovery via Collaborative-TV

      • Lukas Kiefer, Stefania Petra
      Pages 295-307

    8. Simultaneous Reconstruction and Segmentation of CT Scans with Shadowed Data

      • François Lauze, Yvain Quéau, Esben Plenge
      Pages 308-319

  3. Segmentation

    1. Front Matter

      Pages 321-321
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    2. Graphical Model Parameter Learning by Inverse Linear Programming

      • Vera Trajkovska, Paul Swoboda, Freddie Åström, Stefania Petra
      Pages 323-334

    3. A Fast MBO Scheme for Multiclass Data Classification

      • Matt Jacobs
      Pages 335-347

    4. Convex Non-Convex Segmentation over Surfaces

      • Martin Huska, Alessandro Lanza, Serena Morigi, Fiorella Sgallari
      Pages 348-360

    5. Numerical Integration of Riemannian Gradient Flows for Image Labeling

      • Fabrizio Savarino, Ruben Hühnerbein, Freddie Åström, Judit Recknagel, Christoph Schnörr
      Pages 361-372

    6. MAP Image Labeling Using Wasserstein Messages and Geometric Assignment

      • Freddie Åström, Ruben Hühnerbein, Fabrizio Savarino, Judit Recknagel, Christoph Schnörr
      Pages 373-385

    7. Multinomial Level-Set Framework for Multi-region Image Segmentation

      • Tammy Riklin Raviv
      Pages 386-395

    8. Local Mean Multiphase Segmentation with HMMF Models

      • Jacob Daniel Kirstejn Hansen, François Lauze
      Pages 396-407

    9. An Efficient Lagrangian Algorithm for an Anisotropic Geodesic Active Contour Model

      • Günay Doğan
      Pages 408-420

    10. A Probabilistic Framework for Curve Evolution

      • Vedrana Andersen Dahl, Anders Bjorholm Dahl
      Pages 421-432

  4. Convex and Non-convex Modeling and Optimization in Imaging

    1. Front Matter

      Pages 433-433
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  1. Scale Space and Variational Methods in Computer Vision

Keywords

About this book

This book constitutes the refereed proceedings of the 6th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2017, held in Kolding, Denmark, in June 2017. The 55 revised full papers presented were carefully reviewed and selected from 77 submissions. The papers are organized in the following topical sections: Scale Space and PDE Methods; Restoration and Reconstruction; Tomographic Reconstruction; Segmentation; Convex and Non-Convex Modeling and Optimization in Imaging; Optical Flow, Motion Estimation and Registration; 3D Vision.

Editors and Affiliations

  • University of Copenhagen , Copenhagen, Denmark

    François Lauze

  • Technical University of Denmark , Kongens Lyngby, Denmark

    Yiqiu Dong, Anders Bjorholm Dahl

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