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Scale Space and Variational Methods in Computer Vision

Second International Conference, SSVM 2009, Voss, Norway, June 1-5, 2009. Proceedings

  • Conference proceedings
  • © 2009

Overview

Editors:
  1. Xue-Cheng Tai

    1. Department of Mathematics, University of Bergen, Bergen, Norway

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  2. Knut Mørken

    1. Department of Informatics and Centre of Mathematics for Applications, University of Oslo, Oslo, Norway

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  3. Marius Lysaker

    1. Simula Research Laboratory, Lysaker, Norway

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  4. Knut-Andreas Lie

    1. SINTEF ICT, Oslo, Norway

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 5567)

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 2009.

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Table of contents (71 papers)

  1. Front Matter

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  2. Segmentation and Detection

    1. Graph Cut Optimization for the Piecewise Constant Level Set Method Applied to Multiphase Image Segmentation

      • Egil Bae, Xue-Cheng Tai
      Pages 1-13

    2. Tubular Anisotropy Segmentation

      • Fethallah Benmansour, Laurent D. Cohen
      Pages 14-25

    3. An Unconstrained Multiphase Thresholding Approach for Image Segmentation

      • Benjamin Berkels
      Pages 26-37

    4. Extraction of the Intercellular Skeleton from 2D Images of Embryogenesis Using Eikonal Equation and Advective Subjective Surface Method

      • Paul Bourgine, Peter Frolkovič, Karol Mikula, Nadine Peyriéras, Mariana Remešíková
      Pages 38-49

    5. On Level-Set Type Methods for Recovering Piecewise Constant Solutions of Ill-Posed Problems

      • Adriano DeCezaro, Antonio Leitão, Xue-Cheng Tai
      Pages 50-62

    6. The Nonlinear Tensor Diffusion in Segmentation of Meaningful Biological Structures from Image Sequences of Zebrafish Embryogenesis

      • Olga Drblíková, Karol Mikula, Nadine Peyriéras
      Pages 63-74

    7. Composed Segmentation of Tubular Structures by an Anisotropic PDE Model

      • Elena Franchini, Serena Morigi, Fiorella Sgallari
      Pages 75-86

    8. Extrapolation of Vector Fields Using the Infinity Laplacian and with Applications to Image Segmentation

      • Laurence Guillot, Carole Le Guyader
      Pages 87-99

    9. A Schrödinger Equation for the Fast Computation of Approximate Euclidean Distance Functions

      • Karthik S. Gurumoorthy, Anand Rangarajan
      Pages 100-111

    10. Semi-supervised Segmentation Based on Non-local Continuous Min-Cut

      • Nawal Houhou, Xavier Bresson, Arthur Szlam, Tony F. Chan, Jean-Philippe Thiran
      Pages 112-123

    11. Momentum Based Optimization Methods for Level Set Segmentation

      • Gunnar Läthén, Thord Andersson, Reiner Lenz, Magnus Borga
      Pages 124-136

    12. Optimization of Divergences within the Exponential Family for Image Segmentation

      • Francois Lecellier, Stephanie Jehan-Besson, Jalal Fadili, Gilles Aubert, Marinette Revenu
      Pages 137-149

    13. Convex Multi-class Image Labeling by Simplex-Constrained Total Variation

      • Jan Lellmann, Jörg Kappes, Jing Yuan, Florian Becker, Christoph Schnörr
      Pages 150-162

    14. Geodesically Linked Active Contours: Evolution Strategy Based on Minimal Paths

      • Julien Mille, Laurent D. Cohen
      Pages 163-174

    15. Validation of Watershed Regions by Scale-Space Statistics

      • Tomoya Sakai, Atsushi Imiya
      Pages 175-186

    16. Adaptation of Eikonal Equation over Weighted Graph

      • Vinh-Thong Ta, Abderrahim Elmoataz, Olivier Lézoray
      Pages 187-199

    17. A Variational Model for Interactive Shape Prior Segmentation and Real-Time Tracking

      • Manuel Werlberger, Thomas Pock, Markus Unger, Horst Bischof
      Pages 200-211

  3. Image Enhancement and Reconstruction

    1. A Nonlinear Probabilistic Curvature Motion Filter for Positron Emission Tomography Images

      • Musa Alrefaya, Hichem Sahli, Iris Vanhamel, Dinh Nho Hao
      Pages 212-223

    2. Finsler Geometry on Higher Order Tensor Fields and Applications to High Angular Resolution Diffusion Imaging

      • Laura Astola, Luc Florack
      Pages 224-234
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  1. Scale Space and Variational Methods in Computer Vision

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About this book

This book contains 71 original, scienti?c articles that address state-of-the-art researchrelatedto scale space and variationalmethods for image processing and computer vision. Topics covered in the book range from mathematical analysis of both established and new models, fast numerical methods, image analysis, segmentation, registration, surface and shape construction and processing, to real applications in medical imaging and computer vision. The ideas of scale spaceandvariationalmethodsrelatedtopartialdi?erentialequationsarecentral concepts. The papers re?ect the newest developments in these ?elds and also point to the latest literature. All the papers were submitted to the Second International Conference on Scale Space and Variational Methods in Computer Vision, which took place in Voss, Norway, during June 1–5, 2009. The papers underwent a peer review process similar to that of high-level journals in the ?eld. We thank the authors, the Scienti?c Committee, the Program Committee and the reviewers for their hard work and helpful collaboration. Their contribution has been crucial for the e?cient processing of this book, and for the success of the conference.

Editors and Affiliations

  • Department of Mathematics, University of Bergen, Bergen, Norway

    Xue-Cheng Tai

  • Department of Informatics and Centre of Mathematics for Applications, University of Oslo, Oslo, Norway

    Knut Mørken

  • Simula Research Laboratory, Lysaker, Norway

    Marius Lysaker

  • SINTEF ICT, Oslo, Norway

    Knut-Andreas Lie

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