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Imaging, Vision and Learning Based on Optimization and PDEs

IVLOPDE, Bergen, Norway, August 29 – September 2, 2016

  • Xue-Cheng Tai
  • Egil Bae
  • Marius Lysaker
Conference proceedings IVLOPDE 2016

Part of the Mathematics and Visualization book series (MATHVISUAL)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Image Reconstruction from Incomplete Data

    1. Front Matter
      Pages 1-1
    2. Michael Hintermüller, Andreas Langer, Carlos N. Rautenberg, Tao Wu
      Pages 3-26
    3. Bin Wu, Talal Rahman, Xue-Cheng Tai
      Pages 47-64
  3. Image Enhancement, Restoration and Registration

    1. Front Matter
      Pages 65-65
    2. Syed Waqas Zamir, Javier Vazquez-Corral, Marcelo Bertalmío
      Pages 67-100
    3. Benedikt Loewenhauser, Jan Lellmann
      Pages 101-120
    4. Faming Fang, Yingying Fang, Tieyong Zeng
      Pages 121-141
  4. 3D Image Understanding and Classification

    1. Front Matter
      Pages 143-143
    2. Todd C. Torgersen, V. Paúl Pauca, Robert J. Plemmons, Dejan Nikic, Jason Wu, Robert Rand
      Pages 145-164
  5. Machine Learning and Big Data Analysis

    1. Front Matter
      Pages 201-201
    2. Xavier Bresson, Huiyi Hu, Thomas Laurent, Arthur Szlam, James von Brecht
      Pages 203-219
    3. Zhaoyi Meng, Javier Sánchez, Jean-Michel Morel, Andrea L. Bertozzi, P. Jeffrey Brantingham
      Pages 221-239
    4. Haixia Liu, Lizhang Miao, Yang Wang
      Pages 241-251
  6. Back Matter
    Pages 253-255

About these proceedings

Introduction

This volume presents the peer-reviewed proceedings of the international conference Imaging, Vision and Learning Based on Optimization and PDEs (IVLOPDE), held in Bergen, Norway, in August/September 2016. The contributions cover state-of-the-art research on mathematical techniques for image processing, computer vision and machine learning based on optimization and partial differential equations (PDEs).

It has become an established paradigm to formulate problems within image processing and computer vision as PDEs, variational problems or finite dimensional optimization problems. This compact yet expressive framework makes it possible to incorporate a range of desired properties of the solutions and to design algorithms based on well-founded mathematical theory. A growing body of research has also approached more general problems within data analysis and machine learning from the same perspective, and demonstrated the advantages over earlier, more established algorithms.

This volume will appeal to all mathematicians and computer scientists interested in novel techniques and analytical results for optimization, variational models and PDEs, together with experimental results on applications ranging from early image formation to high-level image and data analysis.

Keywords

image processing computer vision machine learning pattern recognition optimization partial differential equations calculus of variations numerical analysis

Editors and affiliations

  • Xue-Cheng Tai
    • 1
  • Egil Bae
    • 2
  • Marius Lysaker
    • 3
  1. 1.Department of MathematicsUniversity of BergenBergenNorway
  2. 2.Norwegian Defence Research EstablishmentKjellerNorway
  3. 3.University of South-Eastern NorwayPorsgrunnNorway

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-91274-5
  • Copyright Information Springer International Publishing AG, part of Springer Nature 2018
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-91273-8
  • Online ISBN 978-3-319-91274-5
  • Series Print ISSN 1612-3786
  • Series Online ISSN 2197-666X
  • Buy this book on publisher's site
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