Perspectives in Shape Analysis

  • Michael Breuß
  • Alfred Bruckstein
  • Petros Maragos
  • Stefanie Wuhrer
Conference proceedings

Part of the Mathematics and Visualization book series (MATHVISUAL)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Numerical Computing for Shape Analysis

    1. Front Matter
      Pages 1-1
    2. Yong Chul Ju, Daniel Maurer, Michael Breuß, Andrés Bruhn
      Pages 43-72
    3. Joviša Žunić, Paul L. Rosin, Mehmet Ali Aktaş
      Pages 117-135
    4. Frank R. Schmidt, Lena Gorelick, Ismail Ben Ayed, Yuri Boykov, Thomas Brox
      Pages 137-154
  3. Sparse Data Representation and Machine Learning for Shape Analysis

    1. Front Matter
      Pages 169-169
    2. Honghua Li, Hao Zhang
      Pages 171-185
    3. Leila De Floriani, Ulderico Fugacci, Federico Iuricich
      Pages 187-209
    4. Jonathan Pokrass, Alexander M. Bronstein, Michael M. Bronstein, Pablo Sprechmann, Guillermo Sapiro
      Pages 211-230
    5. Matthias Vestner, Emanuele Rodolà, Thomas Windheuser, Samuel Rota Bulò, Daniel Cremers
      Pages 231-248
  4. Deformable Shape Modeling

    1. Front Matter
      Pages 273-273
    2. William A. P. Smith
      Pages 299-319
    3. Petros Maragos, Vassilis Pitsikalis, Athanasios Katsamanis, George Pavlakos, Stavros Theodorakis
      Pages 321-344
    4. Alexander Hewer, Stefanie Wuhrer, Ingmar Steiner, Korin Richmond
      Pages 345-365
  5. Back Matter
    Pages 367-370

About these proceedings


This book presents recent advances in the field of shape analysis. Written by experts in the fields of continuous-scale shape analysis, discrete shape analysis and sparsity, and numerical computing who hail from different communities, it provides a unique view of the topic from a broad range of perspectives.

Over the last decade, it has become increasingly affordable to digitize shape information at high resolution. Yet analyzing and processing this data remains challenging because of the large amount of data involved, and because modern applications such as human-computer interaction require real-time processing. Meeting these challenges requires interdisciplinary approaches that combine concepts from a variety of research areas, including numerical computing, differential geometry, deformable shape modeling, sparse data representation, and machine learning. On the algorithmic side, many shape analysis tasks are modeled using partial differential equations, which can be solved using tools from the field of numerical computing. The fields of differential geometry and deformable shape modeling have recently begun to influence shape analysis methods. Furthermore, tools from the field of sparse representations, which aim to describe input data using a compressible representation with respect to a set of carefully selected basic elements, have the potential to significantly reduce the amount of data that needs to be processed in shape analysis tasks. The related field of machine learning offers similar potential.

The goal of the Dagstuhl Seminar on New Perspectives in Shape Analysis held in February 2014 was to address these challenges with the help of the latest tools related to geometric, algorithmic and numerical concepts and to bring together researchers at the forefront of shape analysis who can work together to identify open problems and novel solutions. The book resulting from this seminar will appeal to researchers in the field of shape analysis, image and vision, from those who want to become more familiar with the field, to experts interested in learning about the latest advances.


discrete and continuous shape models machine learning mathematical morphology shape analysis sparsity

Editors and affiliations

  • Michael Breuß
    • 1
  • Alfred Bruckstein
    • 2
  • Petros Maragos
    • 3
  • Stefanie Wuhrer
    • 4
  1. 1.Institute for Applied Mathematics and Scientific ComputingBrandenburg University of TechnologyCottbusGermany
  2. 2.Israel Institute of Technology TechnionHaifaIsrael
  3. 3.School of Electrical and Computer EngineeringNational Technical University of AthensAthensGreece
  4. 4.INRIA, Grenoble Rhône-AlpesSaint IsmierFrance

Bibliographic information