09. – 14. Februar 2014, Dagstuhl Seminar 14072

New Perspectives in Shape Analysis


Michael Breuß (BTU Cottbus, DE)
Alfred M. Bruckstein (Technion – Haifa, IL)
Petros Maragos (National Technical University of Athens, GR)
Stefanie Wuhrer (Universität des Saarlandes, DE)

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Dagstuhl seminar 14072 New Perspectives in Shape Analysis took place February 9-14, 2014. 28 researchers from North America and Europe discussed state-of-the-art, current challenges, and promising future research directions in the areas of 2-D and 3-D shape analysis from a cross-disciplinary point of view. Participants included international experts from the fields of continuous-scale shape analysis, discrete shape analysis and sparsity, and numerical computing. The seminar consisted of an opening session, 11 scientific presentation sessions, as well as a break-out session, which provided room for in-depth discussions in small groups. Furthermore, there was time for extensive discussions both between the talks and in the evenings.

This seminar was motivated by the observation that in everyday life, geometric shapes surround us, and that the understanding of concepts describing these shapes is at the heart of various applications, such as ergonomic design, virtual shopping, scientific and medical visualization, realistic simulation, the design of natural user interfaces, and semantic scene understanding. Traditionally, the notion of shape has been studied either by analysing a sparse set of marker positions on 3-D shapes, mostly for medical imaging applications, or by analysing projections of shapes in 2-D images, mostly for image processing and computer vision applications. New challenges in the analysis and processing of such data arise with the increasing amount of data captured by sensors used to acquire shapes, and with modern applications such as natural user interfaces that require real-time processing of the input shapes. Recently, it has become increasingly affordable to digitise 3-D shapes using multiple modalities, such as laser-range scanners, image-based reconstruction systems, or depth cameras like the Kinect sensor. Using these dense 3-D shapes in the above mentioned applications requires processing and describing the shapes in an efficient and informative way.

The purpose of this seminar was to address these challenges with the latest tools related to geometric, algorithmic and numerical concepts. To do so, we brought together researchers working on shape analysis topics from different perspectives.

As the analysis of 3-D shapes and deformable shape models have received much interest recently, classic shape analysis tools from differential geometry have a fresh influence in the field. Being related to the issue how to represent shapes efficiently, the research areas of sparse data representation and machine learning have begun to influence shape analysis modelling and the numerics. Especially in the context of three-dimensional data (or even higher-dimensional data sets), efficient optimization methods will certainly become increasingly important since many shape analysis models can be cast in the form of an optimization problem.

While the fields of modelling and numerical computing are strongly related when it comes to shape analysis applications, modelling is seen as a hot topic in computer science while numerical computing is often seen as a mathematical domain. The purpose in bringing together researchers from those different communities sharing substantial interest in shape analysis was to explore the benefits of a cross-disciplinary point of view. More specifically,

  • researchers in continuous-scale shape analysis brought to the meeting their knowledge of differential and variational models and also of classic numerical methods in the field,
  • researchers in discrete shape analysis and sparsity brought to the meeting their knowledge about the latest techniques in efficient data representations and related machine learning techniques, as well as efficient data structures and discrete optimization methods, and
  • researchers in numerical computing brought to the meeting their knowledge of numerical techniques for PDEs and optimization.

As the demands in the individual fields are high, the research groups in which the most interesting techniques are proposed are quite specialised. This not only holds for discrete and continuous-scale modelling and numerical computing, but also for the areas of sparsity and machine learning that were discussed during this seminar. Because of this, there is no regular conference or workshop that serves as a meeting place for an exchange of ideas of these groups.

Promising new ways to combine the latest techniques from these different fields were identified during in-depth discussions in small groups. Some especially promising research directions in the areas of intrinsic structure detection, co-segmentation of shapes, shape from shading, modelling deformable shapes, and models for face shapes, were discussed in small groups during the break-out session.

  Creative Commons BY 3.0 Unported license
  Michael Breuß, Alfred M. Bruckstein, Petros Maragos, and Stefanie Wuhrer

Related Dagstuhl Seminar


  • Computer Graphics / Computer Vision


  • Shape analysis
  • Mathematical morphology
  • Shape reconstruction
  • Sparsity
  • Machine learning
  • Numerical computing
  • Level set methods
  • Optimization methods


Bücher der Teilnehmer 

Buchausstellung im Erdgeschoss der Bibliothek

(nur in der Veranstaltungswoche).


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