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Laufende Projekte

Anders finanziert

  • GV-AP1

    Discrete geometric structures motivated by applications in architecture

    Prof. Alexander I. Bobenko

    Projektleiter: Prof. Alexander I. Bobenko
    Projekt Mitglieder: -
    Laufzeit: 01.07.2012 - 30.06.2020
    Status: laufend
    Standort: Technische Universität Berlin

    Beschreibung

    Many of today's most striking buildings are nontraditional freeform shapes. Their fabrication is a big challenge, but also a rich source of research topics in geometry. Project A08 addresses key questions such as: "How can we most efficiently represent and explore the variety of manufacturable designs?" or "Can we do this even under structural constraints such as force equilibrium?" Answers to these questions are expected to support the development of next generation modelling tools which combine shape design with key aspects of function and fabrication.

    http://www.discretization.de/en/projects/C01/
  • GV-AP5

    Geometric Constraints for Polytopes

    Raman Sanyal / Prof. Günter M. Ziegler

    Projektleiter: Raman Sanyal / Prof. Günter M. Ziegler
    Projekt Mitglieder: -
    Laufzeit: 01.07.2012 - 30.06.2020
    Status: laufend
    Standort: Freie Universität Berlin

    Beschreibung

    Polytopes are solid bodies bounded by flat facets. Alternatively, they can be described as the convex hull of their vertices. Thus a polytope can be presented by information on two aspects, a geometric one: "What are the coordinates of the vertices" and a combinatorial one: "Which vertex is incident to which face". There are many interrelations between these two levels: Combinatorial requirements enforce restrictions on the geometry, and vice versa. A03 studies aspects of this interplay.

    http://www.discretization.de/en/projects/A03/
  • GV-AP11

    Transfer of research prototypes to the commercial visualization systems Amira and Avizo

    Dr. Steffen Prohaska

    Projektleiter: Dr. Steffen Prohaska
    Projekt Mitglieder: -
    Laufzeit: 01.09.2012 - 31.12.2020
    Status: laufend
    Standort: Konrad-Zuse-Zentrum für Informationstechnik Berlin

    Beschreibung

    Amira and Avizo are professional software products for 3D visualization, geometry reconstruction and data analysis. The software has been designed and developed at the Zuse Institute Berlin (ZIB) in the department of Visualization and Data Analysis. Today, Amira and Avizo are jointly developed by the ZIB and the FEI Visualization Sciences Group in Bordeaux, France. The goal of the Amira and Avizo Technology Transfer project is to speed up the integration of new algorithms developed at ZIB into the commercial versions of Amira and Avizo. FEI benefits from this project by an early integration of state of the art research into the commercial software. ZIB benefits from the technology transfer in two important ways: Customer support for commercially available modules is provided by the companies; and research prototypes are improved and maintained after the end of a research project, making it easier for other researchers to build upon them in the future.

    http://www.zib.de/projects/transfer-research-prototypes-commercial-visualization-systems-amira-and-avizo
  • GV-AP15

    Geometrical and topological microstructure analysis of metal and steel grains

    PD Dr. Frank Lutz / Prof. Dr. Boris Springborn

    Projektleiter: PD Dr. Frank Lutz / Prof. Dr. Boris Springborn
    Projekt Mitglieder: -
    Laufzeit: 01.03.2016 - 28.02.2022
    Status: laufend
    Standort: Technische Universität Berlin

    Beschreibung

    The objective of this project is to develop geometrical and topological approaches to study boundary surfaces of steel grains from voxel data. We plan to use methods from Discrete Differential Geometry and Combinatorial Topology to extract curvature information of grain interfaces in combination with grain topologies.

    http://page.math.tu-berlin.de/~lutz/steel_interfaces/
  • GV-AP16

    Computational and structural aspects of point set surfaces

    Prof. Dr. Konrad Polthier

    Projektleiter: Prof. Dr. Konrad Polthier
    Projekt Mitglieder: Dr. Konstantin Poelke / M.Sc. Martin Skrodzki
    Laufzeit: 01.07.2016 - 30.06.2020
    Status: laufend
    Standort: Freie Universität Berlin

    Beschreibung

    In the project “Computational and structural aspects of point set surfaces”, we will develop discrete differential geometric representations for point set surfaces and effective computational algorithms. Instead of first reconstructing a triangle based mesh, our operators act directly on the point set data. The concepts will have contact to meshless methods and ansatz spaces of radial basis functions. As proof of concept of our theoretical investigations we will transfer and implement key algorithms from surface processing, for example, for surface parametrization and for feature aware mesh filtering on point set surfaces. Point set surfaces have a more than 15 year long history in geometry processing and computer graphics as they naturally arise in 3D-data acquisition processes. A guiding principle of these algorithms is the direct processing of raw scanning data without prior meshing – a principle that has a long-established history in classical numerical computations. However, their usage mostly restricts to full dimensional domains embedded in R2 or R3 and a thorough investigation of a differential geometric representation of point set surfaces and their properties is not available. Inspired by the notion of manifolds, we will develop new concepts for meshless charts and atlases. These will be used to implement higher order differential operators including curvature descriptors. On this solid basis of meshless differential operators, we will develop novel algorithms for important geometry processing tasks, such as feature recognition, filtering operations, and surface parameterization.

    http://www.discretization.de/en/projects/C05/
  • GV-AP17

    Machine Learning Approaches for Enhanced, Shape Model Based 3D Image Segmentation

    Dr. Hans Lamecker / Dr.-Ing. Stefan Zachow

    Projektleiter: Dr. Hans Lamecker / Dr.-Ing. Stefan Zachow
    Projekt Mitglieder: Dr. Anirban Mukhopadhyay
    Laufzeit: 01.10.2014 - 30.09.2019
    Status: laufend
    Standort: Konrad-Zuse-Zentrum für Informationstechnik Berlin

    Beschreibung

    Fully automatic segmentation of arbitrary anatomical structures from 3D medical image data is a challenging, yet unsolved problem. Though fully automatic segmentation is essential for further clinical analysis, complexity of anatomical structures across population makes a generalized segmentation scheme extremely challenging. Moreover, specific challenges of different imaging modalities have so far hindered the possibility of a general purpose fully automatic 3D segmentation framework. Statistical 3D shape models have proven to be valuable shape priors that are to be deformed within their range of normal variation in shape to match the respective image information. Within the project, we are aiming to combine Machine Learning along with the statistical shape priors for getting a step closer to a general 3D image segmentation approach. In particular, Machine Learning techniques for image matching based on intensity will be developed in order to improve both the model building as well as the segmentation process. Image-based Cost Functions: Principal Component Analysis (PCA) on local intensity profiles has not proven to beneficially act as a robust cost function. Random Forest Regression Voting (RFRV), though a powerful method for 2D image data, turned out to be impractical for 3D data, due to huge memory consumption and computational time. Dictionary Learning (DL) does not require any heuristics and is general enough to be applied across anatomies and modalities. DL operations are matrix operations, thus being efficiently evaluated. Joint Dictionary Learning: Given 3D image data and accordingly segmented anatomical structures of interest, rotational invariant histograms of oriented gradients (HoG) are sampled at the structures’ boundaries. These feature samples are used as input for learning a dictionary. A second dictionary is learnt for background image information. A combined dictionary of foreground and background features has been established, acting as a cost function for image segmentation. Cost Function for a test patch: Sum of residuals from representations by the two dictionaries.

    http://www.zib.de/projects/machine-learning-approaches-enhanced-shape-model-based-3d-image-segmentation