Browsing by Author "Iuricich, Federico"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Application-oriented Analysis of Material Interface Reconstruction Algorithms in Time-varying Bijel Simulations
    (The Eurographics Association, 2022) Bao, Xueyi; Karthikeyan, Nikhil; Schiller, Ulf D.; Iuricich, Federico; Agus, Marco; Aigner, Wolfgang; Hoellt, Thomas
    Multimaterial interface reconstruction has been investigated over the years both from visualization and analytical point of view using different metrics. When focusing on visualization, interface continuity and smoothness are used to quantify interface quality. When the end goal is interface analysis, metrics closer to the physical properties of the material are preferred (e.g., curvature, tortuosity). In this paper, we re-evaluate three Multimaterial Interface Reconstruction (MIR) algorithms, already integrated in established visualization frameworks, under the lens of application-oriented metrics. Specifically, we analyze interface curvature, particle-interface distance, and medial axis-interface distance in a time-varying bijel simulation. Our analysis shows that the interface presenting the best visual qualities is not always the most useful for domain scientists when evaluating the material properties.
  • Thumbnail Image
    Item
    CellTrackVis: Analyzing the Performance of Cell Tracking Algorithms
    (The Eurographics Association, 2022) Li, Weimin; Zhang, Xiang; Stern, Alan; Birtwistle, Marc; Iuricich, Federico; Agus, Marco; Aigner, Wolfgang; Hoellt, Thomas
    Live-cell imaging is a common data acquisition technique used by biologists to analyze cell behavior. Since manually tracking cells in a video sequence is extremely time-consuming, many automatic algorithms have been developed in the last twenty years to accomplish the task. However, none of these algorithms can yet claim robust tracking performance at the varying of acquisition conditions (e.g., cell type, acquisition device, cell treatments). While many visualization tools exist to help with cell behavior analysis, there are no tools to help with the algorithm's validation. This paper proposes CellTrackVis, a new visualization tool for evaluating cell tracking algorithms. CellTrackVis allows comparing automatically generated cell tracks with ground truth data to help biologists select the best-suited algorithm for their experimental pipeline. Moreover, CellTackVis can be used as a debugging tool while developing a new cell tracking algorithm to investigate where, when, and why each tracking error occurred.
  • Thumbnail Image
    Item
    Efficient Homology‐Preserving Simplification of High‐Dimensional Simplicial Shapes
    (© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2020) Fellegara, Riccardo; Iuricich, Federico; De Floriani, Leila; Fugacci, Ulderico; Benes, Bedrich and Hauser, Helwig
    Simplicial complexes are widely used to discretize shapes. In low dimensions, a 3D shape is represented by discretizing its boundary surface, encoded as a triangle mesh, or by discretizing the enclosed volume, encoded as a tetrahedral mesh. High‐dimensional simplicial complexes have recently found their application in topological data analysis. Topological data analysis aims at studying a point cloud P, possibly embedded in a high‐dimensional metric space, by investigating the topological characteristics of the simplicial complexes built on P. Analysing such complexes is not feasible due to their size and dimensions. To this aim, the idea of simplifying a complex while preserving its topological features has been proposed in the literature. Here, we consider the problem of efficiently simplifying simplicial complexes in arbitrary dimensions. We provide a new definition for the edge contraction operator, based on a top‐based data structure, with the objective of preserving structural aspects of a simplicial shape (i.e., its homology), and a new algorithm for verifying the link condition on a top‐based representation. We implement the simplification algorithm obtained by coupling the new edge contraction and the link condition on a specific top‐based data structure, that we use to demonstrate the scalability of our approach.