| 1. |
- Lukasczyk, Jonas, et al.
(författare)
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Cinema Darkroom : A Deferred Rendering Framework for Large-Scale Datasets
- 2020
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Ingår i: 2020 IEEE 10TH SYMPOSIUM ON LARGE DATA ANALYSIS AND VISUALIZATION (LDAV). - : IEEE. - 9781728184685 ; , s. 37-41
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Konferensbidrag (refereegranskat)abstract
- This paper presents a framework that fully leverages the advantages of a deferred rendering approach for the interactive visualization of large-scale datasets. Geometry buffers (G-Buffers) are generated and stored in situ, and shading is performed post hoc in an interactive image-based rendering front end. This decoupled framework has two major advantages. First, the G-Buffers only need to be computed and stored once-which corresponds to the most expensive part of the rendering pipeline. Second, the stored G-Buffers can later be consumed in an image-based rendering front end that enables users to interactively adjust various visualization parameters-such as the applied color map or the strength of ambient occlusion-where suitable choices are often not known a priori. This paper demonstrates the use of Cinema Darkroom on several real-world datasets, highlighting CDs ability to effectively decouple the complexity and size of the dataset from its visualization.
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| 2. |
- Masood, Talha Bin, et al.
(författare)
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An Overview of the Topology ToolKit
- 2021
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Ingår i: Topological Methods in Data Analysis and Visualization VI. - Cham : Springer. - 9783030835002 - 9783030834999 ; , s. 327-342
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Bokkapitel (refereegranskat)abstract
- This software paper gives an overview of the features supported by the Topology ToolKitTopology ToolKit (TTK), which is an  Open-sourceopen-source library for  Topological data analysistopological data analysis (TDA). TTK implements, in a generic and efficient way, a substantial collection of reference algorithms in TDA. Since its initial public release in 2017, both its user and developer bases have grown, resulting in a significant increase in the number of supported features. In contrast to the original paper introducing TTK [40] (which detailed the core algorithms and data structures of TTK), the purpose of this Softwaresoftware paper is to describe the list of features currently supported by TTK, ranging from image segmentation tools to advanced topological analysis of high-dimensional data, with concrete usage examples available on the TTK website [42].
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| 3. |
- Nilsson, Emma, et al.
(författare)
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Exploring Cyclone Evolution with Hierarchical Features
- 2022
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Ingår i: 2022 IEEE WORKSHOP ON TOPOLOGICAL DATA ANALYSIS AND VISUALIZATION (TOPOINVIS 2022). - : IEEE. - 9781665493543 - 9781665493550 ; , s. 92-102
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Konferensbidrag (refereegranskat)abstract
- The problem of tracking and visualizing cyclones is still an active area of climate research, since the nature of cyclones varies depending on geospatial location and temporal season, resulting in no clear mathematical definition. Thus, many cyclone tracking methods are tailored to specific datasets and therefore do not support general cyclone extraction across the globe. To address this challenge, we present a conceptual application for exploring cyclone evolution by organizing the extracted cyclone tracks into hierarchical groups. Our approach is based on extrema tracking, and the resulting tracks can be defined in a multi-scale structure by grouping the points based on a novel feature descriptor defined on the merge tree, so-called crown features. Consequently, multiple parameter settings can be visualized and explored in a level-of-detail approach, supporting experts to quickly gain insights on cyclonic formation and evolution. We describe a general cyclone exploration pipeline that consists of four modular building blocks: (1) an extrema tracking method, (2) multiple definitions of cyclones as groups of extrema, including crown features, (3) the correlation of cyclones based on the underlying tracking information, and (4) a hierarchical visualization of the resulting feature tracks and their spatial embedding, allowing exploration on a global and local scale. In order to be as flexible as possible, our pipeline allows for exchanging every module with different techniques, such as other tracking methods and cyclone definitions.
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| 4. |
- Nilsson, Emma, et al.
(författare)
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Towards Benchmark Data Generation for Feature Tracking in Scalar Fields
- 2022
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Ingår i: 2022 IEEE WORKSHOP ON TOPOLOGICAL DATA ANALYSIS AND VISUALIZATION (TOPOINVIS 2022). - : IEEE. - 9781665493543 - 9781665493550 ; , s. 103-112
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Konferensbidrag (refereegranskat)abstract
- We describe a benchmark data generator for tracking methods for two- and three-dimensional time-dependent scalar fields. More and more topology-based tracking methods are presented in the visualization community, but the validation and evaluation of the tracking results are currently limited to qualitative visual approaches. We present a pipeline for creating different ground truth features that support evaluating tracking methods based on quantitative measures. In short, our approach randomly simulates a temporal point cloud with birth, death, split, merge, and continuation events, where the points are then used to derive a scalar field whose topological features correspond to the points. These scalar fields can be used as the input for different tracking methods, where the computed tracks can be compared against the ground truth feature evolution. This approach facilitates directly comparing the results of different tracking methods, independent of the initial feature characterization.
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