Activity Detection in Scientific Visualization

Özer, Sedat; Silver, Deborah; Bemis, K. G.; Martin, Pino

doi:10.1109/tvcg.2013.117

Cited by 20 publications

(14 citation statements)

References 72 publications

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“…An alternative approach to generate the set of nodes is to extract features in data and represent them as nodes. Many relationship‐wise techniques [SSZC94, SW97, WS09, OSBM14] adopt this strategy, although the actual methods to detect features vary. We refer readers to Section 3.5 for details.…”

Section: Discussionmentioning

confidence: 99%

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Graphs in Scientific Visualization: A Survey

Wang

Tao

2016

Computer Graphics Forum

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Graphs represent general node‐link diagrams and have long been utilized in scientific visualization for data organization and management. However, using graphs as a visual representation and interface for navigating and exploring scientific data sets has a much shorter history, yet the amount of work along this direction is clearly on the rise in recent years. In this paper, we take a holistic perspective and survey graph‐based representations and techniques for scientific visualization. Specifically, we classify these representations and techniques into four categories, namely partition‐wise, relationship‐wise, structure‐wise and provenance‐wise. We survey related publications in each category, explaining the roles of graphs in related work and highlighting their similarities and differences. At the end, we reexamine these related publications following the graph‐based visualization pipeline. We also point out research trends and remaining challenges in graph‐based representations and techniques for scientific visualization.

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Section: Discussionmentioning

confidence: 99%

“…The recent work of Ozer et al . [OSBM14] advances the state‐of‐the‐art feature tracking by leveraging Petri nets to model and detect activities in scientific visualization. Petri nets are graph‐based techniques that model and visualize various types of behaviours.…”

Section: Relationship‐wise Representations and Techniquesmentioning

confidence: 99%

Graphs in Scientific Visualization: A Survey

Wang

Tao

2016

Computer Graphics Forum

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“…Ozer et al [OSBM14] detect spatio‐temporal patterns in tracking graphs using petri‐nets. This work is focused on describing and detecting events and transitions in tracking graphs based on a user‐supplied, abstract description.…”

Section: Related Workmentioning

confidence: 99%

Global Feature Tracking and Similarity Estimation in Time‐Dependent Scalar Fields

Saikia

Weinkauf

2017

Computer Graphics Forum

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We present an algorithm for tracking regions in time‐dependent scalar fields that uses global knowledge from all time steps for determining the tracks. The regions are defined using merge trees, thereby representing a hierarchical segmentation of the data in each time step. The similarity of regions of two consecutive time steps is measured using their volumetric overlap and a histogram difference. The main ingredient of our method is a directed acyclic graph that records all relevant similarity information as follows: the regions of all time steps are the nodes of the graph, the edges represent possible short feature tracks between consecutive time steps, and the edge weights are given by the similarity of the connected regions. We compute a feature track as the global solution of a shortest path problem in the graph. We use these results to steer the – to the best of our knowledge – first algorithm for spatio‐temporal feature similarity estimation. Our algorithm works for 2D and 3D time‐dependent scalar fields. We compare our results to previous work, showcase its robustness to noise, and exemplify its utility using several real‐world data sets.

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“…Climate 2016, 4, 43 3 of 15 introduced a prototype of activity detection in scientific visualization [20]. Their goal was to develop a framework and facilitate scientists to model and identify a spatiotemporal pattern from massive data sets.…”

Section: Related Workmentioning

confidence: 99%

Polar Cyclone Identification from 4D Climate Data in a Knowledge-Driven Visualization System

Wang

2016

Climate

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Abstract:Arctic cyclone activity has a significant association with Arctic warming and Arctic ice decline. Cyclones in the North Pole are more complex and less developed than those in tropical regions. Identifying polar cyclones proves to be a task of greater complexity. To tackle this challenge, a new method which utilizes pressure level data and velocity field is proposed to improve the identification accuracy. In addition, the dynamic, simulative cyclone visualized with a 4D (four-dimensional) wind field further validated the identification result. A knowledge-driven system is eventually constructed for visualizing and analyzing an atmospheric phenomenon (cyclone) in the North Pole. The cyclone is simulated with WebGL on in a web environment using particle tracing. To achieve interactive frame rates, the graphics processing unit (GPU) is used to accelerate the process of particle advection. It is concluded with the experimental results that: (1) the cyclone identification accuracy of the proposed method is 95.6% when compared with the NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) reanalysis data; (2) the integrated knowledge-driven visualization system allows for streaming and rendering of millions of particles with an interactive frame rate to support knowledge discovery in the complex climate system of the Arctic region.

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Activity Detection in Scientific Visualization

Cited by 20 publications

References 72 publications

Graphs in Scientific Visualization: A Survey

Graphs in Scientific Visualization: A Survey

Global Feature Tracking and Similarity Estimation in Time‐Dependent Scalar Fields

Polar Cyclone Identification from 4D Climate Data in a Knowledge-Driven Visualization System

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