Feature extraction and visualization of flow fields (original) (raw)
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Feature Extraction and Visualisation of Flow Fields
Eurographics, 2002
Flow visualisation has already been a very attractive part of visualisation research for a long time. Usually very large data sets need to be processed, which often consist of multivariate data with a large number of sample locations, often arranged in multiple time steps. Recently, the steadily increasing performance of computers again has become a driving factor for a new
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2003
Abstract Flow visualisation is an attractive topic in data visualisation, offering great challenges for research. Very large data sets must be processed, consisting of multivariate data at large numbers of grid points, often arranged in many time steps. Recently, the steadily increasing performance of computers again has become a driving force for new advances in flow visualisation, especially in techniques based on texturing, feature extraction, vector field clustering, and topology extraction.
The State of the Art in Flow visualization, part 1: Direct, Texture-based and Geometric Techniques
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Topology-Based Flow Visualization, The State of the Art
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Flow visualization research has made rapid advances in recent years, especially in the area of topology-based flow visualization. The ever increasing size of scientific data sets favors algorithms that are capable of extracting important subsets of the data, leaving the scientist with a more manageable representation that may be visualized interactively. Extracting the topology of a flow achieves the goal of obtaining a compact representation of a vector or tensor field while simultaneously retaining its most important features. We present the ...
Mathematics and Visualization, 2009
We describe an approach to visually analyzing the dynamic behavior of 3D time-dependent flow fields by considering the behavior of the path lines. At selected positions in the 4D space-time domain, we compute a number of local and global properties of path lines describing relevant features of them. The resulting multivariate data set is analyzed by applying state-of-the-art information visualization approaches in the sense of a set of linked views (scatter plots, parallel coordinates, etc.) with interactive brushing and focus+context visualization. The selected path lines with certain properties are integrated and visualized as colored 3D curves. This approach allows an interactive exploration of intricate 4D flow structures. We apply our method to a number of flow data sets and describe how path line attributes are used for describing characteristic features of these flows.
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2000
ABSTRACT The investigation of flow data can be eased by the visualization of topological information about the flow. Especially, when empirical models or numerical results from flow simulation are investigated, often the first step of analysis is to search structural elements, like fixed points, separatrices, etc. The work presented in this paper focuses on the visualization of 3D dynamical systems (comparable to flow data) on the basis of results which are obtained by automatic analysis of the flow topology.
The State of the Art in Flow Visualization: Dense and Texture‐Based Techniques
2004
Abstract Flow visualization has been a very attractive component of scientific visualization research for a long time. Usually very large multivariate datasets require processing. These datasets often consist of a large number of sample locations and several time steps. The steadily increasing performance of computers has recently become a driving factor for a reemergence in flow visualization research, especially in texture-based techniques. In this paper, dense, texture-based flow visualization techniques are discussed.
Illustrative flow visualization: State of the art, trends and challenges
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Abstract Flow visualization is a well established branch of scientific visualization and it currently represents an invaluable resource to many fields, like automotive design, meteorology and medical imaging. Thanks to the capabilities of modern hardware, flow datasets are increasing in size and complexity, and traditional flow visualization techniques need to be updated and improved in order to deal with the upcoming challenges.
The State of the Art in Flow Visualization: Structure-Based Techniques
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Flow visualization has been a very active subfield of scientific visualization in recent years. From the resulting large variety of methods this paper discusses structure-based techniques. The aim of these approaches is to partition the flow in areas of common behavior. Based on this partitioning, subsequent visualization techniques can be applied. A classification is suggested and advantages/disadvantages of the different techniques are discussed as well.