The State of the Art in Flow visualization, part 1: Direct, Texture-based and Geometric Techniques (original) (raw)

Feature extraction and visualization of flow fields

2002

Abstract 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 boom in flow visualisation, especially in techniques based on feature extraction, vector field clustering, and topology extraction.

The state of the art in flow visualisation: Feature extraction and tracking

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: 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.

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

Visualisation of complex flows using texture-based techniques

ANZIAM Journal, 2013

Detailed representations of complex flow datasets are often difficult to generate using traditional vector visualisation techniques such as arrow plots and streamlines. This is particularly true when the flow regime changes in time. Texture based techniques, which are based on the advection of dense textures, are novel techniques for visualising such flows. We review two popular texture based techniques and their application to flow datasets sourced from active research projects. The techniques investigated were Line integral convolution [Cabral and Leedom, SIGGRAPH'93, pp.263-270, 1993], and Image based flow visualisation [van Wijk, SIGGRAPH'02, pp.745-754, 2002]. We evaluate these and report on their effectiveness from a visualisation perspective. We also report on their ease of implementation and computational overheads.

Topology-Based Flow Visualization, The State of the Art

Mathematics and Visualization, 2007

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 ...

Applications of Texture-Based Flow Visualization

Flow visualization is a classic sub-field of scientific visualization. The task of flow visualization algorithms is to depict vector data, i.e., data with magnitude and direction. An important category of flow visualization techniques makes use of textures in order to convey the properties of a vector field. Recently, several research advances have been made in this special category, of dense, texture-based techniques. We present the application of the most recent texture-based techniques to real world data from oceanography and meteorology, computational fluid dynamics (CFD) in the automotive industry, and medicine. We describe the motivations for using texture-based algorithms as well as the important recent advancements required for their successful incorporation into industry grade software. Our goal is to appeal to practitioners in the field interested in learning how these recent techniques can help them gain insight into problems that engineers and other professionals may be faced with on a daily basis. Many of these applications have only recently become possible. Keywords: flow visualization; vector field visualization; texture; computational fluid dynamics (CFD); meteorology; oceanography; in-cylinder flow; visualization systems; engine simulation; medical application

Design and implementation of geometric and texture-based flow visualization techniques

2005

Abstract Usually, research related software consists of individual, isolated prototypes because researchers are interested in a small proof-of-concept application for demonstration. Here we present software developed for research purposes, but which has been included into a larger, commercial visualization system. We describe the design and implementation of a flow visualization subsystem within the framework of a software package capable of modeling, simulation, and visualization of CFD simulation data.

Illustrative flow visualization: State of the art, trends and challenges

2012

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

2007

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.