KEYWORDS: Feature extraction, Visualization, Detection and tracking algorithms, Computer simulations, Image processing algorithms and systems, Visual analytics, Information visualization, Data communications, Data acquisition, Databases
Time varying simulations are common in many scientific domains to study the evolution of phenomena or features. The data produced in these simulations is massive. Instead of just one dataset of 5123 or 10243 (for regular gridded simulations) there could now be hundreds to thousands of timesteps. For datasets with evolving features, feature analysis and visualization tools are crucial to help interpret all the information. For example, it is usually important to know how many regions are evolving, what are their lifetimes, do they merge with others, how does the volume/mass change, etc. Therefore, feature based approaches, such as feature tracking and feature quantification are needed to follow identified regions over time. In our previous work, we have developed a methodology for analyzing time-varying datasets which tracks 3D amorphous features as they evolve in time. However, the implementation is for single-processor non-adaptive grids and for massive multiresolution datasets this approach needs to be distributed and enhanced. In this paper, we describe extensions to our feature extraction and tracking methodology for distributed AMR simulations. Two different paradigms are described, a fully distributed and a partial- merge strategy. The benefits and implementations of both are discussed.
KEYWORDS: Field programmable gate arrays, Image processing, Visualization, Volume visualization, Volume rendering, Data modeling, 3D modeling, Logic, Composites, Head
Volume visualization is a popular method for viewing simulated or experimental 3D data sets from applications such as medical imaging, computational fluid dynamics, and climate modeling. However, most software and low-cost hardware implementations of visualization algorithms do not have sufficient performance for inter-active viewing. This paper discusses a method for low-cost, parallel hardware acceleration of volume rendering using a PC-hosted FPGA board. Our method uses a parallel distributed memory approach for compositing and tranformation of volume data, and it provides insight into efficient use of low-cost memory systems.
An essential part of visualization of massive time-dependent data sets is to identify, quantify and track important regions and structures (objects of interest). This is true for almost all disciplines since the crux of understanding the original simulation, experiment or observation is the study of the evolution of the 'objects' present. Some well known examples include tracking the progression of a storm, the motion and change of the 'ozone hole', or the movement of vortices shed by the meandering Gulf stream. In this paper, we describe work-in- progress on extracting and tracking three dimensional evolving objects in time dependent simulations. The simulations are from ongoing research in computational fluid dynamics (CFD), however, the tracking procedures are general and are appropriate for many other disciplines.
Our goal is to visualize the shape and structure in a data set of earthquake hypocenters collected from the island of Hawaii over a twenty-three year period. The earthquakes provide information about the magma system in this region and define a collection of conduits and reservoirs around the active volcanos. Because the data is scattered, traditional visualization methods are difficult to use (without interpolation). In this paper, we present some preliminary efforts to extract and define the structures within the data set.
KEYWORDS: Visualization, Mathematical modeling, 3D image processing, Data modeling, Data processing, Fluid dynamics, 3D modeling, Diagnostics, Solitons, Optical tracking
Studying the stability, evolution, and interaction of coherent structures over time-varying data sets is the essence of discovery in many branches of science. In this paper, the authors discuss the process of visiometrics: visualizing, extracting, quantifying, and mathematizing evolving amorphous objects. This concept is applied to data sets from fluid dynamical problems. In particular, for three-dimensional phenomena, it is shown how this approach enhances understanding of the topology and kinematics of these problems.
Proceedings Volume Editor (1)
This will count as one of your downloads.
You will have access to both the presentation and article (if available).
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.