3,584 research outputs found
Drishti, a volume exploration and presentation tool
Among several rendering techniques for volumetric data, direct volume rendering is a powerful visualization tool for a wide variety of applications. This paper describes the major features of hardware based volume exploration and presentation tool - Drishti. The word, Drishti, stands for vision or insight in Sanskrit, an ancient Indian language. Drishti is a cross-platform open-source volume rendering system that delivers high quality, state of the art renderings. The features in Drishti include, though not limited to, production quality rendering, volume sculpting, multi-resolution zooming, transfer function blending, profile generation, measurement tools, mesh generation, stereo/anaglyph/crosseye renderings. Ultimately, Drishti provides an intuitive and powerful interface for choreographing animations
Constraint-based technique for haptic volume exploration
Journal ArticleWe present a haptic rendering technique that uses directional constraints to facilitate enhanced exploration modes for volumetric datasets. The algorithm restricts user motion in certain directions by incrementally moving a proxy point along the axes of a local reference frame. Reaction forces are generated by a spring coupler between the proxy and the data probe, which can be tuned to the capabilities of the haptic interface. Secondary haptic effects including field forces, friction, and texture can be easily incorporated to convey information about additional characteristics of the data. We illustrate the technique with two examples: displaying fiber orientation in heart muscle layers and exploring diffusion tensor fiber tracts in brain white matter tissue. Initial evaluation of the approach indicates that haptic constraints provide an intuitive means for displaying directional information in volume data
Unwind: Interactive Fish Straightening
The ScanAllFish project is a large-scale effort to scan all the world's
33,100 known species of fishes. It has already generated thousands of
volumetric CT scans of fish species which are available on open access
platforms such as the Open Science Framework. To achieve a scanning rate
required for a project of this magnitude, many specimens are grouped together
into a single tube and scanned all at once. The resulting data contain many
fish which are often bent and twisted to fit into the scanner. Our system,
Unwind, is a novel interactive visualization and processing tool which
extracts, unbends, and untwists volumetric images of fish with minimal user
interaction. Our approach enables scientists to interactively unwarp these
volumes to remove the undesired torque and bending using a piecewise-linear
skeleton extracted by averaging isosurfaces of a harmonic function connecting
the head and tail of each fish. The result is a volumetric dataset of a
individual, straight fish in a canonical pose defined by the marine biologist
expert user. We have developed Unwind in collaboration with a team of marine
biologists: Our system has been deployed in their labs, and is presently being
used for dataset construction, biomechanical analysis, and the generation of
figures for scientific publication
VisIVOWeb: A WWW Environment for Large-Scale Astrophysical Visualization
This article presents a newly developed Web portal called VisIVOWeb that aims
to provide the astrophysical community with powerful visualization tools for
large-scale data sets in the context of Web 2.0. VisIVOWeb can effectively
handle modern numerical simulations and real-world observations. Our
open-source software is based on established visualization toolkits offering
high-quality rendering algorithms. The underlying data management is discussed
with the supported visualization interfaces and movie-making functionality. We
introduce VisIVOWeb Network, a robust network of customized Web portals for
visual discovery, and VisIVOWeb Connect, a lightweight and efficient solution
for seamlessly connecting to existing astrophysical archives. A significant
effort has been devoted for ensuring interoperability with existing tools by
adhering to IVOA standards. We conclude with a summary of our work and a
discussion on future developments
Doctor of Philosophy
dissertationVisualization and exploration of volumetric datasets has been an active area of research for over two decades. During this period, volumetric datasets used by domain users have evolved from univariate to multivariate. The volume datasets are typically explored and classified via transfer function design and visualized using direct volume rendering. To improve classification results and to enable the exploration of multivariate volume datasets, multivariate transfer functions emerge. In this dissertation, we describe our research on multivariate transfer function design. To improve the classification of univariate volumes, various one-dimensional (1D) or two-dimensional (2D) transfer function spaces have been proposed; however, these methods work on only some datasets. We propose a novel transfer function method that provides better classifications by combining different transfer function spaces. Methods have been proposed for exploring multivariate simulations; however, these approaches are not suitable for complex real-world datasets and may be unintuitive for domain users. To this end, we propose a method based on user-selected samples in the spatial domain to make complex multivariate volume data visualization more accessible for domain users. However, this method still requires users to fine-tune transfer functions in parameter space transfer function widgets, which may not be familiar to them. We therefore propose GuideME, a novel slice-guided semiautomatic multivariate volume exploration approach. GuideME provides the user, an easy-to-use, slice-based user interface that suggests the feature boundaries and allows the user to select features via click and drag, and then an optimal transfer function is automatically generated by optimizing a response function. Throughout the exploration process, the user does not need to interact with the parameter views at all. Finally, real-world multivariate volume datasets are also usually of large size, which is larger than the GPU memory and even the main memory of standard work stations. We propose a ray-guided out-of-core, interactive volume rendering and efficient query method to support large and complex multivariate volumes on standard work stations
VisIVO - Integrated Tools and Services for Large-Scale Astrophysical Visualization
VisIVO is an integrated suite of tools and services specifically designed for
the Virtual Observatory. This suite constitutes a software framework for
effective visual discovery in currently available (and next-generation) very
large-scale astrophysical datasets. VisIVO consists of VisiVO Desktop - a stand
alone application for interactive visualization on standard PCs, VisIVO Server
- a grid-enabled platform for high performance visualization and VisIVO Web - a
custom designed web portal supporting services based on the VisIVO Server
functionality. The main characteristic of VisIVO is support for
high-performance, multidimensional visualization of very large-scale
astrophysical datasets. Users can obtain meaningful visualizations rapidly
while preserving full and intuitive control of the relevant visualization
parameters. This paper focuses on newly developed integrated tools in VisIVO
Server allowing intuitive visual discovery with 3D views being created from
data tables. VisIVO Server can be installed easily on any web server with a
database repository. We discuss briefly aspects of our implementation of VisiVO
Server on a computational grid and also outline the functionality of the
services offered by VisIVO Web. Finally we conclude with a summary of our work
and pointers to future developments
Multivariate volume visualization through dynamic projections
pre-printWe propose a multivariate volume visualization framework that tightly couples dynamic projections with a high-dimensional transfer function design for interactive volume visualization. We assume that the complex, high-dimensional data in the attribute space can be well-represented through a collection of low-dimensional linear subspaces, and embed the data points in a variety of 2D views created as projections onto these subspaces. Through dynamic projections, we present animated transitions between different views to help the user navigate and explore the attribute space for effective transfer function design. Our framework not only provides a more intuitive understanding of the attribute space but also allows the design of the transfer function under multiple dynamic views, which is more flexible than being restricted to a single static view of the data. For large volumetric datasets, we maintain interactivity during the transfer function design via intelligent sampling and scalable clustering. Using examples in combustion and climate simulations, we demonstrate how our framework can be used to visualize interesting structures in the volumetric space
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