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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shengyin Gu Olivier Poch Bernd Hamann Patrice Koehl Institute for Data Analysis and Visualization Department of Computer Science University of California Davis CA USA Laboratoire de Biologie Structurale IGBMC Strasbourg France Institute for Data Analysis and Visualization Department of Computer Science University of California Davis CA USA Genome Center University of California Davis CA USA

The amino acid sequence of a protein is the key to understanding its structure and ultimately its function in the cell. This paper addresses the fundamental issue of encoding amino acids in ways that the visualization of protein sequences facilitates the decoding of its information content. We show that a feature-based representation in a three-dimensional (3D) space derived from substitution matrices provides an adequate representation from which the domain content of a protein can be predicted. In addition, we show that each dimension of the feature space can be related to a physical property of the amino acids.

关键词： Amino acids Protein sequence Bioinformatics data visualization USA Councils Genomics Encoding data analysis Computer science Biological information theory

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A foveal inset for large display environments

A foveal inset for large display environments

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ACM International Conference on Virtual Reality Continuum and its Applications, VRCIA 2006

作者： Staadt, Oliver G. Ahlborn, Benjamin A. Kreylos, Oliver Hamann, Bernd Institute for Data Analysis and Visualization Department of Computer Science University of California Davis United States

ISBN: (纸本)1595933247

We introduce a system that adds a foveal inset to large-scale projection displays. The effective resolution of the foveal inset projection is higher than the original display resolution, allowing the user to see more details and finer features in large data sets. The foveal inset is generated by projecting a high-resolution image onto a mirror mounted on a pan - tilt unit that is controlled by the user with a laser pointer. Our implementation is based on Chromium and supports many OpenGL applications without modifications. We present experimental results using high-resolution image data from medical imaging and areal photography. © 2006 ACM.

关键词： Calibration

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Clifford Pattern Matching for Color Image Edge Detection 1

Clifford Pattern Matching for Color Image Edge Detection

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1st Workshop of the DFG's International Research Training Group "visualization of Large and Unstructured data Sets - Applications in Geospatial Planning, Modeling, and Engineering"

作者： Schlemmer, Michael Hagen, Hans Hotz, Ingrid Hamann, Bernd TU Kaiserslautern Computer Science Department KaiserslauternD-67653 Germany University of California Davis Institute for Data Analysis and Visualization DavisCA95616 United States

ISBN: (纸本)9783885794387

Feature detection and pattern matching play an important role in visualization. Originally developed for images and scalar fields, pattern matching methods become increasingly interesting for other applications, e.g., vector fields. To apply pattern matching to vector fields the basic concepts of convolution and fast Fourier transform (FFT) have to be generalized to vector fields. A formalism supporting an elegant generalization of these concepts is provided by the Clifford Algebra, originally developed for describing geometry and geometric operations. We discuss an application of the Clifford Pattern Matching (CPM). We apply CPM to images for "Clifford Color Edge Detection"(C2ED), an approach for detecting edges and other features in color images. The basic idea is to treat color value tripels as vectors and apply the pattern matching algorithm to the resulting vector field. We introduce vector-valued filters for edge detection and present results. © 2006 Gesellschaft für Informatik, Bonn.

关键词： Pattern matching

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Frameworks for visualization at the extreme scale

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Journal of Physics: Conference Series 2007年第1期78卷

作者： Joy, Kenneth I. Miller, Mark Childs, Hank Bethel, Wes Clyne, John Ostrouchov, George Ahern, Sean Institute for Data Analysis and Visualization University of California Davis CA 95616-8562 One Shields Avenue United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore CA 94551 P.O. Box 808 L-561 United States Computing Sciences Division Lawrence Berkeley National Laboratory University of California Berkeley CA 94720-8139 United States National Center for Atmospheric Research Boulder CO United States National Center for Computational Sciences Oak Ridge National Laboratory MS-6016 Oak Ridge TN 37831 One Bethel Valley Road United States

The challenges of visualization at the extreme scale involve issues of scale, complexity, temporal exploration and uncertainty. The visualization and Analytics Center for Enabling Technologies (VACET) focuses on leveraging scientific visualization and analytics software technology as an enabling technology to increased scientific discovery and insight. In this paper, we introduce new uses of visualization frameworks through the introduction of Equivalence Class Functions (ECFs). These functions give a new class of derived quantities designed to greatly expand the ability of the end user to explore and visualize data. ECFs are defined over equivalence classes (i.e., groupings) of elements from an original mesh, and produce summary values for the classes as output. ECFs can be used in the visualization process to directly analyze data, or can be used to synthesize new derived quantities on the original mesh. The design of ECFs enable a parallel implementation that allows the use of these techniques on massive data sets that require parallel processing. © 2007 IOP Publishing Ltd.

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A practical system for laser pointer interaction on large displays

A practical system for laser pointer interaction on large di...

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VRST'05 - ACM Symposium on Virtual Reality Software and Technology 2005

作者： Ahlborn, Benjamin A. Thompson, David Kreylos, Oliver Hamann, Bernd Staadt, Oliver G. Institute for Data Analysis and Visualization Dept. of Comp. Sci. University of California Davis Davis CA 95616 United States Sandia National Labs Livermore CA 94550 United States

ISBN: (纸本)1595930981

Much work has been done on the development of laser pointers as interaction devices. Typically a camera captures images of a display surface and extracts a laser pointer dot location. This location is processed and used as a cursor position. While the current literature well explains such a system, we feel that some important practical concerns have gone unaddressed. We discuss the design of such a tracking system, focusing on key practical implementation details. In particular we present a robust and efficient dot detection algorithm that allows us to use our system under a variety lighting conditions, and allows us to reduce the amount of image parsing required to find a laser position by an order of magnitude. Copyright 2005 ACM.

关键词： Interactive computer graphics

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SciDAC visualization and analytics center for enabling technology

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Journal of Physics: Conference Series 2007年第1期78卷

作者： Bethel, E. Wes Johnson, Chris Joy, Ken Ahern, Sean Pascucci, Valerio Childs, Hank Cohen, Jonathan Duchaineau, Mark Hamann, Bernd Hansen, Charles Laney, Dan Lindstrom, Peter Meredith, Jeremy Ostrouchov, George Parker, Steven Silva, Claudio Sanderson, Allen Tricoche, Xavier Computing Sciences Division Lawrence Berkeley National Laboratory University of California Berkeley CA 94720-8139 United States Scientific Computing and Imaging Institute University of Utah Salt Lake City UT 84112 72 S. Central Campus Drive United States Institute for Data Analysis and Visualization University of California Davis CA 95616-8562 One Shields Avenue United States National Center for Computational Sciences Oak Ridge National Laboratory MS-6016 Oak Ridge TN 37831 One Bethel Valley Road United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore CA 94551 P.O. Box 808 L-561 United States

The visualization and Analytics Center for Enabling Technologies (VACET) focuses on leveraging scientific visualization and analytics software technology as an enabling technology for increasing scientific productivity and insight. Advances in computational technology have resulted in an "information big bang," which in turn has created a significant data understanding challenge. This challenge is widely acknowledged to be one of the primary bottlenecks in contemporary science. The vision of VACET is to adapt, extend, create when necessary, and deploy visual data analysis solutions that are responsive to the needs of DOE's computational and experimental scientists. Our center is engineered to be directly responsive to those needs and to deliver solutions for use in DOE's large open computing facilities. The research and development directly target data understanding problems provided by our scientific application stakeholders. VACET draws from a diverse set of visualization technology ranging from production quality applications and application frameworks to state-of-the-art algorithms for visualization, analysis, analytics, data manipulation, and data management. © 2007 IOP Publishing Ltd.

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HDF5-FastQuery: Accelerating complex queries on HDF datasets using fast bitmap indices

HDF5-FastQuery: Accelerating complex queries on HDF datasets...

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18th International Conference on Scientific and Statistical database Management, SSDBM 2006

作者： Gosink, Luke Shalf, John Stockinger, Kurt Wu, Kesheng Bethel, Wes Institute for Data Analysis and Visualization University of California at Davis One Shields Ave Davis CA 95616 United States Computational Research Division Lawrence Berkeley National Laboratory One Cyclotron Road Berkeley CA 94720 United States

ISBN: (纸本)0769525903

Large scale scientific data is often stored in scientific data formats such as FITS, netCDF and HDF. These storage formats are of particular interest to the scientific user community since they provide multi-dimensional storage and retrieval. However, one of the drawbacks of these storage formats is that they do not support semantic indexing which is important for interactive data analysis where scientists look for features of interests such as "Find all supernova explosions where energy > 105 and temperature > 106". In this paper we present a novel approach called HDF5-FastQuery to accelerate the data access of large HDF5 files by introducing multi-dimensional semantic indexing. Our implementation leverages an efficient indexing technology called bitmap indexing that has been widely used in the database community. Bitmap indices are especially well suited for interactive exploration of large-scale readonly data. Storing the bitmap indices into the HDF5 file has the following advantages: a) Significant performance speedup of accessing subsets of multi-dimensional data and b) portability of the indices across multiple computer platforms. We will present an API that simplifies the execution of queries on HDF5 files for general scientific applications and data analysis. The design is flexible enough to accommodate the use of arbitrary indexing technology for semantic range queries. We will also provide a detailed performance analysis of HDF5-FastQuery for both synthetic and scientific data. The results demonstrate that our proposed approach for multi-dimensional queries is up to a factor of 2 faster than HDF5. © 2006 IEEE.

关键词： Semantics

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PointCloudXplore: A visualization Tool for 3D Gene Expression data 1

PointCloudXplore: A Visualization Tool for 3D Gene Expressio...

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1st Workshop of the DFG's International Research Training Group "visualization of Large and Unstructured data Sets - Applications in Geospatial Planning, Modeling, and Engineering"

作者： Rübel, Oliver Weber, Gunther H. Keränen, Soile V. E. Fowlkes, Charless C. Hendriks, Cris L. Luengo Simirenko, Lisa Shah, Nameeta Y. Eisen, Michael B. Biggin, Mark D. Hagen, Hans Sudar, Damir Malik, Jitendra Knowles, David W. Hamann, Bernd International Research Training Group 'Visualization of Large and Unstructured Data Sets' University of Kaiserslautern Germany Institute for Data Analysis and Visualization University of California DavisCA United States Life Sciences and Genomics Divisions Lawrence Berkeley National Laboratory BerkeleyCA United States Computer Science Division University of California BerkeleyCA United States

ISBN: (纸本)9783885794387

The Berkeley Drosophila Transcription Network Project (BDTNP) has developed a suite of methods that support quantitative, computational analysis of threedimensional (3D) gene expression patterns with cellular resolution in early Drosophila embryos, aiming at a more in-depth understanding of gene regulatory networks. We describe a new tool, called PointCloudXplore (PCX), that supports effective 3D gene expression data exploration. PCX is a visualization tool that uses the established visualization techniques of multiple views, brushing, and linking to support the analysis of high-dimensional datasets that describe many genes' expression. Each of the views in PointCloudXplore shows a different gene expression data property. Brushing is used to select and emphasize data associated with defined subsets of embryo cells within a view. Linking is used to show in additional views the expression data for a group of cells that have first been highlighted as a brush in a single view, allowing further data subset properties to be determined. In PCX, physical views of the data are linked to abstract data displays such as parallel coordinates. Physical views show the spatial relationships between different genes' expression patterns within an embryo. Abstract gene expression data displays on the other hand allow for an analysis of relationships between different genes directly in the gene expression space. We discuss on parallel coordinates as one example abstract data view currently available in PCX. We have developed several extensions to standard parallel coordinates to facilitate brushing and the visualization of 3D gene expression data. © 2006 Gesellschaft für Informatik, Bonn.

关键词： visualization

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PointCloudXplore: Visual analysis of 3D Gene Expression data Using Physical Views and Parallel Coordinates

PointCloudXplore: Visual Analysis of 3D Gene Expression Data...

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8th Joint Eurographics/IEEE VGTC Symposium on visualization, EuroVis 2006

作者： Rübel, O. Weber, G.H. Keränen, S.V.E. Fowlkes, C.C. Hendriks, C.L. Luengo Simirenko, L. Shah, N.Y. Eisen, M.B. Biggin, M.D. Hagen, H. Sudar, D. Malik, J. Knowles, D.W. Hamann, B. International Research Training Group "Visualization of Large and Unstructured Data Sets" University of Kaiserslautern Germany Institute for Data Analysis and Visualization University of California DavisCA United States Life Sciences and Genomics Divisions Lawrence Berkeley National Laboratory CA United States Computer Science Division University of California BerkeleyCA United States

ISBN: (纸本)3905673312

To allow a more rigorous understanding of animal gene regulatory networks, the Berkeley Drosophila Transcription Network Project (BDTNP) has developed a suite of methods that support quantitative, computational analysis of three-dimensional (3D) gene expression patterns with cellular resolution in early Drosophila embryos. Here we report the first components of a visualization tool, PointCloudXplore, that allows the relationships between different gene's expression to be analyzed using the BDTNP's datasets. PointCloudXplore uses the established visualization techniques of multiple views, brushing, and linking to support the analysis of high-dimensional datasets that describe many genes' expression. Each of the views in PointCloudXplore shows a different gene expression data property. Brushing is used to select and emphasize data associated with defined subsets of embryo cells within a view. Linking is used to show in additional views the expression data for a group of cells that have first been highlighted as a brush in a single view, allowing further data subset properties to be determined. In PointCloudXplore, physical views of the data are linked to parallel coordinates. Physical views show the spatial relationships between different genes' expression patterns within the embryo. Parallel coordinates, on the other hand, show only some features of each gene's expression, but allow simultaneous analysis of data for many more genes than would be possible in a physical view. We have developed several extensions to standard parallel coordinates to facilitate brushing the visualization of 3D gene expression data. © The Eurographics Association 2006.

关键词： visualization

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Segmenting Point Sets

Segmenting Point Sets

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International Conference on Shape Modeling and Applications

作者： I. Yamazaki V. Natarajan Zhaojun Bai B. Hamann Department of Computer Science University of California Davis CA USA Institute for Data Analysis and Visualization (IDAV) University of California Davis CA USA

Extracting features from point sets is becoming increasingly important for purposes like model classification, matching, and exploration. We introduce a technique for segmenting a point-sampled surface into distinct features without explicit construction of a mesh or other surface representation. Our approach achieves computational efficiency through a three-phase segmentation process. The first phase of the process uses a topo-logical approach to define features and coarsens the input, resulting in a set of supernodes, each one representing a collection of input points. A graph cut is employed in the second phase to bisect the set of supernodes. Similarity between supernodes is computed as a weighted combination of geodesic distances and connectivity. Repeated application of the graph cut results in a hierarchical segmentation of the point input. In the last phase, a segmentation of the original point set is constructed by refining the segmentation of the supernodes based on their associated feature sizes. We apply our segmentation algorithm on laser-scanned models to evaluate its ability to capture geometric features in complex data sets

关键词： Shape Feature extraction Spectral analysis data analysis data visualization Computer science data mining Computational efficiency Geophysics computing Laser beam cutting

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