Results 1  10
of
23
The "Parallel Vectors" Operator  A Vector Field Visualization Primitive
"... In this paper we propose an elementary operation on a pair of vector fields as a building block for defining and computing global line–type features of vector or scalar fields. While usual feature definitions often are procedural and therefore implicit, our operator allows precise mathematical defin ..."
Abstract

Cited by 57 (5 self)
 Add to MetaCart
In this paper we propose an elementary operation on a pair of vector fields as a building block for defining and computing global line–type features of vector or scalar fields. While usual feature definitions often are procedural and therefore implicit, our operator allows precise mathematical definitions. It can serve as a basis for comparing feature definitions and for reuse of algorithms and implementations. Applications focus on vortex core methods.
Accelerating 3D convolution using graphics hardware
 In Proceedings of IEEE Visualization ’99
, 1999
"... Many volume filtering operations used for image enhancement, data processing or feature detection can be written in terms of threedimensional convolutions. It is not possible to yield interactive frame rates on todays hardware when applying such convolutions on volume data using software filter rou ..."
Abstract

Cited by 47 (6 self)
 Add to MetaCart
(Show Context)
Many volume filtering operations used for image enhancement, data processing or feature detection can be written in terms of threedimensional convolutions. It is not possible to yield interactive frame rates on todays hardware when applying such convolutions on volume data using software filter routines. As modern graphics workstations have the ability to render twodimensional convoluted images to the frame buffer, this feature can be used to accelerate the process significantly. This way generic 3D convolution can be added as a powerful tool in interactive volume visualization toolkits.
Sizebased Transfer Functions: A New Volume Exploration Technique
 IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
, 2008
"... The visualization of complex 3D images remains a challenge, a fact that is magnified by the difficulty to classify or segment volume data. In this paper, we introduce sizebased transfer functions, which map the local scale of features to color and opacity. Features in a data set with similar or i ..."
Abstract

Cited by 31 (3 self)
 Add to MetaCart
(Show Context)
The visualization of complex 3D images remains a challenge, a fact that is magnified by the difficulty to classify or segment volume data. In this paper, we introduce sizebased transfer functions, which map the local scale of features to color and opacity. Features in a data set with similar or identical scalar values can be classified based on their relative size. We achieve this with the use of scale fields, which are 3D fields that represent the relative size of the local feature at each voxel. We present a mechanism for obtaining these scale fields at interactive rates, through a continuous scalespace analysis and a set of detection filters. Through a number of examples, we show that sizebased transfer functions can improve classification and enhance volume rendering techniques, such as maximum intensity projection. The ability to classify objects based on local size at interactive rates proves to be a powerful method for complex data exploration.
Vortex Tracking in ScaleSpace
, 2002
"... Scalespace techniques have become popular in computer vision for their capabiliv to access the multiscale information inherently contained in images. We show that the field of flow visualization can benefit from these techniques, too, yielding more coherent features and sorting out numerical artifa ..."
Abstract

Cited by 24 (1 self)
 Add to MetaCart
Scalespace techniques have become popular in computer vision for their capabiliv to access the multiscale information inherently contained in images. We show that the field of flow visualization can benefit from these techniques, too, yielding more coherent features and sorting out numerical artifacts as well as irrelevant largescale features. We describe an implementation of scalespace computation using finite elements and show that performance is sufficient for computing a scalespace of timedependent CFD data. Feature tracking,...
Hardware Accelerated Wavelet Transformations
 In Proceedings of EG/IEEE TCVG Symposium on Visualization VisSym ’00
, 2000
"... . Wavelets and related multiscale representations are important means for edge detection and processing as well as for segmentation and registration. Due to the computational complexity of these approaches no interactive visualization of the extraction process is possible nowadays. By using the hard ..."
Abstract

Cited by 22 (3 self)
 Add to MetaCart
(Show Context)
. Wavelets and related multiscale representations are important means for edge detection and processing as well as for segmentation and registration. Due to the computational complexity of these approaches no interactive visualization of the extraction process is possible nowadays. By using the hardware of modern graphics workstations for accelerating wavelet decomposition and reconstruction we realize a first important step for removing lags in the visualization cycle. 1 Introduction Feature extraction has been proven to be a useful utility for segmentation and registration in volume visualization [7, 13]. Many edge detection algorithms used in this step employ wavelets or related basis functions for the internal representation of the volume. Additionally, wavelets can be used for fast volume visualization [5] using the Fourier rendering approach [8, 12]. Wavelet analysis is a mainly memory bound problem. Graphics hardware on the other hand regularly has memory systems that can be ad...
Hardware Based Wavelet Transformations
 Vision, Modeling, and Visualization '99
, 1999
"... Abstract Many filtering and feature extraction algorithms usewavelet or related multiscale representations of volume data for edge detection and processing. Due tothe computational complexity of these approaches no interactive visualization of the extraction process ispossible nowadays. Using the ha ..."
Abstract

Cited by 10 (5 self)
 Add to MetaCart
(Show Context)
Abstract Many filtering and feature extraction algorithms usewavelet or related multiscale representations of volume data for edge detection and processing. Due tothe computational complexity of these approaches no interactive visualization of the extraction process ispossible nowadays. Using the hardware of modern graphics workstations for wavelet decomposition andreconstruction is a first important step for removing lags in the visualization cycle. 1 Introduction Feature extraction has been proven to be a usefulutility for segmentation and registration in volume visualization [6, 14]. Many edge detectionalgorithms used in this step employ wavelets or related basis functions for the internal representation of the volume. Additionally, wavelets can be used for fast volume visualization [4] usingthe Fourier rendering approach [7, 13]. Wavelet decomposition and reconstruction isusually implemented by applying multiple convolution and down / upsampling steps to thevolume data. The convolution steps will not scale with new computer hardware as well aspure computational problems, as they are already mainly memorybound. When using typical tensorproduct wavelets the complete volume data has to be accessed three times for eachwavelet filtering step.
Multiresolution Maximum Intensity Volume Rendering by Morphological Adjunction Pyramids
 In Data Visualization 2001. Proc. Joint Eurographics – IEEE TCVG Symposium on Visualization, May 2830, 2001
, 2001
"... We describe a multiresolution extension to maximum intensity projection (MIP) volume rendering, allowing progressive refinement and perfect reconstruction. The method makes use of morphological adjunction pyramids. The pyramidal analysis and synthesis operators are composed of morphological 3D e ..."
Abstract

Cited by 7 (4 self)
 Add to MetaCart
(Show Context)
We describe a multiresolution extension to maximum intensity projection (MIP) volume rendering, allowing progressive refinement and perfect reconstruction. The method makes use of morphological adjunction pyramids. The pyramidal analysis and synthesis operators are composed of morphological 3D erosion and dilation, combined with dyadic downsampling for analysis and dyadic upsampling for synthesis. In this case the MIP operator can be interchanged with the synthesis operator. This fact is the key to an efficient multiresolution MIP algorithm, because it allows the computation of the maxima along the line of sight on a coarse level, before applying a twodimensional synthesis operator to perform reconstruction of the projection image to a finer level. For interpolation and resampling of volume data, which is required to deal with arbitrary view directions, morphological sampling is used, an interpolation method well adapted to the nonlinear character of MIP. The structure of the resulting multiresolution algorithm is very similar to wavelet splatting, the main differences being that (i) linear summation of voxel values is replaced by maximum computation, and (ii) linear wavelet filters are replaced by (nonlinear) morphological filters.
Emerging Challenges in Computational Topology
 Results of the NFS Workshop on Computational Topology
, 1999
"... Here we present the results of the NSFfunded Workshop on Computational Topology, which met on June 11 and 12 in Miami Beach, Florida. This report identifies important problems involving both computation and topology. Author affiliations: Marshall Bern, Xerox Palo Alto Research Ctr., bern@parc. ..."
Abstract

Cited by 6 (0 self)
 Add to MetaCart
Here we present the results of the NSFfunded Workshop on Computational Topology, which met on June 11 and 12 in Miami Beach, Florida. This report identifies important problems involving both computation and topology. Author affiliations: Marshall Bern, Xerox Palo Alto Research Ctr., bern@parc.xerox.com. David Eppstein, Univ. of California, Irvine, Dept. of Information & Computer Science, eppstein@ics.uci.edu. Pankaj K. Agarwal, Duke Univ., Dept. of Computer Science, pankaj@cs.duke.edu. Nina Amenta, Univ. of Texas, Austin, Dept. of Computer Sciences, amenta@cs.utexas.edu. Paul Chew, Cornell Univ., Dept. of Computer Science, chew@cs.cornell.edu. Tamal Dey, Ohio State Univ., Dept. of Computer and Information Science, tamaldey@cis.ohiostate.edu. David P. Dobkin, Princeton Univ., Dept. of Computer Science, dpd@cs.princeton.edu. Herbert Edelsbrunner, Duke Univ., Dept. of Computer Science, edels@cs.duke.edu. Cindy Grimm, Brown Univ., Dept. of Computer Science, cmg@cs.brown.edu. Leonid...
Morphological Pyramids in Multiresolution MIP Rendering of Large Volume Data: Survey and New Results
 J. Math. Imag. Vision 2005
"... We recently proposed a multiresolution representation for maximum intensity projection (MIP) volume rendering based on morphological adjunction pyramids which allow progressive refinement and have the property of perfect reconstruction. In this algorithm the pyramidal analysis and synthesis operator ..."
Abstract

Cited by 4 (3 self)
 Add to MetaCart
(Show Context)
We recently proposed a multiresolution representation for maximum intensity projection (MIP) volume rendering based on morphological adjunction pyramids which allow progressive refinement and have the property of perfect reconstruction. In this algorithm the pyramidal analysis and synthesis operators are composed of morphological erosion and dilation, combined with dyadic downsampling for analysis and dyadic upsampling for synthesis. Here we introduce an alternative pyramid scheme in which a morphological opening instead of an erosion is used for pyramidal analysis. As a result, the approximation accuracy when rendering from higher levels of the pyramid is improved. Categories and Subject Descriptors (according to ACM CCS): I.3.6 [Computer Graphics]: Interaction techniques. I.4.10 [Image processing and Computer vision]: Image Representation, Hierarchical, Morphological.
Multiresolution and hierarchical methods for the visualization of volume data
 Future Generation Computer Systems
, 1999
"... As threedimensional data sets resulting from simulations or measurements become available at ever growing sizes the need for visualization tools which allow the inspection and the analysis of these data sets at interactive rates is increasing. One way to deal with the complexity is the compression ..."
Abstract

Cited by 4 (0 self)
 Add to MetaCart
(Show Context)
As threedimensional data sets resulting from simulations or measurements become available at ever growing sizes the need for visualization tools which allow the inspection and the analysis of these data sets at interactive rates is increasing. One way to deal with the complexity is the compression of the data in such a way that the number of cells which have to be processed by the visualization mapping is reduced. Since this compression will be lossy, it is up to the user to choose between quality or speed. The decision will usually be made interactively requiring fast access to a complete hierarchy of representations of the data set at various levels of resolution. Two different approaches and visualization algorithms based upon them are presented in this paper: wavelet analysis deriving a hierarchy of coarser representations from the original data set and multilevel finite elements generating successively refined tetrahedral grids from an initially coarse triangulation.