Enhancing the Interactive Visualization of Procedurally Encoded Multifield Data with Ellipsoidal Basis Functions

Jang, Yun; Botchen, Ralf P.; Lauser, Andreas; Ebert, David S.; Gaither, Kelly; Ertl, Thomas

doi:10.1111/j.1467-8659.2006.00978.x

Cited by 30 publications

(19 citation statements)

References 27 publications

(29 reference statements)

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“…The minimization can be obtained by the least-squares normal equations. Then a target coordinate of a new sample in details within the contours can be approximated from Equation (11) or directly from the closed-form solution as follows. For brevity, we drop the argument aa for ssssssss and denote the estimated means and covariances in the following manner: …”

Section: Detail Preserving With Loessmentioning

confidence: 99%

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Animating Characters Using Nonparametric Regression

Chou

Shih

2008

International Symposium on Computer Science and Its Applications

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In this paper, we propose an image deformation technique using nonparametric regression to animate characters in a still image for multimedia applications. The proposed approach effectively produces a sequence of contiguous frames in an animation. It automatically generates deformed shapes by using elliptic radial basis functions (ERBFs) and locally weighted regression (LOESS). ERBFs are used for representing the deformed character's shapes in synthesized frames. For preserving the pattern within a shape, LOESS is applied to fit the detail with local control. Furthermore, the results show the synthesized frames without unnatural distortion.

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Section: Detail Preserving With Loessmentioning

confidence: 99%

“…Nonlinear approximation of functions in general spaces with ERBF networks (referred to as elliptic basis function networks [15]) was proposed. Furthermore, a volumetric approximation system was developed with ellipsoidal Gaussian functions for 3D volumes (referred to as ellipsoidal basis functions [11]). View interpolation.…”

Section: Related Workmentioning

confidence: 99%

Animating Characters Using Nonparametric Regression

Chou

Shih

2008

International Symposium on Computer Science and Its Applications

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“…Jang et al (2004Jang et al ( , 2006, however, presented reconstruction of volumetric functional representations using graphics hardware without resampling on grid structures. Jang et al (2004Jang et al ( , 2006, however, presented reconstruction of volumetric functional representations using graphics hardware without resampling on grid structures.…”

Section: Previous Workmentioning

confidence: 99%

Interactive visualization of quantum-chemistry data

Jang

Varetto

2010

Acta Cryst Sect A

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Simulation and computation in chemistry studies have improved as computational power has increased over recent decades. Many types of chemistry simulation results are available, from atomic level bonding to volumetric representations of electron density. However, tools for the visualization of the results from quantum-chemistry computations are still limited to showing atomic bonds and isosurfaces or isocontours corresponding to certain isovalues. In this work, we study the volumetric representations of the results from quantum-chemistry computations, and evaluate and visualize the representations directly on a modern graphics processing unit without resampling the result in grid structures. Our visualization tool handles the direct evaluation of the approximated wavefunctions described as a combination of Gaussian-like primitive basis functions. For visualizations, we use a slice-based volume-rendering technique with a two-dimensional transfer function, volume clipping and illustrative rendering in order to reveal and enhance the quantum-chemistry structure. Since there is no need to resample the volume from the functional representations for the volume rendering, two issues, data transfer and resampling resolution, can be ignored; therefore, it is possible to explore interactively a large amount of different information in the computation results.

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“…In the last few years, lot of attention has been paid to the problem of object reconstruction from sparse samples [1]. However, there are still very few reconstruction methods for vector fields, which is the fundamental object in classical Physics and Engineering (velocity fields, force fields, etc.).…”

Section: Introductionmentioning

confidence: 99%

Support Vectors Learning for Vector Field Reconstruction

Lage

Castro

Petronetto

et al. 2009

2009 XXII Brazilian Symposium on Computer Graphics and Image Processing

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Figure 1. Reconstruction of a real 3D velocity field captured by a PIV device with sparse, irregular sampling: magnitude (left) and phase (right).Abstract-Sampled vector fields generally appear as measurements of real phenomena. They can be obtained by the use of a Particle Image Velocimetry acquisition device, or as the result of a physical simulation, such as a fluid flow simulation, among many examples. This paper proposes to formulate the unstructured vector field reconstruction and approximation through Machine-Learning. The machine learns from the samples a global vector field estimation function that could be evaluated at arbitrary points from the whole domain. Using an adaptation of the Support Vector Regression method for multi-scale analysis, the proposed method provides a global, analytical expression for the reconstructed vector field through an efficient non-linear optimization. Experiments on artificial and real data show a statistically robust behavior of the proposed technique.

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Enhancing the Interactive Visualization of Procedurally Encoded Multifield Data with Ellipsoidal Basis Functions

Abstract: Functional approximation of scattered data is a popular technique for compactly representing various types of datasets in computer graphics

Cited by 30 publications

References 27 publications

Animating Characters Using Nonparametric Regression

Animating Characters Using Nonparametric Regression

Interactive visualization of quantum-chemistry data

Support Vectors Learning for Vector Field Reconstruction

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