Digital topology: introduction and survey
Computer Vision, Graphics, and Image Processing
Geometric model extraction from magnetic resonance volume data
Geometric model extraction from magnetic resonance volume data
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
PostScript Language Reference Manual
PostScript Language Reference Manual
Computational Topology for Shape Modeling
SMI '99 Proceedings of the International Conference on Shape Modeling and Applications
ACM SIGGRAPH 2003 Papers
Material Interface Reconstruction
IEEE Transactions on Visualization and Computer Graphics
Silhouette maps for improved texture magnification
Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware
Multilabel Random Walker Image Segmentation Using Prior Models
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Resolution independent curve rendering using programmable graphics hardware
ACM SIGGRAPH 2005 Papers
Efficient magnification of bi-level textures
SIGGRAPH '05 ACM SIGGRAPH 2005 Sketches
MRI tissue classification with neighborhood statistics: a nonparametric, entropy-minimizing approach
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Bixels: picture samples with sharp embedded boundaries
EGSR'04 Proceedings of the Fifteenth Eurographics conference on Rendering Techniques
Segmentation of biological volume datasets using a level-set framework
VG'01 Proceedings of the 2001 Eurographics conference on Volume Graphics
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Topology has been an important tool for analyzing scalar data and flow fields in visualization. In this work, we analyze the topology of multivariate image and volume data sets with discontinuities in order to create an efficient, raster-based representation we call IStar. Specifically, the topology information is used to create a dual structure that contains nodes and connectivity information for every segmentable region in the original data set. This graph structure, along with a sampled representation of the segmented data set, is embedded into a standard raster image which can then be substantially downsampled and compressed. During rendering, the raster image is upsampled and the dual graph is used to reconstruct the original function. Unlike traditional raster approaches, our representation can preserve sharp discontinuities at any level of magnification, much like scalable vector graphics. However, because our representation is raster-based, it is well suited to the real-time rendering pipeline. We demonstrate this by reconstructing our data sets on graphics hardware at real-time rates.