Interactive Glyph Placement for Tensor Fields: Tracking Lines in Higher Order Tensor Fields

Visualization of glyphs has a long history in medical imaging but gains much more power when the glyphs are properly placed to fill the screen. Glyph packing is often performed via an iterative approach to improve the location of glyphs. We present an alternative implementation of glyph packing based on a Delaunay triangulation to speed up the clustering process and reduce costs for neighborhood searches. Our approach does not require a re–computation of acceleration structures when a plane is moved through a volume, which can be done interactively. We provide two methods for initial placement of glyphs to improve the convergence of our algorithm for glyphs larger and glyphs smaller than the data set’s voxel size. The main contribution of this paper is a novel approach to glyph packing that supports simpler parameterization and can be used easily for highly efficient interactive data exploration, in contrast to previous methods

Modeling Streams-based Variants of Ant Colony Optimisation for Parallel Systems

Wei Cheng, Frank Penczek, Clemens Grelck, Raimund Kirner, Bernd Scheuermann, Alex Shafarenko, 'Modeling Streams-based Variants of Ant Colony Optimisation for Parallel Systems' in Proceedings: 2nd HiPEAC Workshop on Feedback-Directed Compiler Optimization for Multi-Core Architectures. Berlin, Germany. 22 January 2013In this paper we present the implementation of a concurrent ant colony optimisation based solver for the combinatorial Single Machine Total Weighted Tardiness Problem (ACO- SMTWTP). We introduce S-Net, a coordination language based on dataflow principles, report on the performance of the implementation and compare it against a sequential and a parallel implementation of the same algorithm in C. As the workload of the optimisation algorithm is highly irregu- lar we consider this application to be an important use-case for runtime measurement directed optimisations of the co- ordination rogram as much as for guiding optimisations of numerical code

Tensor Lines in Tensor Fields of Arbitrary Order: Tracking Lines in Higher Order Tensor Fields

This paper presents a method to reduce time complexity of the computation of higher–order tensor lines. The method can be applied to higher–order tensors and the spherical harmonics representation, both widely used in medical imaging. It is based on a gradient descend technique and integrates well into fiber tracking algorithms. Furthermore, the method improves the angular resolution in contrast to discrete sampling methods which is especially important to tractography, since there, small errors accumulate fast and make the result unusable. Our implementation does not interpolate derived directions but works directly on the interpolated tensor information. The specific contribution of this paper is a fast algorithm for tracking lines tensor fields of arbitrary order that increases angular resolution compared to previous approaches

Fabric-like Visualization of Tensor Field Data on Arbitrary Surfaces in Image Space

Tensors are of great interest to many applications in engineering and in medical imaging, but a proper analysis and visualization remains challenging. It already has been shown that, by employing the metaphor of a fabric structure, tensor data can be visualized precisely on surfaces where the two eigendirections in the plane are illustrated as thread-like structures. This leads to a continuous visualization of most salient features of the tensor data set. We introduce a novel approach to compute such a visualization from tensor field data that is motivated by image-space line integral convolution (LIC). Although our approach can be applied to arbitrary, non-selfintersecting surfaces, the main focus lies on special surfaces following important features, such as surfaces aligned to the neural pathways in the human brain. By adding a postprocessing step, we are able to enhance the visual quality of the of the results, which improves perception of the major patterns

Interactive Glyph Placement for Tensor Fields: Tracking Lines in Higher Order Tensor Fields

Visualization of glyphs has a long history in medical imaging but gains much more power when the glyphs are properly placed to fill the screen. Glyph packing is often performed via an iterative approach to improve the location of glyphs. We present an alternative implementation of glyph packing based on a Delaunay triangulation to speed up the clustering process and reduce costs for neighborhood searches. Our approach does not require a re–computation of acceleration structures when a plane is moved through a volume, which can be done interactively. We provide two methods for initial placement of glyphs to improve the convergence of our algorithm for glyphs larger and glyphs smaller than the data set’s voxel size. The main contribution of this paper is a novel approach to glyph packing that supports simpler parameterization and can be used easily for highly efficient interactive data exploration, in contrast to previous methods

Interactive Glyph Placement for Tensor Fields: Tracking Lines in Higher Order Tensor Fields

Visualization of glyphs has a long history in medical imaging but gains much more power when the glyphs are properly placed to fill the screen. Glyph packing is often performed via an iterative approach to improve the location of glyphs. We present an alternative implementation of glyph packing based on a Delaunay triangulation to speed up the clustering process and reduce costs for neighborhood searches. Our approach does not require a re–computation of acceleration structures when a plane is moved through a volume, which can be done interactively. We provide two methods for initial placement of glyphs to improve the convergence of our algorithm for glyphs larger and glyphs smaller than the data set’s voxel size. The main contribution of this paper is a novel approach to glyph packing that supports simpler parameterization and can be used easily for highly efficient interactive data exploration, in contrast to previous methods