Hierarchical Data Structures and Algorithms for Computer Graphics. Part I.
IEEE Computer Graphics and Applications
Digital topology: introduction and survey
Computer Vision, Graphics, and Image Processing
The Quadtree and Related Hierarchical Data Structures
ACM Computing Surveys (CSUR)
Hierarchical Data Structures and Algorithms for Computer Graphics
IEEE Computer Graphics and Applications
IEEE Computer Graphics and Applications
Efficient collision detection of complex deformable models using AABB trees
Journal of Graphics Tools
Morse-smale complexes for piecewise linear 3-manifolds
Proceedings of the nineteenth annual symposium on Computational geometry
On visible surface generation by a priori tree structures
SIGGRAPH '80 Proceedings of the 7th annual conference on Computer graphics and interactive techniques
Iso-Splatting: A Point-Based Alternative to Isosurface Visualization
PG '03 Proceedings of the 11th Pacific Conference on Computer Graphics and Applications
Visualization of Fibrous and Thread-like Data
IEEE Transactions on Visualization and Computer Graphics
Filament tracking and encoding for complex biological networks
Proceedings of the 2008 ACM symposium on Solid and physical modeling
Hi-index | 0.00 |
We present a data structure for the representation of filamentary volumetric data, called the L-block. While the L-block can be used to represent arbitrary volume data sets, it is particularly geared towards representing long, thin, branching structures that prior volumetric representations have difficulty dealing with efficiently. The data structure is designed to allow for easy compression, storage, segmentation, and reconstruction of volumetric data such as scanned neuronal data. By "polymerizing" adjacent connected voxels into connected components, L-block construction facilitates real-time data compression and segmentation, as well as subsequent geometric modeling and visualization of embedded objects within the volume data set. We describe its application in the context of reconstruction of brain microstructure at a neuronal level of detail.