Fundamentals of digital image processing
Fundamentals of digital image processing
Imaging vector fields using line integral convolution
SIGGRAPH '93 Proceedings of the 20th annual conference on Computer graphics and interactive techniques
Signal Processing for Computer Vision
Signal Processing for Computer Vision
Feature Extraction of Separation and Attachment Lines *
IEEE Transactions on Visualization and Computer Graphics
Anisotropic Diffusion in Vector Field Visualization on Euclidean Domains and Surfaces
IEEE Transactions on Visualization and Computer Graphics
Visualizing Vector Field Topology in Fluid Flows
IEEE Computer Graphics and Applications
Interactive feature specification for focus+context visualization of complex simulation data
VISSYM '03 Proceedings of the symposium on Data visualisation 2003
Discrete multiscale vector field decomposition
ACM SIGGRAPH 2003 Papers
Flow Field Clustering via Algebraic Multigrid
VIS '04 Proceedings of the conference on Visualization '04
Clifford Fourier Transform on Vector Fields
IEEE Transactions on Visualization and Computer Graphics
Clifford Convolution And Pattern Matching On Vector Fields
Proceedings of the 14th IEEE Visualization 2003 (VIS'03)
Three-dimensional flow characterization using vector pattern matching
IEEE Transactions on Visualization and Computer Graphics
Analysis and visualization of 3-C PIV images from HART II using image processing methods
EUROVIS'05 Proceedings of the Seventh Joint Eurographics / IEEE VGTC conference on Visualization
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Due to the amount of data nowadays, automatic detection, classification and visualization of features is necessary for a thorough inspection of flow data sets. Pattern matching using vector valued templates has already been applied successfully for the detection of features. In this paper, the approach is extended to automatically compute feature based segmentations of flow data sets. Different problems of the segmentation like the influence of thresholds, overlapping features, and classification errors are discussed. Visualizations of the segmentation display important structures of the flow and highlight the interesting features. The segmentation algorithm presented in this paper is applicable to 2D and 3D vector fields as well as to time-dependent data.