Interactive Visual Exploration of Line Clusters

We propose a visualization approach to interactively explore the structure of clusters of lines in 3D space. We introduce cluster consistency fields to indicate the local consistency of the lines in a cluster depending on line density and dispersion of line directions. Via brushing the user can select a focus region where lines are shown, and the consistency fields are used to automatically control the density of displayed lines according to information content. The brush is automatically continued along the gradient of the consistency field towards high information regions, or along a derived mean direction field to reveal major pathways. For a given line clustering, visualizations of cluster hulls are added to preserve context information.

[1]  David Banks,et al.  Image-guided streamline placement , 1996, SIGGRAPH.

[2]  Robert J. Moorhead,et al.  An Advanced Evenly-Spaced Streamline Placement Algorithm , 2006, IEEE Transactions on Visualization and Computer Graphics.

[3]  Kwan-Liu Ma,et al.  View-Dependent Streamlines for 3D Vector Fields , 2010, IEEE Transactions on Visualization and Computer Graphics.

[4]  Jun Ma,et al.  Coherent view-dependent streamline selection for importance-driven flow visualization , 2013, Electronic Imaging.

[5]  Christian Rössl,et al.  Opacity optimization for 3D line fields , 2013, ACM Trans. Graph..

[6]  Rüdiger Westermann,et al.  Line density control in screen-space via balanced line hierarchies , 2016, Comput. Graph..

[7]  Tieniu Tan,et al.  Comparison of Similarity Measures for Trajectory Clustering in Outdoor Surveillance Scenes , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[8]  Xiaoyang Mao,et al.  Image-guided streamline placement on curvilinear grid surfaces , 1998, Proceedings Visualization '98 (Cat. No.98CB36276).

[9]  Tobias Isenberg,et al.  Efficient Structure-Aware Selection Techniques for 3D Point Cloud Visualizations with 2DOF Input , 2012, IEEE Transactions on Visualization and Computer Graphics.

[10]  Daniel F. Keefe,et al.  Force Brushes: Progressive Data-Driven Haptic Selection and Filtering for Multi-Variate Flow Visualizations , 2012, EuroVis.

[11]  Hans Hagen,et al.  IRIS: Illustrative Rendering for Integral Surfaces , 2010, IEEE Transactions on Visualization and Computer Graphics.

[12]  Bernhard Preim,et al.  Ieee Transactions on Visualization and Computer Graphics 1 Blood Flow Clustering and Applications in Virtual Stenting of Intracranial Aneurysms , 2022 .

[13]  David L. Kao,et al.  Strategy for seeding 3D streamlines , 2005, VIS 05. IEEE Visualization, 2005..

[14]  Anna Vilanova,et al.  Evaluation of fiber clustering methods for diffusion tensor imaging , 2005, VIS 05. IEEE Visualization, 2005..

[15]  Jean-Philippe Thiran,et al.  Counting Pedestrians in Video Sequences Using Trajectory Clustering , 2006, IEEE Transactions on Circuits and Systems for Video Technology.

[16]  Wilfrid Lefer,et al.  Creating Evenly-Spaced Streamlines of Arbitrary Density , 1997, Visualization in Scientific Computing.

[17]  Eduard Gröller,et al.  Strategies for interactive exploration of 3D flow using evenly-spaced illuminated streamlines , 2003, SCCG '03.

[18]  Bernhard Preim,et al.  The FLOWLENS: A Focus-and-Context Visualization Approach for Exploration of Blood Flow in Cerebral Aneurysms , 2011, IEEE Transactions on Visualization and Computer Graphics.

[19]  Anton L. Fuhrmann,et al.  Real-time techniques for 3D flow visualization , 1998, Proceedings Visualization '98 (Cat. No.98CB36276).

[20]  Jonathan D. Cohen,et al.  Similarity-Guided Streamline Placement with Error Evaluation , 2007, IEEE Transactions on Visualization and Computer Graphics.

[21]  Roland G. Henry,et al.  Probabilistic streamline q-ball tractography using the residual bootstrap , 2008, NeuroImage.

[22]  Derek K. Jones.  Tractography Gone Wild: Probabilistic Fibre Tracking Using the Wild Bootstrap With Diffusion Tensor MRI , 2008, IEEE Transactions on Medical Imaging.

[23]  Rüdiger Westermann,et al.  Instant visitation maps for interactive visualization of uncertain particle trajectories , 2012, Visualization and Data Analysis.

[24]  F Mut,et al.  Quantifying the Large-Scale Hemodynamics of Intracranial Aneurysms , 2014, American Journal of Neuroradiology.

[25]  Guido Gerig,et al.  Towards a shape model of white matter fiber bundles using diffusion tensor MRI , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[26]  Vivek Verma,et al.  A flow-guided streamline seeding strategy , 2000 .

[27]  Lijie Xu,et al.  An Information-Theoretic Framework for Flow Visualization , 2010, IEEE Transactions on Visualization and Computer Graphics.

[28]  Robert S. Laramee,et al.  Evenly Spaced Streamlines for Surfaces: An Image‐Based Approach , 2009, Comput. Graph. Forum.

[29]  Han-Wei Shen,et al.  Illustrative Streamline Placement and Visualization , 2008, 2008 IEEE Pacific Visualization Symposium.

[30]  Ching-Kuang Shene,et al.  Hierarchical Streamline Bundles , 2012, IEEE Transactions on Visualization and Computer Graphics.

[31]  Anil K. Jain.  Data clustering: 50 years beyond K-means , 2008, Pattern Recognit. Lett..

[32]  Hans Hagen,et al.  Priority Streamlines: A context-based Visualization of Flow Fields , 2007, EuroVis.

[33]  Bernhard Preim,et al.  Explorative Blood Flow Visualization using Dynamic Line Filtering based on Surface Features , 2018, Comput. Graph. Forum.

[34]  Roger Crawfis,et al.  View point evaluation and streamline filtering for flow visualization , 2011, 2011 IEEE Pacific Visualization Symposium.

[35]  Robert S. Laramee,et al.  Similarity Measures for Enhancing Interactive Streamline Seeding , 2013, IEEE Transactions on Visualization and Computer Graphics.

[36]  Hans-Christian Hege,et al.  Dual streamline seeding , 2009, 2009 IEEE Pacific Visualization Symposium.

[37]  Nikolaos Papanikolopoulos,et al.  Clustering of Vehicle Trajectories , 2010, IEEE Transactions on Intelligent Transportation Systems.