Features Tutorial 7: • Features are parts of a dataset that get Feature Oriented Methods in special attention by the user. Flow • Application dependence! Gerik Scheuermann, Julia Ebling, • User dependence! Helwig Hauser, Raghu Machiraju, • Definition complicated and often unclear! Ronald Peikert, Xavier Tricoche

Flow Features Examples for Vortices I

• Flow features are areas of fluid flow datasets that are of special importance for fluid dynamics!

• Main examples: Vortices (regions of swirling motion), vortex cores, Vortex burst, shock waves, stagnation points/critical points, attachment/separation lines

Collection by Raghu Machiraju

1 Vortices II Vortex with Strong Core

Figures by Ronald Peikert Collection by Raghu Machiraju

Vortex Core Vortex Burst

Figures by Raghu Machiraju Figures by Xavier Tricoche

2 Stagnation Point/ Shock Wave Critical Point

Figures by Hans-Georg Pagendarm

Attachment and Separation Lines Types of Features

• A good classification of features is based on their geometric shape!

• Point Features: Critical points/stagnation points • Line Features: Vortex cores, attachment and separation lines • Surface Features: Shock fronts • Volume or Region Features: Vortex region, region of swirling motion

3 Tutorial Overview II. Automatic Feature Detection I. Introduction (Gerik Scheuermann, University of Leipzig, Germany) • Basics from Fluid Dynamics • Vortex Definitions II. Automatic Feature Detection w (Ronald Peikert, ETH Zürich, Switzerland) • Vortex Detection III. Evaluation of Features (Raghu Machiraju, Ohio State University, USA) • Real World Applications IV. Clifford Convolution • Pros and Cons (Julia Ebling, University of Leipzig, Germany) V. Topological Methods (Xavier Tricoche, University of Utah, USA) VI. Interactive Feature Definition and Detection (Helwig Hauser, VRVis, , Austria)

III. Evaluation of Features Ronald Peikert • Senior Researcher at Institute of • Vortex Definitions Computational Science at ETH • Detection Zürich, Switzerland, since 1999 • PhD in mathematics, ETH Zürich, 1985 • Verification • 1988-1995 Visualization at ETH´s Interdisciplinary Project • Application - characterization Center for Supercomputing • 1996-1998 Leader of Scientific Visualization Group of the FeatureFeature Swiss Center for Scientific Computing RepresentationRepresentation FeatureFeature • Special Interests: Flow Visualization, Feature Extraction DetectionDetection FeatureFeature FeatureFeature Techniques, Industrial Applications of Visualization, MatchingMatching AssociationAssociation Visualization in Virtual Environments FeatureFeature VerificationVerification FeatureFeature TrackingTracking

4 IV. Clifford Convolution Raghu Machiraju • Clifford Algebra • Associate Professor at • Clifford Convolution The Ohio State University since 1999 • PhD in Computer Science, • Clifford Fourier Transform Ohio State University, 1996 • Gabor Filter • Assistant Professor at NSF Engineering Research Center for Computation Field Simulation, Mississippi State University, 1996-1999 • Special Interests: Feature Detection, Large Data Visualization, Signal Processing

V. Topological Methods Julia Ebling • Motivation • Research assistant at University of • Theory basics Leipzig since 2004 • Research assistant at University of • Implementation Kaiserslautern, 2002-2004 • Special interests: flow visualization, image processing, • Applications clifford algebra • Pros and cons

5 VI. Interactive Feature Xavier Tricoche Definition and Detection • Introduction • Postdoc at SCI, University of Utah, • Visualization Example since 2003 • PhD in Computer Science, • Infovis for Flowvis University of Kaiserslautern, 2002 • Interactive Feature • Special Interests: Topological visualization, vector and tensor visualization, time-dependent data, direct volume Extraction rendering

Contact Helwig Hauser

Julia Ebling Helwig Hauser Xavier Tricoche • Scientific director of VRVis Research Computer Science Department VRVis Research Center Scientific Computing and Imaging Institute University of Leipzig TechGate Vienna University of Utah Center, Vienna, since 2000 PO Box 902 Donau-City-Strasse 1 50 S Central Campus Drive Room 3490 • PhD in computer science, TU Wien, 1998 D-04009 Leipzig A-1220 Wien Salt Lake City, UT 84112 Germany Austria USA • Assistant professor at TU Wien, 1998-2000 [email protected] [email protected] [email protected] Gerik Scheuermann • Research interests: volume visualization, flow Raghu Machiraju Computer Science Institute 395 Dreese Labs Ronald Peikert visualization, information visualization, combination of University of Leipzig 2015 Neil Avenue Mall ETH Zentrum, IFW C27.2 PO Box 902 these visualization fields The Ohio State University Haldeneggsteig 4 D-04009 Leipzig Columbus, OH 43210 CH-8092 Zuerich Germany USA Switzerland [email protected] [email protected] [email protected]

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