Efficient Visualization of Lagrangian Coherent Structures by Filtered AMR Ridge Extraction October 2007 - IEEE Vis Filip Sadlo, Ronald CGL -

Slides:



Advertisements
Similar presentations
Sauber et al.: Multifield-Graphs Multifield-Graphs: An Approach to Visualizing Correlations in Multifield Scalar Data Natascha Sauber, Holger Theisel,
Advertisements

Visualization Tools for Vorticity Transport Analysis in Incompressible Flow November IEEE Vis Filip Sadlo, Ronald CGL - ETH Zurich Mirjam.
1 IEEE Visualization 2006 Vortex Visualization for Practical Engineering Applications IEEE Visualization 2006 M. Jankun-Kelly, M. Jiang, D. S. Thompson,
Distinctive Image Features from Scale-Invariant Keypoints David Lowe.
Yingcai Xiao Chapter 6 Fundamental Algorithms. Types of Visualization Transformation Types 1.Data (Attribute Transformation) 2.Topology (Topological Transformation)
ICC 2009, Santiago de Chile Visualization of Glacier Surface Movement Samuel Wiesmann Institute of Cartography, ETH Zurich.
Topology-Caching for Dynamic Particle Volume Raycasting Jens Orthmann, Maik Keller and Andreas Kolb, University of Siegen.
Vortex detection in time-dependent flow Ronny Peikert ETH Zurich.
1 Higher Dimensional Vector Field Visualization: A Survey Zhenmin Peng, Robert S. Laramee Department of Computer Science Swansea University, Wales UK
Poisson Surface Reconstruction M Kazhdan, M Bolitho & H Hoppe
Ronny Peikert Over Two Decades of Integration-Based, Geometric Vector Field Visualization Part III: Curve based seeding Planar based.
HMAX Models Architecture Jim Mutch March 31, 2010.
Supervised by Dr. Hau-San WONG Prepared by Kam-fung YU ( )
Activity Recognition Aneeq Zia. Agenda What is activity recognition Typical methods used for action recognition “Evaluation of local spatio-temporal features.
Fast and Extensible Building Modeling from Airborne LiDAR Data Qian-Yi Zhou Ulrich Neumann University of Southern California.
Instructor: Mircea Nicolescu Lecture 13 CS 485 / 685 Computer Vision.
Robust Moving Object Detection & Categorization using self- improving classifiers Omar Javed, Saad Ali & Mubarak Shah.
Light Field Compression Using 2-D Warping and Block Matching Shinjini Kundu Anand Kamat Tarcar EE398A Final Project 1 EE398A - Compression of Light Fields.
lecture 4 : Isosurface Extraction
Trajectory and Invariant Manifold Computation for Flows in the Chesapeake Bay Nathan Brasher February 13, 2005.
Uncertainty and Variability in Point Cloud Surface Data Mark Pauly 1,2, Niloy J. Mitra 1, Leonidas J. Guibas 1 1 Stanford University 2 ETH, Zurich.
Pauly, Keiser, Kobbelt, Gross: Shape Modeling with Point-Sampled GeometrySIGGRAPH 2003 Shape Modeling with Point-Sampled Geometry Mark Pauly Richard Keiser.
Adaptive Sampling And Prediction Dynamical Systems Methods for Adaptive Sampling ASAP Kickoff Meeting June 28, 2004 Shawn C. Shadden (PI: Jerrold Marsden)
Shape Modeling International 2007 – University of Utah, School of Computing Robust Smooth Feature Extraction from Point Clouds Joel Daniels ¹ Linh Ha ¹.
Feature matching and tracking Class 5 Read Section 4.1 of course notes Read Shi and Tomasi’s paper on.
Feature tracking Class 5 Read Section 4.1 of course notes Read Shi and Tomasi’s paper on good features.
Xavier Tricoche Dense Vector Field Representations Texture-based Interactive (GPU) Steady / transient flows Planar / curved geometries Viscous flow past.
Vision-based Control of 3D Facial Animation Jin-xiang Chai Jing Xiao Jessica Hodgins Carnegie Mellon University.
Smart Traveller with Visual Translator for OCR and Face Recognition LYU0203 FYP.
Tensor Field Visualization
Multiple Object Class Detection with a Generative Model K. Mikolajczyk, B. Leibe and B. Schiele Carolina Galleguillos.
ITUppsala universitet Data representation and fundamental algorithms Filip Malmberg
Visualization Research Center University of Stuttgart On the Finite-Time Scope for Computing Lagrangian Coherent Structures from Lyapunov Exponents TopoInVis.
Computer Graphics Group Tobias Weyand Mesh-Based Inverse Kinematics Sumner et al 2005 presented by Tobias Weyand.
Gwangju Institute of Science and Technology Intelligent Design and Graphics Laboratory Multi-scale tensor voting for feature extraction from unstructured.
Computational Geometry The systematic study of algorithms and data structures for geometric objects, with a focus on exact algorithms that are asymptotically.
Lei Zhang and Guoning Chen, Department of Computer Science, University of Houston Robert S. Laramee, Swansea University David Thompson and Adrian Sescu,
Using Transactional Information to Predict Link Strength in Online Social Networks Indika Kahanda and Jennifer Neville Purdue University.
“Study on Parallel SVM Based on MapReduce” Kuei-Ti Lu 03/12/2015.
Contact, Collision and Congestion Nobuyuki Umetani.
CDS 301 Fall, 2009 Vector Visualization Chap. 6 October 7, 2009 Jie Zhang Copyright ©
On-line Space Sculpturing for 3D Shape Manipulation
Time-Dependent Visualization of Lagrangian Coherent Structures by Grid Advection February 2009 – TopoInVis Filip VISUS – Universität Stuttgart,
Visual Attention Accelerated Vehicle Detection in Low-Altitude Airborne Video of Urban Environment Xianbin Cao, Senior Member, IEEE, Renjun Lin, Pingkun.
A Survey on Visualization of Time-Dependent Vector Fields by Texture-based Methods Henry “Dan” Derbes MSIM 842 ODU Main Campus.
FTLE and LCS Pranav Mantini. Contents Introduction Visualization Lagrangian Coherent Structures Finite-Time Lyapunov Exponent Fields Example Future Plan.
The concept and use of Lagrangian Coherent Structures IFTS Intensive Course on Advaned Plasma Physics-Spring 2015 Theory and simulation of nonlinear physics.
Vision and SLAM Ingeniería de Sistemas Integrados Departamento de Tecnología Electrónica Universidad de Málaga (Spain) Acción Integrada –’Visual-based.
- Laboratoire d'InfoRmatique en Image et Systèmes d'information
1 Perception and VR MONT 104S, Fall 2008 Lecture 4 Lightness, Brightness and Edges.
Analysis of Lagrangian Coherent Structures of the Chesapeake Bay Stephanie Young, Kayo Ide,
Data Visualization Fall 2015.
Stephanie Young, Kayo Ide, Atmospheric and Oceanic Science Department
Elementary Mechanics of Fluids Lab # 3 FLOW VISUALIZATION.
CHAPTER 5 CONTOURING. 5.3 CONTOURING Fig 5.7. Relationship between color banding and contouring Contour line (isoline): the same scalar value, or isovalue.
Course14 Dynamic Vision. Biological vision can cope with changing world Moving and changing objects Change illumination Change View-point.
Unconditionally Stable Shock Filters for Image and Geometry Processing
Instructor: Mircea Nicolescu Lecture 5 CS 485 / 685 Computer Vision.
Ronny Peikert 1 Over Two Decades of Integration-Based, Geometric Vector Field Visualization Part III:
Processing Images and Video for An Impressionist Effect Automatic production of “painterly” animations from video clips. Extending existing algorithms.
Shadow Detection in Remotely Sensed Images Based on Self-Adaptive Feature Selection Jiahang Liu, Tao Fang, and Deren Li IEEE TRANSACTIONS ON GEOSCIENCE.
1 Bilinear Classifiers for Visual Recognition Computational Vision Lab. University of California Irvine To be presented in NIPS 2009 Hamed Pirsiavash Deva.
Image-Guided Fracture David Mould University of Saskatchewan.
Surface Mixed Layer Instabilities and Deep Flows
Interactive Offline Tracking for Color Objects
Bag-of-Visual-Words Based Feature Extraction
Real-time Wall Outline Extraction for Redirected Walking
Image Space Based Visualization of Unsteady Flow on Surfaces
Detection of salient points
Elementary Mechanics of Fluids Lab # 3 FLOW VISUALIZATION
Presentation transcript:

Efficient Visualization of Lagrangian Coherent Structures by Filtered AMR Ridge Extraction October IEEE Vis Filip Sadlo, Ronald CGL - ETH Zurich

Efficient Visualization of LCS by filtered AMR Ridge Extraction 2 Lagrangian Coherent Structures (LCS) Vector Field Topology Crit. pts. & streamlines Instantaneous view Fast Lagr. Coherent Structures Ridges in Lyapunov Exponent Transient view Slow (trajectory per point & time) -> Adaptive approach Shadden et al FTLE

Efficient Visualization of LCS by filtered AMR Ridge Extraction 3 Lagrangian Coherent Structures (LCS) Vector Field Topology Crit. pts. & streamlines Instantaneous view Fast Lagr. Coherent Structures Ridges in Lyapunov Exponent Transient view Slow (trajectory per point & time) -> Adaptive approach Shadden et al FTLE

Efficient Visualization of LCS by filtered AMR Ridge Extraction 4 Finite-Time Lyapunov Exponent (FTLE) FTLE: “growth of perturbation after advection time T”

Efficient Visualization of LCS by filtered AMR Ridge Extraction 5 FTLE Computation Advection of particle pairs: tedious Haller 2001: by pre-sampled flow map  Shadden et al t0=t0= FTLE

Efficient Visualization of LCS by filtered AMR Ridge Extraction 6 FTLE Computation Advection of particle pairs: tedious Haller 2001: by pre-sampled flow map  Shadden et al t0=t0= FTLE

Efficient Visualization of LCS by filtered AMR Ridge Extraction 7 FTLE Computation Advection of particle pairs: tedious Haller 2001: by pre-sampled flow map  Shadden et al t0=t0= FTLE

Efficient Visualization of LCS by filtered AMR Ridge Extraction 8 FTLE Computation Advection of particle pairs: tedious Haller 2001: by pre-sampled flow map  Shadden et al t0=t0= FTLE

Efficient Visualization of LCS by filtered AMR Ridge Extraction 9 LCS in Nature Confluences Interfaces Sacramento & Feather Glaciers Moraines Glacier Bay National Park from:

Efficient Visualization of LCS by filtered AMR Ridge Extraction 10 Moraines and LCS “Appearing as dark lines on the surface, moraines indicate how many smaller glaciers feed into the system” -> LCS, dynamical systems from:

Efficient Visualization of LCS by filtered AMR Ridge Extraction 11 Overview Related Work Height Ridges Filtered AMR Ridge Extraction Efficiency FTLE & FSLE Proposed: FTLEM FTLEM & FSLE

Efficient Visualization of LCS by filtered AMR Ridge Extraction 12 Related Work Ridge Extraction –Eberly 1996: Ridges in Image and Data Analysis (nD) –Furst et al. 2001: Marching Ridges (2D) –Sahner et al. 2005: Streamlines in Feature Flow Field (1D) LCS –Hussain 1986: Based on vorticity (3D) –Robinson 1991: Based on correlation (3D) –Haller 2001: Ridges in FTLE, material surfaces (2D) FTLE –Lorenz 1965: Measures predictability –Haller 2001: Based on pre-sampled flow map Path Line Oriented Topology –Theisel et al. 2004: Based on geometry of path lines –Shi et al. 2006: Same for periodic fields

Efficient Visualization of LCS by filtered AMR Ridge Extraction 13 Height Ridges Eberly 1996: –s : scalar field – min : min. eigenvalue of Hessian (s) –  min : eigenvector for min (  min  ridge) –2D height ridge in 3-space:  min   s = 0   min  0  min  min   s = 0, min  0

Efficient Visualization of LCS by filtered AMR Ridge Extraction 14 Furst et al. 2001: Marching Ridges –Orientate  min at nodes of cell by PCA –Evaluate  min   s at nodes –Interpolate zero crossings on edges –Use zero crossings with min  0 –Triangulate crossings –We also filter crossings e.g. by FTLE –We use Marching Cubes instead of triangulation Height Ridges |, | : “  min   s = 0” PC A min  0, min  0

Efficient Visualization of LCS by filtered AMR Ridge Extraction 15 Filtered AMR Ridge Extraction: Motivation Avoid sampling – in regions with no ridges (after filtering) Advantages –if only few ridges are present in given data –if data can be sampled at arbitrary locations –if cost of sampling is high Accuracy –Obtained ridges identical to those from uniform sampling –Rarely small or faint ridges may get missed (see paper)

Efficient Visualization of LCS by filtered AMR Ridge Extraction 16 Filtered AMR Ridge Extraction ridge intersects cell edge Initialization: Ridge-Cell Detection

Efficient Visualization of LCS by filtered AMR Ridge Extraction 17 Filtered AMR Ridge Extraction ridge cell Initialization: Ridge-Cell Detection

Efficient Visualization of LCS by filtered AMR Ridge Extraction 18 Filtered AMR Ridge Extraction ridge cell ridge cell neighbor Iteration 1: Collect for Subdivision

Efficient Visualization of LCS by filtered AMR Ridge Extraction 19 Filtered AMR Ridge Extraction Iteration 1: Subdivision

Efficient Visualization of LCS by filtered AMR Ridge Extraction 20 Filtered AMR Ridge Extraction ridge intersects cell edge Iteration 1: Ridge-Cell Detection

Efficient Visualization of LCS by filtered AMR Ridge Extraction 21 Filtered AMR Ridge Extraction ridge cell Iteration 1: Ridge-Cell Detection

Efficient Visualization of LCS by filtered AMR Ridge Extraction 22 Filtered AMR Ridge Extraction ridge cell ridge cell 2-neighbor Iteration 1: Ridge Growing

Efficient Visualization of LCS by filtered AMR Ridge Extraction 23 Filtered AMR Ridge Extraction ridge cell Iteration 1: Ridge Growing

Efficient Visualization of LCS by filtered AMR Ridge Extraction 24 Filtered AMR Ridge Extraction ridge intersects cell edge ridge cell Iteration 1: Ridge Growing

Efficient Visualization of LCS by filtered AMR Ridge Extraction 25 Filtered AMR Ridge Extraction ridge cell Iteration 1: Ridge Growing

Efficient Visualization of LCS by filtered AMR Ridge Extraction 26 Filtered AMR Ridge Extraction ridge cell neighbor ridge cell Iteration 2: Collect for Subdivision

Efficient Visualization of LCS by filtered AMR Ridge Extraction 27 Filtered AMR Ridge Extraction Iteration 2: Subdivision

Efficient Visualization of LCS by filtered AMR Ridge Extraction 28 Filtered AMR Ridge Extraction ridge intersects cell edge Iteration 2: Ridge-Cell Detection

Efficient Visualization of LCS by filtered AMR Ridge Extraction 29 Filtered AMR Ridge Extraction Iteration 2: Ridge-Cell Detection ridge cell

Efficient Visualization of LCS by filtered AMR Ridge Extraction 30 Filtered AMR Ridge Extraction Iteration 2: Ridge Growing ridge cell ridge cell 2-neighbor

Efficient Visualization of LCS by filtered AMR Ridge Extraction 31 Filtered AMR Ridge Extraction Iteration 2: Ridge Growing ridge cell ridge cell 2-neighbor for  1-level difference

Efficient Visualization of LCS by filtered AMR Ridge Extraction 32 Filtered AMR Ridge Extraction Iteration 2: Ridge Growing ridge cell

Efficient Visualization of LCS by filtered AMR Ridge Extraction 33 Filtered AMR Ridge Extraction Iteration 2: Ridge Growing ridge cell ridge intersects cell edge

Efficient Visualization of LCS by filtered AMR Ridge Extraction 34 Filtered AMR Ridge Extraction Iteration 2: Ridge Growing ridge cell

Efficient Visualization of LCS by filtered AMR Ridge Extraction 35 Filtered AMR Ridge Extraction ridge cell Iteration 3: Collect for Subdivision ridge cell neighbor

Efficient Visualization of LCS by filtered AMR Ridge Extraction 36 Filtered AMR Ridge Extraction... Iteration 3: …

Efficient Visualization of LCS by filtered AMR Ridge Extraction 37 Filtered AMR Ridge Extraction Final Result

Efficient Visualization of LCS by filtered AMR Ridge Extraction 38 Filtered AMR Ridge Extraction from FTLE: Method video

Efficient Visualization of LCS by filtered AMR Ridge Extraction 39 Filtered AMR Ridge Extraction from FTLE: Francis Turbine video

Efficient Visualization of LCS by filtered AMR Ridge Extraction 40 Efficiency directadaptive initial grid3,613,153 nodes1,183 nodes final grid3,613,153 nodes298,964 nodes flow map [s]19, , FTLE [s] ridge extr. [s] , total [s]20, , Subdivision iterations: 4 Speed-up: > 4

Efficient Visualization of LCS by filtered AMR Ridge Extraction 41 Finite-Size Lyapunov Exponent (FSLE), Aurell 1997 FSLE: “time needed to separate by factor s”

Efficient Visualization of LCS by filtered AMR Ridge Extraction 42 FTLE & FSLE (Filtered) FTLE T = 0.1 FSLE Prescribed scale = 1.5 T max = 0.1 FSLE Prescribed scale = 4 T max = 0.1

Efficient Visualization of LCS by filtered AMR Ridge Extraction 43 Proposed: Finite-Time Lyapunov Exponent Maximum (FTLEM) FTLEM: “maximum FTLE over advection time T” …

Efficient Visualization of LCS by filtered AMR Ridge Extraction 44 FTLEM & FSLE (Filtered) FTLEM T max = 0.1 Properties of both FSLE FSLE Prescribed scale = 1.5 T max = 0.1 FSLE Prescribed scale = 4 T max = 0.1

Efficient Visualization of LCS by filtered AMR Ridge Extraction 45 Conclusion Efficient method for ridge extraction Applied to FTLE, FSLE and FTLEM FTLEM as a new FTLE variant Future Work –Exploit temporal coherency

Efficient Visualization of LCS by filtered AMR Ridge Extraction 46 Thanks for your attention

Efficient Visualization of LCS by filtered AMR Ridge Extraction 47 FTLE Ridge Filtering No filtering FTLE min = 3.5, 4.0 & CC min = 1000, 4000 tria