Computer Vision and Measurements in Aerospace Applications
|
|
- Buddy Houston
- 6 years ago
- Views:
Transcription
1 Computer Vision and Measurements in Aerospace Applications ianshu Liu Department of Mechanical and Aeronautical Engineering Western Michigan University Kalamazoo, M 49008
2 Objective o build a unified theoretical framework for quantitative image-based measurements of morphology and motion fields of deformable bodies like fluids Geometric Structures: Points, Curves, Surfaces Motion Fields: Points, Curves, Surfaces (Geometric Flow Comple Continuous Patterns
3 Background Photogrammetry Computer Vision Human Vision Analogy Computer Vision
4 Optical nfo Processing
5 Geometric and Physical-Based Processing Emission Radiance in mages Geometric Features n mages Perspective ransformation Geometrical, Kinematical and Physical Representations in 3D object Space Mapping of Geometry and Motion Mapping of Physical Quantities Emission Radiance in mages Principles of Physics Mathematical Models
6 mportant Quantities in Aerodynamics ( Pressure ( emperature (3 Skin Friction (4 Velocity Field (5 Attitude and Kinematics (6 Shape Molecular Sensors!
7 Needs for New Methods Quantitative, Dynamic, Universal, Applicable to Comple Patterns & Motions of Deformable Bodies Combination of Approaches Perspective Geometry Differential Geometry Continuum Kinematics & Dynamics Radiometry
8 Relevant opics ( Perspective Projection ransformation ( Projective Developable Conical Surface (3 Perspective Projection under Surface Constraint (4 Perspective Projection of Motion Field Constrained on Surface (5 he Correspondence Problem (6 Composite mage Space and Object Space (7 Perspective nvariants of 3D Curve (8 Reflection and Shape Recovery (9 Motion Equations of mage ntensity
9 Perspective Projection ransformation Camera parameters 3 Π (,,, c, c, c,c, p, p,k, K, P, P eterior interior distortion
10 Formulations of Perspective ransformation ( ( c ( ( c p p c 3 c c 3 c m m m m h h h P ( Collinearity Equations ( Homogenous Coordinate Form (3 W-Vector Form 0 ( 0 ( c c W W
11 Geometric mage Measurements 3D Projection D Recover the Lost Dimension
12 Perspective Developable Conical Surface Plane parallel to image 3D curve Ray Equation s c ( P h 3 P t ds 0 C P 0 Conical Surface Equations ( c N ( s 0 D ( c dn ( s / ds 0 D
13 Reconstruction of 3D Curves and Surfaces Using Projective Conical Surfaces 3D Space Curve Surface
14 Reconstruction of 3D Displacement Vectors Providing a rational and general method for Stereoscopic Particle mage Velocimetry (SPV and Scalar mage Velocimetry (SV
15 Motion Field of 3D Space Curve time time St Variational Problem: St U( t min Physical and Geometric Constraints: G i [ U( ] 0 i,,
16 Perspective Projection under Surface Constraint 3 F(, One-to-One Differential Relation d d m3 ( c f S Q d d Geometric Structures ds d g d d Quantities: tangents, normal, length, angle, area topology
17 Motion Field ] ( [ U ] ( [ U ( Q dt d f f f m u S S S c 3 Optical Flow Surface-Constrained Motion Field Motion Field on Surface mage
18 he Point Correspondence Problem Epipolar Line Epipolar Line h( h( mage mage Generalized Longuet-Higgins Relation: ( h( h( Q ( h( h( 0
19 Determining Point Correspondence Four mages or Cameras 3 Si Longuet-Higgins Equations for Si Unknowns 4
20 Composite mage Space and Object Space m ( m 3( m ( Provide additional dimensions One-to-One Relation Reconstruct 3D Displacement Vectors from Composite mage Coordinates
21 Perspective nvariants of 3D Curve Rectifying plane orsion im, im, d d obj, obj, D D new Curvatures Osculating plane Geometrical Flow Problem im, im, d d i i (,',,3 (,,',3 d d 4 obj, obj, Distances d d 43 3 D D 4 D D 43 3 D D (,',,3 (,,',3 (Brill, et al. 99 new
22 Shape and Reflection ( c sys E a a c sys E ls [ d N L s s p( a N N V a L L s V ]
23 Motion Equations of mage ntensity mage ntensity and Radiance (, t c L(,a, g, t Physical parameters: Geometric parameters: p (p, p,, pn q (q,q,,qm Radiance: L(, p, q
24 Generic Motion Equations of mage ntensity t u c sys L t U L dp dt p L dq dt q L Optical Flow Velocity Field Physical parameters: Geometric parameters: p (p, p,, pn q (q,q,,qm Radiance: L(, p, q
25 Emitting Passive Scalar ransport Luminescence Governing Equation Radiance d dt U t D L(,t c (,t Perspective Projection onto mage Plane
26 Motion Equation of mage ntensity for Emitting Passive Scalar ransport h h D u t ] ( [ U ] ( [ U ( dt d f f f m G u S S S c 3 Optical Flow and Velocity Field on Surface
27 Light ransmitting Scalar ransport Governing Equation d dt U t D L L0 ep( 0 s et ds Perspective Projection onto mage Plane
28 D u t u U Optical Flow and Path-Averaged Velocity Motion Equation of mage ntensity for Light ransmitting Scalar ransport 3 3 d d U U where
29 Schlieren mage of Density-Varying Flows mage ntensity and Density Gradient K Cschl K d Motion Equations of mage ntensity 3 t ( d [ K U 0 0 ( K d ] 0 where U U d d 3 3 ( /, /
30 Shadowgraph mage of Density-Varying Flows mage ntensity and Second-Order Density Derivative Cshad d 3 Motion Equations of mage ntensity t ( [ U ( ] 0 where solution (
31 ransmittance mage of Density-Varying Flows mage ntensity and Density C trans d 3 Motion Equations of mage ntensity t ( [ U ( ] 0
32 ypical Applications Aerodynamic measurements: pressure and temperature sensitive paints, videogrammetric attitude measurement, stereoscopic PV, lasertagging technique, schlieren, shadow and transmittance imaging, oil-film/liquid-crystal skin friction measurements. Metrology and kinematics of large inflatable space structure.
33 Unification of Measurement Systems Conventional echniques mage-based echniques Force Balances Accelerometers PSP & SP Videogrammetry Hot-Wires & Films Pressure aps & Probes Global Velocimetries (PV, DGV Schlieren/Shadowgraph emperature & Heat ransfer Gauges Oil & LC -Film Skin Friction Meters
34 Data Fusion and Understanding Measurements Data Base & Models ntegrated data & data at discrete locations Aerodynamics data base Distributions on surfaces heoretical models CFD Fields in 3D space SuperAerodynamicist : ntelligent Epert System!?
35 Conclusions We will see unified image-based instrumentation providing non-contact, global measurements of important physical, geometric and dynamical quantities in wind tunnel testing. deal Aerodynamics Lab Unified Measurement echniques Wind unnel esting CFD heories Aerospace System Design
Flow Structures Extracted from Visualization Images: Vector Fields and Topology
Flow Structures Extracted from Visualization Images: Vector Fields and Topology Tianshu Liu Department of Mechanical & Aerospace Engineering Western Michigan University, Kalamazoo, MI 49008, USA We live
More informationVideogrammetric Technique for Aerospace Applications: From Model Attitude and Deformation to Surface Geometry
Videogrammetric Technique for Aerospace Applications: From Model Attitude and Deformation to Surface Geometry Tianshu Liu Department of Mechanical and Aerospace Engineering Western Michigan University,
More informationExtraction of Skin Friction Fields from Surface Flow Visualizations as an Inverse Problem
Extraction of Skin Friction Fields from Surface Flow Visualizations as an Inverse Problem Tianshu Liu Department of Mechanical & Aerospace Engineering Western Michigan University, Kalamazoo, MI 49008 Objective
More informationStructure from Motion. Prof. Marco Marcon
Structure from Motion Prof. Marco Marcon Summing-up 2 Stereo is the most powerful clue for determining the structure of a scene Another important clue is the relative motion between the scene and (mono)
More informationComparison between Optical Flow and Cross-Correlation Methods for Extraction of Velocity Fields from Particle Images
Comparison between Optical Flow and Cross-Correlation Methods for Extraction of Velocity Fields from Particle Images (Optical Flow vs Cross-Correlation) Tianshu Liu, Ali Merat, M. H. M. Makhmalbaf Claudia
More informationVector Field Visualization: Introduction
Vector Field Visualization: Introduction What is a Vector Field? A simple 2D steady vector field A vector valued function that assigns a vector (with direction and magnitude) to any given point. It typically
More informationExterior Orientation Parameters
Exterior Orientation Parameters PERS 12/2001 pp 1321-1332 Karsten Jacobsen, Institute for Photogrammetry and GeoInformation, University of Hannover, Germany The georeference of any photogrammetric product
More informationcalibrated coordinates Linear transformation pixel coordinates
1 calibrated coordinates Linear transformation pixel coordinates 2 Calibration with a rig Uncalibrated epipolar geometry Ambiguities in image formation Stratified reconstruction Autocalibration with partial
More informationIndustrial applications of image based measurement techniques in aerodynamics: problems, progress and future needs
Industrial applications of image based measurement techniques in aerodynamics: problems, progress and future needs Jürgen Kompenhans 1 Department Experimental Methods, Institute of Aerodynamics and Flow
More informationNoncontact measurements of optical inhomogeneity stratified media parameters by location of laser radiation caustics
Noncontact measurements of optical inhomogeneity stratified media parameters by location of laser radiation caustics Anastasia V. Vedyashkina *, Bronyus S. Rinkevichyus, Irina L. Raskovskaya V.A. Fabrikant
More informationChapters 1 7: Overview
Chapters 1 7: Overview Chapter 1: Introduction Chapters 2 4: Data acquisition Chapters 5 7: Data manipulation Chapter 5: Vertical imagery Chapter 6: Image coordinate measurements and refinements Chapter
More informationCurve Matching and Stereo Calibration
Curve Matching and Stereo Calibration John Porrill & Stephen Pollard The topological obstacles to the matching of smooth curves in stereo images are shown to occur at epipolar tangencies. Such points are
More informationShape optimisation using breakthrough technologies
Shape optimisation using breakthrough technologies Compiled by Mike Slack Ansys Technical Services 2010 ANSYS, Inc. All rights reserved. 1 ANSYS, Inc. Proprietary Introduction Shape optimisation technologies
More informationIntroduction to Computer Vision. Week 8, Fall 2010 Instructor: Prof. Ko Nishino
Introduction to Computer Vision Week 8, Fall 2010 Instructor: Prof. Ko Nishino Midterm Project 2 without radial distortion correction with radial distortion correction Light Light Light! How do you recover
More informationLecture # 16: Review for Final Exam
AerE 344 Lecture Notes Lecture # 6: Review for Final Exam Hui Hu Department of Aerospace Engineering, Iowa State University Ames, Iowa 5, U.S.A AerE343L: Dimensional Analysis and Similitude Commonly used
More informationMeasurements in Fluid Mechanics
Measurements in Fluid Mechanics 13.1 Introduction The purpose of this chapter is to provide the reader with a basic introduction to the concepts and techniques applied by engineers who measure flow parameters
More informationTwo-View Geometry (Course 23, Lecture D)
Two-View Geometry (Course 23, Lecture D) Jana Kosecka Department of Computer Science George Mason University http://www.cs.gmu.edu/~kosecka General Formulation Given two views of the scene recover the
More informationVector Field Visualization: Introduction
Vector Field Visualization: Introduction What is a Vector Field? Why It is Important? Vector Fields in Engineering and Science Automotive design [Chen et al. TVCG07,TVCG08] Weather study [Bhatia and Chen
More informationFeature Transfer and Matching in Disparate Stereo Views through the use of Plane Homographies
Feature Transfer and Matching in Disparate Stereo Views through the use of Plane Homographies M. Lourakis, S. Tzurbakis, A. Argyros, S. Orphanoudakis Computer Vision and Robotics Lab (CVRL) Institute of
More informationSupport for Multi physics in Chrono
Support for Multi physics in Chrono The Story Ahead Overview of multi physics strategy in Chrono Summary of handling rigid/flexible body dynamics using Lagrangian approach Summary of handling fluid, and
More informationAutomotive Testing: Optical 3D Metrology Improves Safety and Comfort
Automotive Testing: Optical 3D Metrology Improves Safety and Comfort GOM Measuring System: ARAMIS, TRITOP, GOM Touch Probe Keywords: Automotive, Crash Testing, Static and Dynamic Deformation, Simulation
More informationModeling & Simulation of Supersonic Flow Using McCormack s Technique
Modeling & Simulation of Supersonic Flow Using McCormack s Technique M. Saif Ullah Khalid*, Afzaal M. Malik** Abstract In this work, two-dimensional inviscid supersonic flow around a wedge has been investigated
More informationCOMPUTER AND ROBOT VISION
VOLUME COMPUTER AND ROBOT VISION Robert M. Haralick University of Washington Linda G. Shapiro University of Washington T V ADDISON-WESLEY PUBLISHING COMPANY Reading, Massachusetts Menlo Park, California
More informationRange Sensors (time of flight) (1)
Range Sensors (time of flight) (1) Large range distance measurement -> called range sensors Range information: key element for localization and environment modeling Ultrasonic sensors, infra-red sensors
More informationSPC 307 Aerodynamics. Lecture 1. February 10, 2018
SPC 307 Aerodynamics Lecture 1 February 10, 2018 Sep. 18, 2016 1 Course Materials drahmednagib.com 2 COURSE OUTLINE Introduction to Aerodynamics Review on the Fundamentals of Fluid Mechanics Euler and
More informationStudies of the Continuous and Discrete Adjoint Approaches to Viscous Automatic Aerodynamic Shape Optimization
Studies of the Continuous and Discrete Adjoint Approaches to Viscous Automatic Aerodynamic Shape Optimization Siva Nadarajah Antony Jameson Stanford University 15th AIAA Computational Fluid Dynamics Conference
More informationAnnouncements. Motion. Structure-from-Motion (SFM) Motion. Discrete Motion: Some Counting
Announcements Motion Introduction to Computer Vision CSE 152 Lecture 20 HW 4 due Friday at Midnight Final Exam: Tuesday, 6/12 at 8:00AM-11:00AM, regular classroom Extra Office Hours: Monday 6/11 9:00AM-10:00AM
More informationCamera model and multiple view geometry
Chapter Camera model and multiple view geometry Before discussing how D information can be obtained from images it is important to know how images are formed First the camera model is introduced and then
More informationMassachusetts Institute of Technology Department of Computer Science and Electrical Engineering 6.801/6.866 Machine Vision QUIZ II
Massachusetts Institute of Technology Department of Computer Science and Electrical Engineering 6.801/6.866 Machine Vision QUIZ II Handed out: 001 Nov. 30th Due on: 001 Dec. 10th Problem 1: (a (b Interior
More informationKeywords: industrial photogrammetry, quality control, small aircraft
INVESTIGATING OFF-LINE LOW COST PHOTOGRAMMETRY APPLIED TO SMALL AIRCRAFT QULAITY CONTROL Dr M. Varshosaz, A. Amini Department of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering,
More informationCamera Calibration. Schedule. Jesus J Caban. Note: You have until next Monday to let me know. ! Today:! Camera calibration
Camera Calibration Jesus J Caban Schedule! Today:! Camera calibration! Wednesday:! Lecture: Motion & Optical Flow! Monday:! Lecture: Medical Imaging! Final presentations:! Nov 29 th : W. Griffin! Dec 1
More informationFlow Field of Truncated Spherical Turrets
Flow Field of Truncated Spherical Turrets Kevin M. Albarado 1 and Amelia Williams 2 Aerospace Engineering, Auburn University, Auburn, AL, 36849 Truncated spherical turrets are used to house cameras and
More informationAnnouncements. Motion. Structure-from-Motion (SFM) Motion. Discrete Motion: Some Counting
Announcements Motion HW 4 due Friday Final Exam: Tuesday, 6/7 at 8:00-11:00 Fill out your CAPES Introduction to Computer Vision CSE 152 Lecture 20 Motion Some problems of motion 1. Correspondence: Where
More informationMA 323 Geometric Modelling Course Notes: Day 21 Three Dimensional Bezier Curves, Projections and Rational Bezier Curves
MA 323 Geometric Modelling Course Notes: Day 21 Three Dimensional Bezier Curves, Projections and Rational Bezier Curves David L. Finn Over the next few days, we will be looking at extensions of Bezier
More informationHuman beings are extremely interested in the observation of nature, as this was and still is of utmost importance for their survival.
Historical Background Human beings are extremely interested in the observation of nature, as this was and still is of utmost importance for their survival. (www.copyright-free-images.com) 1 Historical
More informationEstimation of 3D Geometry Using Multi-View and Structured Circular Light System
Journal of Image and Graphics, Volume, No., June, 04 Estimation of 3D Geometry Using Multi-View and Structured Circular Light System Deokwoo Lee Samsung Electronics / Camera R&D Lab, Division of Mobile
More informationComputer Vision Lecture 17
Computer Vision Lecture 17 Epipolar Geometry & Stereo Basics 13.01.2015 Bastian Leibe RWTH Aachen http://www.vision.rwth-aachen.de leibe@vision.rwth-aachen.de Announcements Seminar in the summer semester
More informationweighted minimal surface model for surface reconstruction from scattered points, curves, and/or pieces of surfaces.
weighted minimal surface model for surface reconstruction from scattered points, curves, and/or pieces of surfaces. joint work with (S. Osher, R. Fedkiw and M. Kang) Desired properties for surface reconstruction:
More informationMAE 3130: Fluid Mechanics Lecture 5: Fluid Kinematics Spring Dr. Jason Roney Mechanical and Aerospace Engineering
MAE 3130: Fluid Mechanics Lecture 5: Fluid Kinematics Spring 2003 Dr. Jason Roney Mechanical and Aerospace Engineering Outline Introduction Velocity Field Acceleration Field Control Volume and System Representation
More information04 - Normal Estimation, Curves
04 - Normal Estimation, Curves Acknowledgements: Olga Sorkine-Hornung Normal Estimation Implicit Surface Reconstruction Implicit function from point clouds Need consistently oriented normals < 0 0 > 0
More informationComputer Vision Lecture 17
Announcements Computer Vision Lecture 17 Epipolar Geometry & Stereo Basics Seminar in the summer semester Current Topics in Computer Vision and Machine Learning Block seminar, presentations in 1 st week
More informationProjective geometry for Computer Vision
Department of Computer Science and Engineering IIT Delhi NIT, Rourkela March 27, 2010 Overview Pin-hole camera Why projective geometry? Reconstruction Computer vision geometry: main problems Correspondence
More informationThe Level Set Method. Lecture Notes, MIT J / 2.097J / 6.339J Numerical Methods for Partial Differential Equations
The Level Set Method Lecture Notes, MIT 16.920J / 2.097J / 6.339J Numerical Methods for Partial Differential Equations Per-Olof Persson persson@mit.edu March 7, 2005 1 Evolving Curves and Surfaces Evolving
More informationAutomated calculation report (example) Date 05/01/2018 Simulation type
Automated calculation report (example) Project name Tesla Semi Date 05/01/2018 Simulation type Moving Table of content Contents Table of content... 2 Introduction... 3 Project details... 3 Disclaimer...
More information1 Mathematical Concepts
1 Mathematical Concepts Mathematics is the language of geophysical fluid dynamics. Thus, in order to interpret and communicate the motions of the atmosphere and oceans. While a thorough discussion of the
More informationLecture 1.1 Introduction to Fluid Dynamics
Lecture 1.1 Introduction to Fluid Dynamics 1 Introduction A thorough study of the laws of fluid mechanics is necessary to understand the fluid motion within the turbomachinery components. In this introductory
More informationMethod of Finite Elements I
Institute of Structural Engineering Page 1 Held by Prof. Dr. E. Chatzi, Dr. P. Steffen Assistants: Adrian Egger (HIL E 13.3), Harry Mylonas (HIL H33.1), Konstantinos Tatsis (HIL H33.1) Lectures homepage:
More informationLab 6 - Ocean Acoustic Environment
Lab 6 - Ocean Acoustic Environment 2.680 Unmanned Marine Vehicle Autonomy, Sensing and Communications Feb 26th 2019 Henrik Schmidt, henrik@mit.edu Michael Benjamin, mikerb@mit.edu Department of Mechanical
More information1 Projective Geometry
CIS8, Machine Perception Review Problem - SPRING 26 Instructions. All coordinate systems are right handed. Projective Geometry Figure : Facade rectification. I took an image of a rectangular object, and
More informationReminder: Lecture 20: The Eight-Point Algorithm. Essential/Fundamental Matrix. E/F Matrix Summary. Computing F. Computing F from Point Matches
Reminder: Lecture 20: The Eight-Point Algorithm F = -0.00310695-0.0025646 2.96584-0.028094-0.00771621 56.3813 13.1905-29.2007-9999.79 Readings T&V 7.3 and 7.4 Essential/Fundamental Matrix E/F Matrix Summary
More information10/5/09 1. d = 2. Range Sensors (time of flight) (2) Ultrasonic Sensor (time of flight, sound) (1) Ultrasonic Sensor (time of flight, sound) (2) 4.1.
Range Sensors (time of flight) (1) Range Sensors (time of flight) (2) arge range distance measurement -> called range sensors Range information: key element for localization and environment modeling Ultrasonic
More informationMeasurement of a laser-induced underwater shock wave by the optical-flow-based background-oriented schlieren technique
Measurement of a laser-induced underwater shock wave by the optical-flow-based background-oriented schlieren technique K. Hayasaka 1, Y. Tagawa 1*, T. Liu 2, M. Kameda 1 1: Dept. of Mechanical Systems
More informationMeasuring Light: Radiometry and Cameras
Lecture 11: Measuring Light: Radiometry and Cameras Computer Graphics CMU 15-462/15-662, Fall 2015 Slides credit: a majority of these slides were created by Matt Pharr and Pat Hanrahan Simulating a pinhole
More informationIntroduction to Computer Vision. Introduction CMPSCI 591A/691A CMPSCI 570/670. Image Formation
Introduction CMPSCI 591A/691A CMPSCI 570/670 Image Formation Lecture Outline Light and Optics Pinhole camera model Perspective projection Thin lens model Fundamental equation Distortion: spherical & chromatic
More informationVisual Odometry. Features, Tracking, Essential Matrix, and RANSAC. Stephan Weiss Computer Vision Group NASA-JPL / CalTech
Visual Odometry Features, Tracking, Essential Matrix, and RANSAC Stephan Weiss Computer Vision Group NASA-JPL / CalTech Stephan.Weiss@ieee.org (c) 2013. Government sponsorship acknowledged. Outline The
More informationCapturing, Modeling, Rendering 3D Structures
Computer Vision Approach Capturing, Modeling, Rendering 3D Structures Calculate pixel correspondences and extract geometry Not robust Difficult to acquire illumination effects, e.g. specular highlights
More informationOpenOpticalFlow: An Open Source Program for Extraction of. Velocity Fields from Flow Visualization Images. Tianshu Liu
OpenOpticalFlow: An Open Source Program for Extraction of Velocity Fields from Flow Visualization Images Tianshu Liu Department of Mechanical and Aerospace Engineering Western Michigan University, Kalamazoo,
More informationVision Review: Image Formation. Course web page:
Vision Review: Image Formation Course web page: www.cis.udel.edu/~cer/arv September 10, 2002 Announcements Lecture on Thursday will be about Matlab; next Tuesday will be Image Processing The dates some
More informationSTRUCTURE AND MOTION ESTIMATION FROM DYNAMIC SILHOUETTES UNDER PERSPECTIVE PROJECTION *
STRUCTURE AND MOTION ESTIMATION FROM DYNAMIC SILHOUETTES UNDER PERSPECTIVE PROJECTION * Tanuja Joshi Narendra Ahuja Jean Ponce Beckman Institute, University of Illinois, Urbana, Illinois 61801 Abstract:
More informationBut First: Multi-View Projective Geometry
View Morphing (Seitz & Dyer, SIGGRAPH 96) Virtual Camera Photograph Morphed View View interpolation (ala McMillan) but no depth no camera information Photograph But First: Multi-View Projective Geometry
More informationPRACE Workshop, Worksheet 2
PRACE Workshop, Worksheet 2 Stockholm, December 3, 2013. 0 Download files http://csc.kth.se/ rvda/prace files ws2.tar.gz. 1 Introduction In this exercise, you will have the opportunity to work with a real
More informationVolume Illumination & Vector Field Visualisation
Volume Illumination & Vector Field Visualisation Visualisation Lecture 11 Institute for Perception, Action & Behaviour School of Informatics Volume Illumination & Vector Vis. 1 Previously : Volume Rendering
More informationLaser speckle based background oriented schlieren measurements in a fire backlayering front
Laser speckle based background oriented schlieren measurements in a fire backlayering front Philipp Bühlmann 1*, Alexander H. Meier 1, Martin Ehrensperger 1, Thomas Rösgen 1 1: ETH Zürich, Institute of
More information1.2 Numerical Solutions of Flow Problems
1.2 Numerical Solutions of Flow Problems DIFFERENTIAL EQUATIONS OF MOTION FOR A SIMPLIFIED FLOW PROBLEM Continuity equation for incompressible flow: 0 Momentum (Navier-Stokes) equations for a Newtonian
More informationPhotogrammetry: DTM Extraction & Editing
Photogrammetry: DTM Extraction & Editing Review of terms Vertical aerial photograph Perspective center Exposure station Fiducial marks Principle point Air base (Exposure Station) Digital Photogrammetry:
More informationLecture 6 Stereo Systems Multi-view geometry
Lecture 6 Stereo Systems Multi-view geometry Professor Silvio Savarese Computational Vision and Geometry Lab Silvio Savarese Lecture 6-5-Feb-4 Lecture 6 Stereo Systems Multi-view geometry Stereo systems
More information3. The three points (2, 4, 1), (1, 2, 2) and (5, 2, 2) determine a plane. Which of the following points is in that plane?
Math 4 Practice Problems for Midterm. A unit vector that is perpendicular to both V =, 3, and W = 4,, is (a) V W (b) V W (c) 5 6 V W (d) 3 6 V W (e) 7 6 V W. In three dimensions, the graph of the equation
More informationA brief introduction to fluidstructure. O. Souček
A brief introduction to fluidstructure interactions O. Souček Fluid-structure interactions Important class computational models Civil engineering Biomechanics Industry Geophysics From http://www.ihs.uni-stuttgart.de
More informationMeasurement Techniques. Digital Particle Image Velocimetry
Measurement Techniques Digital Particle Image Velocimetry Heat and Mass Transfer Laboratory (LTCM) Sepideh Khodaparast Marco Milan Navid Borhani 1 Content m Introduction m Particle Image Velocimetry features
More informationMATH 31A HOMEWORK 9 (DUE 12/6) PARTS (A) AND (B) SECTION 5.4. f(x) = x + 1 x 2 + 9, F (7) = 0
FROM ROGAWSKI S CALCULUS (2ND ED.) SECTION 5.4 18.) Express the antiderivative F (x) of f(x) satisfying the given initial condition as an integral. f(x) = x + 1 x 2 + 9, F (7) = 28.) Find G (1), where
More informationCh 22 Inspection Technologies
Ch 22 Inspection Technologies Sections: 1. Inspection Metrology 2. Contact vs. Noncontact Inspection Techniques 3. Conventional Measuring and Gaging Techniques 4. Coordinate Measuring Machines 5. Surface
More informationMore Animation Techniques
CS 231 More Animation Techniques So much more Animation Procedural animation Particle systems Free-form deformation Natural Phenomena 1 Procedural Animation Rule based animation that changes/evolves over
More informationPhotometric Stereo. Lighting and Photometric Stereo. Computer Vision I. Last lecture in a nutshell BRDF. CSE252A Lecture 7
Lighting and Photometric Stereo Photometric Stereo HW will be on web later today CSE5A Lecture 7 Radiometry of thin lenses δa Last lecture in a nutshell δa δa'cosα δacos β δω = = ( z' / cosα ) ( z / cosα
More informationLab #5 Ocean Acoustic Environment
Lab #5 Ocean Acoustic Environment 2.S998 Unmanned Marine Vehicle Autonomy, Sensing and Communications Contents 1 The ocean acoustic environment 3 1.1 Ocean Acoustic Waveguide................................
More informationLecture 9: Epipolar Geometry
Lecture 9: Epipolar Geometry Professor Fei Fei Li Stanford Vision Lab 1 What we will learn today? Why is stereo useful? Epipolar constraints Essential and fundamental matrix Estimating F (Problem Set 2
More informationFlow Visualization around Generic Bridge Shapes using Particle Image Velocimetry
Flow Visualization around Generic Bridge Shapes using Particle Image Velocimetry by Harold Bosch 1 and Kornel Kerenyi 2 ABSTRACT This paper examines the flow field around generic bridge shape models using
More informationProjective 2D Geometry
Projective D Geometry Multi View Geometry (Spring '08) Projective D Geometry Prof. Kyoung Mu Lee SoEECS, Seoul National University Homogeneous representation of lines and points Projective D Geometry Line
More informationThree-dimensional nondestructive evaluation of cylindrical objects (pipe) using an infrared camera coupled to a 3D scanner
Three-dimensional nondestructive evaluation of cylindrical objects (pipe) using an infrared camera coupled to a 3D scanner F. B. Djupkep Dizeu, S. Hesabi, D. Laurendeau, A. Bendada Computer Vision and
More informationMETR Robotics Tutorial 2 Week 2: Homogeneous Coordinates
METR4202 -- Robotics Tutorial 2 Week 2: Homogeneous Coordinates The objective of this tutorial is to explore homogenous transformations. The MATLAB robotics toolbox developed by Peter Corke might be a
More informationLevel Set Method in a Finite Element Setting
Level Set Method in a Finite Element Setting John Shopple University of California, San Diego November 6, 2007 Outline 1 Level Set Method 2 Solute-Solvent Model 3 Reinitialization 4 Conclusion Types of
More informationMorphing high lift structures: Smart leading edge device and smart single slotted flap Hans Peter Monner, Johannes Riemenschneider Madrid, 30 th
Morphing high lift structures: Smart leading edge device and smart single slotted flap Hans Peter Monner, Johannes Riemenschneider Madrid, 30 th March 2011 Outline Background Project overview Selected
More informationIntegration of 3D Stereo Vision Measurements in Industrial Robot Applications
Integration of 3D Stereo Vision Measurements in Industrial Robot Applications Frank Cheng and Xiaoting Chen Central Michigan University cheng1fs@cmich.edu Paper 34, ENG 102 Abstract Three dimensional (3D)
More informationAnalysis of fluid-solid coupling vibration characteristics of probe based on ANSYS Workbench
Analysis of fluid-solid coupling vibration characteristics of probe based on ANSYS Workbench He Wang 1, a, Changzheng Zhao 1, b and Hongzhi Chen 1, c 1 Shandong University of Science and Technology, Qingdao
More informationGeometry of Multiple views
1 Geometry of Multiple views CS 554 Computer Vision Pinar Duygulu Bilkent University 2 Multiple views Despite the wealth of information contained in a a photograph, the depth of a scene point along the
More information(LSS Erlangen, Simon Bogner, Ulrich Rüde, Thomas Pohl, Nils Thürey in collaboration with many more
Parallel Free-Surface Extension of the Lattice-Boltzmann Method A Lattice-Boltzmann Approach for Simulation of Two-Phase Flows Stefan Donath (LSS Erlangen, stefan.donath@informatik.uni-erlangen.de) Simon
More informationSimWise. 3D Dynamic Motion, and Stress Analysis. integrated with Alibre Design
SimWise 3D Dynamic Motion, and Stress Analysis integrated with Alibre Design SimWise 4D for Alibre Integrated Motion Simulation and Stress Analysis SimWise 4D is a software tool that allows the functional
More informationLecture VI: Constraints and Controllers
Lecture VI: Constraints and Controllers Motion Constraints In practice, no rigid body is free to move around on its own. Movement is constrained: wheels on a chair human body parts trigger of a gun opening
More informationChapter 1 - Basic Equations
2.20 Marine Hydrodynamics, Fall 2017 Lecture 2 Copyright c 2017 MIT - Department of Mechanical Engineering, All rights reserved. 2.20 Marine Hydrodynamics Lecture 2 Chapter 1 - Basic Equations 1.1 Description
More informationMeshing of flow and heat transfer problems
Meshing of flow and heat transfer problems Luyao Zou a, Zhe Li b, Qiqi Fu c and Lujie Sun d School of, Shandong University of science and technology, Shandong 266590, China. a zouluyaoxf@163.com, b 1214164853@qq.com,
More informationDebojyoti Ghosh. Adviser: Dr. James Baeder Alfred Gessow Rotorcraft Center Department of Aerospace Engineering
Debojyoti Ghosh Adviser: Dr. James Baeder Alfred Gessow Rotorcraft Center Department of Aerospace Engineering To study the Dynamic Stalling of rotor blade cross-sections Unsteady Aerodynamics: Time varying
More informationChapter 4 FLUID KINEMATICS
Fluid Mechanics: Fundamentals and Applications, 2nd Edition Yunus A. Cengel, John M. Cimbala McGraw-Hill, 2010 Chapter 4 FLUID KINEMATICS Lecture slides by Hasan Hacışevki Copyright The McGraw-Hill Companies,
More informationDraft SPOTS Standard Part III (7)
SPOTS Good Practice Guide to Electronic Speckle Pattern Interferometry for Displacement / Strain Analysis Draft SPOTS Standard Part III (7) CALIBRATION AND ASSESSMENT OF OPTICAL STRAIN MEASUREMENTS Good
More informationDepth Estimation with a Plenoptic Camera
Depth Estimation with a Plenoptic Camera Steven P. Carpenter 1 Auburn University, Auburn, AL, 36849 The plenoptic camera is a tool capable of recording significantly more data concerning a particular image
More informationCOSC579: Scene Geometry. Jeremy Bolton, PhD Assistant Teaching Professor
COSC579: Scene Geometry Jeremy Bolton, PhD Assistant Teaching Professor Overview Linear Algebra Review Homogeneous vs non-homogeneous representations Projections and Transformations Scene Geometry The
More informationLab 9: FLUENT: Transient Natural Convection Between Concentric Cylinders
Lab 9: FLUENT: Transient Natural Convection Between Concentric Cylinders Objective: The objective of this laboratory is to introduce how to use FLUENT to solve both transient and natural convection problems.
More informationHigh-Order Numerical Algorithms for Steady and Unsteady Simulation of Viscous Compressible Flow with Shocks (Grant FA )
High-Order Numerical Algorithms for Steady and Unsteady Simulation of Viscous Compressible Flow with Shocks (Grant FA9550-07-0195) Sachin Premasuthan, Kui Ou, Patrice Castonguay, Lala Li, Yves Allaneau,
More informationOther approaches to obtaining 3D structure
Other approaches to obtaining 3D structure Active stereo with structured light Project structured light patterns onto the object simplifies the correspondence problem Allows us to use only one camera camera
More informationGEOG 4110/5100 Advanced Remote Sensing Lecture 2
GEOG 4110/5100 Advanced Remote Sensing Lecture 2 Data Quality Radiometric Distortion Radiometric Error Correction Relevant reading: Richards, sections 2.1 2.8; 2.10.1 2.10.3 Data Quality/Resolution Spatial
More informationSIMULATION OF METAL FORMING PROCESSES. Konstantin SOLOMONOV a, Victor SVIRIN b
SIMULATION OF METAL FORMING PROCESSES Konstantin SOLOMONOV a, Victor SVIRIN b a Moscow State University of Railway Engineering (Voronezh branch), 75а, Uritskogo street, 394026, Voronezh, Russia, E-mail
More informationPartial Differential Equations
Simulation in Computer Graphics Partial Differential Equations Matthias Teschner Computer Science Department University of Freiburg Motivation various dynamic effects and physical processes are described
More information