Close-Range. Photogrammetry. and 3D Imaging. 2nd edition. Edited by. Thomas Luhmann, Stuart Robson, Stephen Kyle. and Jan Boehm.
|
|
- Howard Ford
- 5 years ago
- Views:
Transcription
1 Close-Range Photogrammetry and 3D Imaging 2nd edition Edited by Thomas Luhmann, Stuart Robson, Stephen Kyle and Jan Boehm De Gruyter
2 Content 1 Introduction Overview Content References Fundamental methods The photogrammetric process Aspects of photogrammetry Image-forming model Photogrammetric systems and procedures Analogue systems Digital systems Recording and analysis procedures Photogrammetric products Application areas Historical development 17 2 Mathematical fundamentals Coordinate systems Image and camera coordinate systems Pixel and sensor coordinate system Model coordinate system Object coordinate system Coordinate transformations Plane transformations Similarity transformation Affine transformation Polynomial transformation Bilinear transformation Projective transformation Spatial transformations Spatial rotations Spatial similarity transformation Homogeneous coordinate transformations Geometric elements Analytical geometry in the plane Straight line Circle Ellipse Curves Analytical geometry in 3D space Straight line Plane Rotationally symmetric shapes Surfaces Digital surface model 78
3 vjjj Content B-spline and Bezier surfaces Compliance with design Adjustment techniques The problem Functional model Stochastic model Least-squares method (Gauss-Markov linear model) Adjustment of direct observations General least squares adjustment Levenberg-Marquardt algorithm Conditional least squares adjustment Quality measures Precision and accuracy Confidence interval Correlations Reliability Error detection in practice Data snooping Variance component estimation Robust estimation with weighting functions Robust estimation according to LI norm RANSAC Computational aspects Linearisation Normal systems of equations Sparse matrix techniques and optimisation Imaging technology Physics of image formation Wave optics Electro-magnetic spectrum Radiometry Refraction and reflection Diffraction Optical imaging Geometric optics Apertures and stops Focussing Scheimpflug condition Aberrations Distortion Chromatic aberration Spherical aberration Astigmatism and curvature of field Coma Light fall-off and vignetting Resolution Resolving power of a lens 126
4 Content jx Geometric resolving power Contrast and modulation transfer function Fundamentals of sampling theory Sampling theorem Detector characteristics Photogrammetric Imaging Concepts Offline and online systems Offline photogrammetry Online photogrammetry Imaging configurations Single image acquisition Stereo image acquisition Multi-image acquisition Geometry of the camera as a measuring device Image scale and accuracy Image scale Accuracy estimation Interior orientation of a camera Physical definition of the image coordinate system Perspective centre and distortion Parameters of interior orientation Metric and semi-metric cameras Determination of interior orientation (calibration) Standardised correction functions Symmetric radial distortion Tangential distortion Affinity and shear Total correction Alternative correction formulations Simplified models Additional parameters Correction of distortion as a function of object distance Image-variant calibration Correction of local image deformation Iterative correction of imaging errors Fisheye projections System components Opto-electronic imaging sensors Principle of CCD sensor CCD area sensors CMOS matrix sensors Colour cameras Geometric properties Radiometric properties Camera technology Camera types Shutter Image stabilisation 187
5 x Content Lenses Relative aperture and f/number Field of view Super wide-angle and fisheye lenses Zoom lenses Tilt-shift lenses Telecentric lenses Stereo image splitting Filters Imaging systems Analogue cameras Analogue video cameras Analogue camera technology Digitisation of analogue video signals Digital cameras High-speed cameras Stereo and multi-camera systems Micro and macro-scanning cameras Micro scanning Macro scanning Panoramic cameras Line scanners Panorama stitching Panoramas from fisheye lenses Video theodolites and total stations Thermal imaging cameras Targeting and illumination Object targeting Targeting material Circular targets Spherical targets Patterned targets Coded targets Probes and hidden-point devices Illumination and projection techniques Electronic flash Pattern projection Laser projectors Directional lighting D cameras and range systems Laser-based systems Laser triangulation Laser scanners Laser trackers Fringe projection systems Stationary fringe projection Dynamic fringe projection (phase-shift method) Coded light (Gray code) 249
6 Content Single-camera fringe-projection systems Multi-camera fringe-projection systems Low-cost consumer grade range 3D cameras Analytical methods Overview Processing of single images Exterior orientation Standard case Special case of terrestrial photogrammetry Collinearity equations Space resection Space resection with known interior orientation Space resection with unknown interior orientation Approximate values for resection Resection with minimum object information Quality measures Linear orientation methods Direct linear transformation (DLT) Perspective projection matrix Object position and orientation by inverse resection Position and orientation of an object with respect to a camera Position and orientation of one object relative to another Projective transformation of a plane Mathematical model Influence of interior orientation Influence of non-coplanar object points Plane rectification Measurement of flat objects Single image evaluation of three-dimensional object models Object planes Digital surface models Differential rectification Processing of stereo images Stereoscopic principle Stereoscopic matching Tie points Orientation of stereo image pairs Normal case of stereo photogrammetry Epipolar geometry Relative orientation Coplanarity constraint Calculation Model coordinates Calculation of epipolar lines Calculation of normal-case images Quality of relative orientation Special cases of relative orientation 307
7 xii Content Fundamental matrix and essential matrix Absolute orientation Mathematical model Definition of the datum Calculation of exterior orientations Calculation of relative orientation from exterior orientations Stereoscopic processing Principle of stereo image processing Point determination using image coordinates Point determination with floating mark Multi-image processing and bundle adjustment General remarks Objectives Dataflow Mathematical model Adjustment model Normal equations Combined adjustment of photogrammetric and survey observations Adjustment of additional parameters Object coordinate system (definition of datum) Rank and datum defect Reference points Free net adjustment Generation of approximate Strategies for the automatic calculation of approximate values 349 values Initial value generation by automatic point measurement Practical aspects of the generation of approximate values Quality measures and analysis of results Output report Precision of image coordinates Precision of object coordinates Quality of self-calibration Strategies for bundle adjustment Simulation Divergence Elimination of gross errors Multi-image processing General space intersection Direct determination of geometric elements Determination of spatial curves (snakes) Panoramic photogrammetry Cylindrical panoramic imaging model Orientation of panoramic imagery Approximate values Space resection Bundle adjustment Epipolar geometry Spatial intersection 381
8 Content xiii Rectification of panoramic images Orthogonal rectification Tangential images Multi-media photogrammetry Light refraction at media interfaces Media interfaces Plane parallel media interfaces Ray tracing through refracting interfaces Extended model ofbundle triangulation Object-invariant interfaces Bundle-invariant interfaces Digital image processing Fundamentals Image processing procedure Pixel coordinate system Handling image data Image pyramids Data formats Image compression Image preprocessing Point operations Histogram Lookup tables Contrast enhancement Thresholding Image arithmetic Colour operations Colour spaces Colour transformations Colour combinations Filter operations Spatial domain and frequency domain Smoothing filters Morphological operations Wallis filter Edge extraction First order differential filters Second order differential filters Laplacian of Gaussian filter Image sharpening Hough transform Enhanced edge operators Sub-pixel interpolation Geometric image transformation Fundamentals of rectification Grey-value interpolation D visualisation 439
9 iterative x;v Content Overview Reflection and illumination Texture mapping Digital processing of single images Approximate values Possibilities Segmentation of point features Measurement of single point features On-screen measurement Centroid methods Correlation methods Least-squares matching Structural measuring methods Accuracy issues Contour following Profile-driven contour following Contour following by gradient analysis Image matching and 3D object reconstruction Overview Feature-based matching procedures Interest operators Feature detectors Correspondence analysis Correspondence analysis based on epipolar geometry Matching in image pairs Matching in image triples Matching in an unlimited number of images Area-based multi-image matching Multi-image matching Geometric constraints Semi-global matching Matching methods with object models Object-based multi-image matching Multi-image matching with surface grids Range imaging and point clouds Data representations Registration D target recognition D target recognition Automated correspondence analysis Point cloud registration - closest point algorithm Range-image processing Measuring tasks and systems Overview Single-camera systems Camera with hand-held probe Probing system with integrated camera 502
10 Content xy_ Camera system for robot calibration High-speed 6 DOF system Stereoscopic systems Digital stereo plotters Principle of stereoplotting Orientation procedures Object reconstruction Digital stereo viewing systems Stereo vision systems Multi-image systems Interactive processing systems Mobile industrial point measuring-systems Offline photogrammetric systems Online photogrammetric systems Static industrial online measuring systems Tube inspection system Steel-plate positioning system Passive surface-measuring systems Point and grid projection Multi-camera system with projected point arrays Multi-camera systems with target grid projection Multi-camera system with grid projection Digital image correlation with random surface-texture patterns Techniques for texture generation Data processing Multi-camera system for dynamic surface changes Measurement of complex surfaces Self-locating scanners orientation with object points Scanner location by optical tracking Mechanical location of scanners Dynamic photogrammetry Relative movement between object and imaging system Static object Moving object Recording dynamic sequences Motion capture (MoCap) Mobile measurement platforms Mobile mapping systems Close-range aerial imagery Measurement design and quality Project planning Planning criteria Accuracy issues Restrictions on imaging configuration Monte Carlo simulation Computer-aided design of the imaging network Quality measures and performance testing 552
11 xvj Content Quality parameters Measurement uncertainty Reference value Measurement error Accuracy Precision Precision and accuracy parameters from a bundle adjustment Relative accuracy Tolerance Resolution Acceptance and re-verification of measuring systems Definition of terms Differentiation from coordinate measuring machines (CMMs) Reference artefacts Testing of point-by-point measuring systems Testing of area-scanning systems Strategies for camera calibration Calibration methods Laboratory calibration Test-field calibration Plumb-line calibration On-the-job calibration Self-calibration System calibration Imaging configurations Calibration using a plane point field Calibration using a spatial point field Calibration with moving scale bar Problems with self-calibration Example applications Architecture, archaeology and cultural heritage Photogrammetric building records Siena cathedral Gunpowder tower, Oldenburg Haderburg castle D city and landscape models Building visualisation City models D record of Pompeii Free-form surfaces Statues and sculptures Large free-form objects Survey of the Bremen cog Image mosaics Image mosaics for mapping dinosaur tracks Central perspective image mosaic Engineering surveying and civil engineering 594
12 Content xvi[ D modelling of complex objects As-built documentation Stairwell measurement Deformation analysis Shape measurement of large steel converters Deformation of concrete tanks Material testing Surface measurement of mortar joints in brickwork Structural loading tests Roof and facade measurement Industrial applications Power stations and production plants Wind power stations Particle accelerators Aircraft and space industries Inspection of tooling jigs Process control Antenna measurement Car industry Rapid prototyping and reverse engineering Car safety tests Car body deformations Ship building industry Medicine Surface measurement Online navigation systems Miscellaneous applications Forensic applications Accident recording Scene-of-crime recording Scientific applications D reconstruction of a spider's web Monitoring glacier movements Earth sciences Literature Textbooks Photogrammetry Optic, camera and imaging techniques Digital image processing, computer vision and pattern recognition Mathematics and 3D computer graphics Least-squares adjustment and statistics Industrial and optical 3D metrology Introduction and history Mathematical fundamentals Transformations and geometry Adjustment techniques Imaging technology 633
13 xvjjj Content Optics and sampling theory Camera modelling and calibration Sensors and cameras Targeting and illumination Laser-based systems D imaging systems Phase-based measurements Analytical methods Analytical photogrammetry Bundle adjustment Camera calibration Multi-media photogrammetry Panoramic photogrammetry Digital image processing Fundamentals Pattern recognition and image matching Range image and point cloud processing Measurement tasks and systems Overviews Measurement of points and contours Measurement of surfaces Dynamic and mobile systems Quality issues and optimization Project planning and simulation Quality Applications Architecture, archaeology, city models Engineering and industrial applications Medicine, forensics, earth sciences Other sources of information Standards and guidelines Working groups and conferences 661 Abbreviations 663 Image sources 667 Index 671
Exterior 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 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 informationIndex. 3D reconstruction, point algorithm, point algorithm, point algorithm, point algorithm, 263
Index 3D reconstruction, 125 5+1-point algorithm, 284 5-point algorithm, 270 7-point algorithm, 265 8-point algorithm, 263 affine point, 45 affine transformation, 57 affine transformation group, 57 affine
More informationTERRESTRIAL AND NUMERICAL PHOTOGRAMMETRY 1. MID -TERM EXAM Question 4
TERRESTRIAL AND NUMERICAL PHOTOGRAMMETRY 1. MID -TERM EXAM Question 4 23 November 2001 Two-camera stations are located at the ends of a base, which are 191.46m long, measured horizontally. Photographs
More informationIndex. 3D reconstruction, point algorithm, point algorithm, point algorithm, point algorithm, 253
Index 3D reconstruction, 123 5+1-point algorithm, 274 5-point algorithm, 260 7-point algorithm, 255 8-point algorithm, 253 affine point, 43 affine transformation, 55 affine transformation group, 55 affine
More informationContents I IMAGE FORMATION 1
Contents I IMAGE FORMATION 1 1 Geometric Camera Models 3 1.1 Image Formation............................. 4 1.1.1 Pinhole Perspective....................... 4 1.1.2 Weak Perspective.........................
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 informationIMAGE ACQUISITION FOR DIGITAL PHOTOGRAMMETRY USING OF THE SHELF AND METRIC CAMERAS
IMAGE ACQUISITION FOR DIGITAL PHOTOGRAMMETRY USING OF THE SHELF AND METRIC CAMERAS Günter Pomaska FH Bielefeld, University of Applied Sciences Artilleriestr. 9, D32427 Minden gp@imagefact.de, www.imagefact.de
More informationChapters 1 5. Photogrammetry: Definition, introduction, and applications. Electro-magnetic radiation Optics Film development and digital cameras
Chapters 1 5 Chapter 1: Photogrammetry: Definition, introduction, and applications Chapters 2 4: Electro-magnetic radiation Optics Film development and digital cameras Chapter 5: Vertical imagery: Definitions,
More informationDESIGN AND TESTING OF MATHEMATICAL MODELS FOR A FULL-SPHERICAL CAMERA ON THE BASIS OF A ROTATING LINEAR ARRAY SENSOR AND A FISHEYE LENS
DESIGN AND TESTING OF MATHEMATICAL MODELS FOR A FULL-SPHERICAL CAMERA ON THE BASIS OF A ROTATING LINEAR ARRAY SENSOR AND A FISHEYE LENS Danilo SCHNEIDER, Ellen SCHWALBE Institute of Photogrammetry and
More informationChapters 1 9: Overview
Chapters 1 9: Overview Chapter 1: Introduction Chapters 2 4: Data acquisition Chapters 5 9: Data manipulation Chapter 5: Vertical imagery Chapter 6: Image coordinate measurements and refinements Chapters
More informationChapter 3 Image Registration. Chapter 3 Image Registration
Chapter 3 Image Registration Distributed Algorithms for Introduction (1) Definition: Image Registration Input: 2 images of the same scene but taken from different perspectives Goal: Identify transformation
More informationChapters 1 5. Photogrammetry: Definition, introduction, and applications. Electro-magnetic radiation Optics Film development and digital cameras
Chapters 1 5 Chapter 1: Photogrammetry: Definition, introduction, and applications Chapters 2 4: Electro-magnetic radiation Optics Film development and digital cameras Chapter 5: Vertical imagery: Definitions,
More informationFundamentals of Digital Image Processing
\L\.6 Gw.i Fundamentals of Digital Image Processing A Practical Approach with Examples in Matlab Chris Solomon School of Physical Sciences, University of Kent, Canterbury, UK Toby Breckon School of Engineering,
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 informationOverview. Augmented reality and applications Marker-based augmented reality. Camera model. Binary markers Textured planar markers
Augmented reality Overview Augmented reality and applications Marker-based augmented reality Binary markers Textured planar markers Camera model Homography Direct Linear Transformation What is augmented
More informationADS40 Calibration & Verification Process. Udo Tempelmann*, Ludger Hinsken**, Utz Recke*
ADS40 Calibration & Verification Process Udo Tempelmann*, Ludger Hinsken**, Utz Recke* *Leica Geosystems GIS & Mapping GmbH, Switzerland **Ludger Hinsken, Author of ORIMA, Konstanz, Germany Keywords: ADS40,
More informationCalibration of IRS-1C PAN-camera
Calibration of IRS-1C PAN-camera Karsten Jacobsen Institute for Photogrammetry and Engineering Surveys University of Hannover Germany Tel 0049 511 762 2485 Fax -2483 Email karsten@ipi.uni-hannover.de 1.
More informationLaser sensors. Transmitter. Receiver. Basilio Bona ROBOTICA 03CFIOR
Mobile & Service Robotics Sensors for Robotics 3 Laser sensors Rays are transmitted and received coaxially The target is illuminated by collimated rays The receiver measures the time of flight (back and
More informationImage Processing Fundamentals. Nicolas Vazquez Principal Software Engineer National Instruments
Image Processing Fundamentals Nicolas Vazquez Principal Software Engineer National Instruments Agenda Objectives and Motivations Enhancing Images Checking for Presence Locating Parts Measuring Features
More informationMultiple View Geometry in Computer Vision Second Edition
Multiple View Geometry in Computer Vision Second Edition Richard Hartley Australian National University, Canberra, Australia Andrew Zisserman University of Oxford, UK CAMBRIDGE UNIVERSITY PRESS Contents
More informationAUTOMATIC ORIENTATION AND MERGING OF LASER SCANNER ACQUISITIONS THROUGH VOLUMETRIC TARGETS: PROCEDURE DESCRIPTION AND TEST RESULTS
AUTOMATIC ORIENTATION AND MERGING OF LASER SCANNER ACQUISITIONS THROUGH VOLUMETRIC TARGETS: PROCEDURE DESCRIPTION AND TEST RESULTS G.Artese a, V.Achilli b, G.Salemi b, A.Trecroci a a Dept. of Land Planning,
More informationCentre for Digital Image Measurement and Analysis, School of Engineering, City University, Northampton Square, London, ECIV OHB
HIGH ACCURACY 3-D MEASUREMENT USING MULTIPLE CAMERA VIEWS T.A. Clarke, T.J. Ellis, & S. Robson. High accuracy measurement of industrially produced objects is becoming increasingly important. The techniques
More informationMULTIPLE-SENSOR INTEGRATION FOR EFFICIENT REVERSE ENGINEERING OF GEOMETRY
Proceedings of the 11 th International Conference on Manufacturing Research (ICMR2013) MULTIPLE-SENSOR INTEGRATION FOR EFFICIENT REVERSE ENGINEERING OF GEOMETRY Feng Li, Andrew Longstaff, Simon Fletcher,
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 informationAn introduction to 3D image reconstruction and understanding concepts and ideas
Introduction to 3D image reconstruction An introduction to 3D image reconstruction and understanding concepts and ideas Samuele Carli Martin Hellmich 5 febbraio 2013 1 icsc2013 Carli S. Hellmich M. (CERN)
More informationEXAM SOLUTIONS. Image Processing and Computer Vision Course 2D1421 Monday, 13 th of March 2006,
School of Computer Science and Communication, KTH Danica Kragic EXAM SOLUTIONS Image Processing and Computer Vision Course 2D1421 Monday, 13 th of March 2006, 14.00 19.00 Grade table 0-25 U 26-35 3 36-45
More informationIMAGE-BASED 3D ACQUISITION TOOL FOR ARCHITECTURAL CONSERVATION
IMAGE-BASED 3D ACQUISITION TOOL FOR ARCHITECTURAL CONSERVATION Joris Schouteden, Marc Pollefeys, Maarten Vergauwen, Luc Van Gool Center for Processing of Speech and Images, K.U.Leuven, Kasteelpark Arenberg
More information5 member consortium o University of Strathclyde o Wideblue o National Nuclear Laboratory o Sellafield Ltd o Inspectahire
3 year, 1.24M Innovate UK funded Collaborative Research and Development Project (Nuclear Call) o Commenced April 2015 o Follows on from a successful 6 month Innovate UK funded feasibility study 2013-2014
More informationFlexible Calibration of a Portable Structured Light System through Surface Plane
Vol. 34, No. 11 ACTA AUTOMATICA SINICA November, 2008 Flexible Calibration of a Portable Structured Light System through Surface Plane GAO Wei 1 WANG Liang 1 HU Zhan-Yi 1 Abstract For a portable structured
More informationBroad field that includes low-level operations as well as complex high-level algorithms
Image processing About Broad field that includes low-level operations as well as complex high-level algorithms Low-level image processing Computer vision Computational photography Several procedures and
More informationPrecise laser-based optical 3D measurement of welding seams under water
Precise laser-based optical 3D measurement of welding seams under water ISPRS/CIPA Workshop Underwater 3D Recording & Modeling" Piano di Sorrento (Napoli), Italy 16. 17. April 2015 Tanja Ekkel (M.Sc.)
More informationROBUST LINE-BASED CALIBRATION OF LENS DISTORTION FROM A SINGLE VIEW
ROBUST LINE-BASED CALIBRATION OF LENS DISTORTION FROM A SINGLE VIEW Thorsten Thormählen, Hellward Broszio, Ingolf Wassermann thormae@tnt.uni-hannover.de University of Hannover, Information Technology Laboratory,
More informationApplication questions. Theoretical questions
The oral exam will last 30 minutes and will consist of one application question followed by two theoretical questions. Please find below a non exhaustive list of possible application questions. The list
More informationFeature Extraction and Image Processing, 2 nd Edition. Contents. Preface
, 2 nd Edition Preface ix 1 Introduction 1 1.1 Overview 1 1.2 Human and Computer Vision 1 1.3 The Human Vision System 3 1.3.1 The Eye 4 1.3.2 The Neural System 7 1.3.3 Processing 7 1.4 Computer Vision
More informationEE795: Computer Vision and Intelligent Systems
EE795: Computer Vision and Intelligent Systems Spring 2012 TTh 17:30-18:45 FDH 204 Lecture 10 130221 http://www.ee.unlv.edu/~b1morris/ecg795/ 2 Outline Review Canny Edge Detector Hough Transform Feature-Based
More informationFinal Exam Study Guide
Final Exam Study Guide Exam Window: 28th April, 12:00am EST to 30th April, 11:59pm EST Description As indicated in class the goal of the exam is to encourage you to review the material from the course.
More informationVideo Mosaics for Virtual Environments, R. Szeliski. Review by: Christopher Rasmussen
Video Mosaics for Virtual Environments, R. Szeliski Review by: Christopher Rasmussen September 19, 2002 Announcements Homework due by midnight Next homework will be assigned Tuesday, due following Tuesday.
More informationComputational Optical Imaging - Optique Numerique. -- Single and Multiple View Geometry, Stereo matching --
Computational Optical Imaging - Optique Numerique -- Single and Multiple View Geometry, Stereo matching -- Autumn 2015 Ivo Ihrke with slides by Thorsten Thormaehlen Reminder: Feature Detection and Matching
More informationAUTOMATED CALIBRATION TECHNIQUE FOR PHOTOGRAMMETRIC SYSTEM BASED ON A MULTI-MEDIA PROJECTOR AND A CCD CAMERA
AUTOMATED CALIBRATION TECHNIQUE FOR PHOTOGRAMMETRIC SYSTEM BASED ON A MULTI-MEDIA PROJECTOR AND A CCD CAMERA V. A. Knyaz * GosNIIAS, State Research Institute of Aviation System, 539 Moscow, Russia knyaz@gosniias.ru
More informationBSB663 Image Processing Pinar Duygulu. Slides are adapted from Selim Aksoy
BSB663 Image Processing Pinar Duygulu Slides are adapted from Selim Aksoy Image matching Image matching is a fundamental aspect of many problems in computer vision. Object or scene recognition Solving
More informationA METHOD TO ACHIEVE LARGE VOLUME, HIGH ACCURACY PHOTOGRAMMETRIC MEASUREMENTS THROUGH THE USE OF AN ACTIVELY DEFORMABLE SENSOR MOUNTING PLATFORM
A METHOD TO ACHIEVE LARGE VOLUME, HIGH ACCURACY PHOTOGRAMMETRIC MEASUREMENTS THROUGH THE USE OF AN ACTIVELY DEFORMABLE SENSOR MOUNTING PLATFORM B. Sargeant a, S. Robson a, E.Szigeti b, P.Richardson b,
More informationHigh Definition Modeling of Calw, Badstrasse and its Google Earth Integration
Master Thesis Yuanting LI High Definition Modeling of Calw, Badstrasse and its Google Earth Integration Duration of the Thesis: 6 months Completion: July, 2014 Supervisors: Prof.Dr.-Ing.Dieter Fritsch
More informationImage Processing, Analysis and Machine Vision
Image Processing, Analysis and Machine Vision Milan Sonka PhD University of Iowa Iowa City, USA Vaclav Hlavac PhD Czech Technical University Prague, Czech Republic and Roger Boyle DPhil, MBCS, CEng University
More informationUnit 3 Multiple View Geometry
Unit 3 Multiple View Geometry Relations between images of a scene Recovering the cameras Recovering the scene structure http://www.robots.ox.ac.uk/~vgg/hzbook/hzbook1.html 3D structure from images Recover
More informationChapter 1: Overview. Photogrammetry: Introduction & Applications Photogrammetric tools:
Chapter 1: Overview Photogrammetry: Introduction & Applications Photogrammetric tools: Rotation matrices Photogrammetric point positioning Photogrammetric bundle adjustment This chapter will cover the
More informationAll good things must...
Lecture 17 Final Review All good things must... UW CSE vision faculty Course Grading Programming Projects (80%) Image scissors (20%) -DONE! Panoramas (20%) - DONE! Content-based image retrieval (20%) -
More informationMotion Estimation and Optical Flow Tracking
Image Matching Image Retrieval Object Recognition Motion Estimation and Optical Flow Tracking Example: Mosiacing (Panorama) M. Brown and D. G. Lowe. Recognising Panoramas. ICCV 2003 Example 3D Reconstruction
More informationMultiple View Geometry
Multiple View Geometry Martin Quinn with a lot of slides stolen from Steve Seitz and Jianbo Shi 15-463: Computational Photography Alexei Efros, CMU, Fall 2007 Our Goal The Plenoptic Function P(θ,φ,λ,t,V
More informationMultiview Stereo COSC450. Lecture 8
Multiview Stereo COSC450 Lecture 8 Stereo Vision So Far Stereo and epipolar geometry Fundamental matrix captures geometry 8-point algorithm Essential matrix with calibrated cameras 5-point algorithm Intersect
More informationPOSITIONING A PIXEL IN A COORDINATE SYSTEM
GEOREFERENCING AND GEOCODING EARTH OBSERVATION IMAGES GABRIEL PARODI STUDY MATERIAL: PRINCIPLES OF REMOTE SENSING AN INTRODUCTORY TEXTBOOK CHAPTER 6 POSITIONING A PIXEL IN A COORDINATE SYSTEM The essential
More informationCSE 527: Introduction to Computer Vision
CSE 527: Introduction to Computer Vision Week 5 - Class 1: Matching, Stitching, Registration September 26th, 2017 ??? Recap Today Feature Matching Image Alignment Panoramas HW2! Feature Matches Feature
More informationLens Design I. Lecture 11: Imaging Herbert Gross. Summer term
Lens Design I Lecture 11: Imaging 2015-06-29 Herbert Gross Summer term 2015 www.iap.uni-jena.de 2 Preliminary Schedule 1 13.04. Basics 2 20.04. Properties of optical systrems I 3 27.05. 4 04.05. Properties
More informationSTEREO VISION AND LASER STRIPERS FOR THREE-DIMENSIONAL SURFACE MEASUREMENTS
XVI CONGRESO INTERNACIONAL DE INGENIERÍA GRÁFICA STEREO VISION AND LASER STRIPERS FOR THREE-DIMENSIONAL SURFACE MEASUREMENTS BARONE, Sandro; BRUNO, Andrea University of Pisa Dipartimento di Ingegneria
More informationComputational Optical Imaging - Optique Numerique. -- Multiple View Geometry and Stereo --
Computational Optical Imaging - Optique Numerique -- Multiple View Geometry and Stereo -- Winter 2013 Ivo Ihrke with slides by Thorsten Thormaehlen Feature Detection and Matching Wide-Baseline-Matching
More informationTerrain correction. Backward geocoding. Terrain correction and ortho-rectification. Why geometric terrain correction? Rüdiger Gens
Terrain correction and ortho-rectification Terrain correction Rüdiger Gens Why geometric terrain correction? Backward geocoding remove effects of side looking geometry of SAR images necessary step to allow
More informationCHAPTER 1 Introduction 1. CHAPTER 2 Images, Sampling and Frequency Domain Processing 37
Extended Contents List Preface... xi About the authors... xvii CHAPTER 1 Introduction 1 1.1 Overview... 1 1.2 Human and Computer Vision... 2 1.3 The Human Vision System... 4 1.3.1 The Eye... 5 1.3.2 The
More informationCOMPUTER AND ROBOT VISION
VOLUME COMPUTER AND ROBOT VISION Robert M. Haralick University of Washington Linda G. Shapiro University of Washington A^ ADDISON-WESLEY PUBLISHING COMPANY Reading, Massachusetts Menlo Park, California
More informationGeometric Rectification Using Feature Points Supplied by Straight-lines
Available online at www.sciencedirect.com Procedia Environmental Sciences (0 ) 00 07 Geometric Rectification Using Feature Points Supplied by Straight-lines Tengfei Long, Weili Jiao, Wei Wang Center for
More informationComputer and Machine Vision
Computer and Machine Vision Lecture Week 11 Part-2 Segmentation, Camera Calibration and Feature Alignment March 28, 2014 Sam Siewert Outline of Week 11 Exam #1 Results Overview and Solutions Wrap up of
More informationTrimble Engineering & Construction Group, 5475 Kellenburger Road, Dayton, OH , USA
Trimble VISION Ken Joyce Martin Koehler Michael Vogel Trimble Engineering and Construction Group Westminster, Colorado, USA April 2012 Trimble Engineering & Construction Group, 5475 Kellenburger Road,
More informationarxiv: v1 [cs.cv] 28 Sep 2018
Camera Pose Estimation from Sequence of Calibrated Images arxiv:1809.11066v1 [cs.cv] 28 Sep 2018 Jacek Komorowski 1 and Przemyslaw Rokita 2 1 Maria Curie-Sklodowska University, Institute of Computer Science,
More informationMeasurement and Precision Analysis of Exterior Orientation Element Based on Landmark Point Auxiliary Orientation
2016 rd International Conference on Engineering Technology and Application (ICETA 2016) ISBN: 978-1-60595-8-0 Measurement and Precision Analysis of Exterior Orientation Element Based on Landmark Point
More informationComputer Vision I - Appearance-based Matching and Projective Geometry
Computer Vision I - Appearance-based Matching and Projective Geometry Carsten Rother 05/11/2015 Computer Vision I: Image Formation Process Roadmap for next four lectures Computer Vision I: Image Formation
More informationCamera models and calibration
Camera models and calibration Read tutorial chapter 2 and 3. http://www.cs.unc.edu/~marc/tutorial/ Szeliski s book pp.29-73 Schedule (tentative) 2 # date topic Sep.8 Introduction and geometry 2 Sep.25
More informationEpipolar Geometry in Stereo, Motion and Object Recognition
Epipolar Geometry in Stereo, Motion and Object Recognition A Unified Approach by GangXu Department of Computer Science, Ritsumeikan University, Kusatsu, Japan and Zhengyou Zhang INRIA Sophia-Antipolis,
More informationStep-by-Step Model Buidling
Step-by-Step Model Buidling Review Feature selection Feature selection Feature correspondence Camera Calibration Euclidean Reconstruction Landing Augmented Reality Vision Based Control Sparse Structure
More information55:148 Digital Image Processing Chapter 11 3D Vision, Geometry
55:148 Digital Image Processing Chapter 11 3D Vision, Geometry Topics: Basics of projective geometry Points and hyperplanes in projective space Homography Estimating homography from point correspondence
More informationTowards the completion of assignment 1
Towards the completion of assignment 1 What to do for calibration What to do for point matching What to do for tracking What to do for GUI COMPSCI 773 Feature Point Detection Why study feature point detection?
More informationRodenstock Products Photo Optics / Digital Imaging
Go to: Apo-Sironar digital Apo-Macro-Sironar digital Apo-Sironar digital HR Lenses for Digital Professional Photography Digital photography may be superior to conventional photography if the end-product
More informationComputer Vision I - Appearance-based Matching and Projective Geometry
Computer Vision I - Appearance-based Matching and Projective Geometry Carsten Rother 01/11/2016 Computer Vision I: Image Formation Process Roadmap for next four lectures Computer Vision I: Image Formation
More informationContents 1 Measurement and Machine Tools An Introduction
Contents 1 Measurement and Machine Tools An Introduction... 1 1.1 Why the Need for Accurate and Precise Machine Tools a Brief History.... 1 1.2 The Early Historical Development of a Linear Measurements....
More informationThe main problem of photogrammetry
Structured Light Structured Light The main problem of photogrammetry to recover shape from multiple views of a scene, we need to find correspondences between the images the matching/correspondence problem
More information3D Vision Real Objects, Real Cameras. Chapter 11 (parts of), 12 (parts of) Computerized Image Analysis MN2 Anders Brun,
3D Vision Real Objects, Real Cameras Chapter 11 (parts of), 12 (parts of) Computerized Image Analysis MN2 Anders Brun, anders@cb.uu.se 3D Vision! Philisophy! Image formation " The pinhole camera " Projective
More informationDERIVING PEDESTRIAN POSITIONS FROM UNCALIBRATED VIDEOS
DERIVING PEDESTRIAN POSITIONS FROM UNCALIBRATED VIDEOS Zoltan Koppanyi, Post-Doctoral Researcher Charles K. Toth, Research Professor The Ohio State University 2046 Neil Ave Mall, Bolz Hall Columbus, OH,
More informationOutline. ETN-FPI Training School on Plenoptic Sensing
Outline Introduction Part I: Basics of Mathematical Optimization Linear Least Squares Nonlinear Optimization Part II: Basics of Computer Vision Camera Model Multi-Camera Model Multi-Camera Calibration
More informationIntroduction Photogrammetry Photos light Gramma drawing Metron measure Basic Definition The art and science of obtaining reliable measurements by mean
Photogrammetry Review Neil King King and Associates Testing is an art Introduction Read the question Re-Read Read The question What is being asked Answer what is being asked Be in the know Exercise the
More informationLEARNING KIT AND TUTORIALS FOR THE DIFFUSION OF THE DIGITAL PHOTOGRAMMETRY
LEARNING KIT AND TUTORIALS FOR THE DIFFUSION OF THE DIGITAL PHOTOGRAMMETRY Elena ALBERY *, Andrea LINGUA *, Paolo MASCHIO * * Politecnico di Torino, Italia Dipartimento di Georisorse e Territorio Albery@vdiget.polito.it,
More informationAUTOMATIC RECTIFICATION OF IMAGES THROUGH SCALE INDEPENDENT TARGETS
AUTOMATIC RECTIFICATION OF IMAGES THROUGH SCALE INDEPENDENT TARGETS G. Artese a a Land Planning Dept, University of Calabria, 87036 Rende, Italy - g.artese@unical.it KEY WORDS: Target Recognition, Orthoimage,
More informationCamera Calibration for a Robust Omni-directional Photogrammetry System
Camera Calibration for a Robust Omni-directional Photogrammetry System Fuad Khan 1, Michael Chapman 2, Jonathan Li 3 1 Immersive Media Corporation Calgary, Alberta, Canada 2 Ryerson University Toronto,
More informationStructured Light II. Thanks to Ronen Gvili, Szymon Rusinkiewicz and Maks Ovsjanikov
Structured Light II Johannes Köhler Johannes.koehler@dfki.de Thanks to Ronen Gvili, Szymon Rusinkiewicz and Maks Ovsjanikov Introduction Previous lecture: Structured Light I Active Scanning Camera/emitter
More informationCOMPARATIVE STUDY OF DIFFERENT APPROACHES FOR EFFICIENT RECTIFICATION UNDER GENERAL MOTION
COMPARATIVE STUDY OF DIFFERENT APPROACHES FOR EFFICIENT RECTIFICATION UNDER GENERAL MOTION Mr.V.SRINIVASA RAO 1 Prof.A.SATYA KALYAN 2 DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING PRASAD V POTLURI SIDDHARTHA
More information1-2 Feature-Based Image Mosaicing
MVA'98 IAPR Workshop on Machine Vision Applications, Nov. 17-19, 1998, Makuhari, Chibq Japan 1-2 Feature-Based Image Mosaicing Naoki Chiba, Hiroshi Kano, Minoru Higashihara, Masashi Yasuda, and Masato
More information3D DEFORMATION MEASUREMENT USING STEREO- CORRELATION APPLIED TO EXPERIMENTAL MECHANICS
3D DEFORMATION MEASUREMENT USING STEREO- CORRELATION APPLIED TO EXPERIMENTAL MECHANICS Dorian Garcia, Jean-José Orteu École des Mines d Albi, F-81013 ALBI CT Cedex 09, France Dorian.Garcia@enstimac.fr,
More informationAnd. Modal Analysis. Using. VIC-3D-HS, High Speed 3D Digital Image Correlation System. Indian Institute of Technology New Delhi
Full Field Displacement And Strain Measurement And Modal Analysis Using VIC-3D-HS, High Speed 3D Digital Image Correlation System At Indian Institute of Technology New Delhi VIC-3D, 3D Digital Image Correlation
More informationThere are many cues in monocular vision which suggests that vision in stereo starts very early from two similar 2D images. Lets see a few...
STEREO VISION The slides are from several sources through James Hays (Brown); Srinivasa Narasimhan (CMU); Silvio Savarese (U. of Michigan); Bill Freeman and Antonio Torralba (MIT), including their own
More informationTowards a visual perception system for LNG pipe inspection
Towards a visual perception system for LNG pipe inspection LPV Project Team: Brett Browning (PI), Peter Rander (co PI), Peter Hansen Hatem Alismail, Mohamed Mustafa, Joey Gannon Qri8 Lab A Brief Overview
More informationObject Recognition with Invariant Features
Object Recognition with Invariant Features Definition: Identify objects or scenes and determine their pose and model parameters Applications Industrial automation and inspection Mobile robots, toys, user
More informationInformation page for written examinations at Linköping University TER2
Information page for written examinations at Linköping University Examination date 2016-08-19 Room (1) TER2 Time 8-12 Course code Exam code Course name Exam name Department Number of questions in the examination
More information3D Modeling of Objects Using Laser Scanning
1 3D Modeling of Objects Using Laser Scanning D. Jaya Deepu, LPU University, Punjab, India Email: Jaideepudadi@gmail.com Abstract: In the last few decades, constructing accurate three-dimensional models
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 informationTERRESTRIAL LASER SCANNER DATA PROCESSING
TERRESTRIAL LASER SCANNER DATA PROCESSING L. Bornaz (*), F. Rinaudo (*) (*) Politecnico di Torino - Dipartimento di Georisorse e Territorio C.so Duca degli Abruzzi, 24 10129 Torino Tel. +39.011.564.7687
More informationMODERN DIMENSIONAL MEASURING TECHNIQUES BASED ON OPTICAL PRINCIPLES
MODERN DIMENSIONAL MEASURING TECHNIQUES BASED ON OPTICAL PRINCIPLES J. Reichweger 1, J. Enzendorfer 1 and E. Müller 2 1 Steyr Daimler Puch Engineering Center Steyr GmbH Schönauerstrasse 5, A-4400 Steyr,
More information5 member consortium o University of Strathclyde o Wideblue o National Nuclear Laboratory o Sellafield Ltd o Inspectahire
Summan, Rahul and Mathur, Neha and Dobie, Gordon and West, Graeme and Marshall, Stephen and Mineo, Carmelo and MacLeod, Charles Norman and Pierce, Stephen and Kerr, William (2016) Mosaicing for automated
More informationDense 3D Reconstruction. Christiano Gava
Dense 3D Reconstruction Christiano Gava christiano.gava@dfki.de Outline Previous lecture: structure and motion II Structure and motion loop Triangulation Today: dense 3D reconstruction The matching problem
More informationStructured light 3D reconstruction
Structured light 3D reconstruction Reconstruction pipeline and industrial applications rodola@dsi.unive.it 11/05/2010 3D Reconstruction 3D reconstruction is the process of capturing the shape and appearance
More informationDepth Sensors Kinect V2 A. Fornaser
Depth Sensors Kinect V2 A. Fornaser alberto.fornaser@unitn.it Vision Depth data It is not a 3D data, It is a map of distances Not a 3D, not a 2D it is a 2.5D or Perspective 3D Complete 3D - Tomography
More informationHIGH PRECISION SURVEY AND ALIGNMENT OF LARGE LINEAR COLLIDERS - HORIZONTAL ALIGNMENT -
HIGH PRECISION SURVEY AND ALIGNMENT OF LARGE LINEAR COLLIDERS - HORIZONTAL ALIGNMENT - A. Herty, J. Albert 1 Deutsches Elektronen-Synchrotron DESY, Hamburg, Germany with international partners * 1. INTRODUCTION
More informationMetrology and Sensing
Metrology and Sensing Lecture 4: Fringe projection 2016-11-08 Herbert Gross Winter term 2016 www.iap.uni-jena.de 2 Preliminary Schedule No Date Subject Detailed Content 1 18.10. Introduction Introduction,
More informationToday. Stereo (two view) reconstruction. Multiview geometry. Today. Multiview geometry. Computational Photography
Computational Photography Matthias Zwicker University of Bern Fall 2009 Today From 2D to 3D using multiple views Introduction Geometry of two views Stereo matching Other applications Multiview geometry
More information