Overview. Augmented reality and applications Marker-based augmented reality. Camera model. Binary markers Textured planar markers
|
|
- Deirdre Boyd
- 6 years ago
- Views:
Transcription
1 Augmented reality
2 Overview Augmented reality and applications Marker-based augmented reality Binary markers Textured planar markers Camera model Homography Direct Linear Transformation
3 What is augmented reality? Reality is subjective Sight Hearing Smell Haptics Balance Augmenting sensory information
4 Augmenting visual information Superpositioning digital information on top of real imagery Who Framed Roger Rabbit (1988) Tom Caudell - Boeing (1990) Xerox & University of Toronto (1990) Virtual Fixtures (1992)
5 Classical approach Visual information acquisition: camera Camera localization Image: camera Depth: depth camera Other: GPS, WiFi, IMU Displaying augmented information: monitor, mobile phone, projector, smart glasses
6 Application examples (TV) Olympic games 2004 Monitor/TV Robotic camera USA elections 2008 CNN Hologram conference 35 cameras 20 computers
7 Application examples (Mobile) Mobile devices Pokemon Go Vuforia, ARKit Wearables BMW Hololens
8 AR using depth information Depth cameras Active (IR light) Passive (Stereo systems) Automatic scene reconstruction Easier interaction with objects Izadi et al. "KinectFusion: Real-Time Dynamic 3D Surface Reconstruction and Interaction", SIGGRAPH, 2011
9 AR with visual anchor Localization with visual information Detect key object in image Determine relative position of the object to camera Draw information with this relation
10 From point to pixel Transform 3D point to camera coordinate system (pixels) Required data K... camera calibration matrix (intrinsic parameters) R,t... rotation and translation matrices (extrinsic parameters)
11 AR with binary marker Detect markers that are easy to detect and identify Detect marker from edges Identify marker with correlation Known marker size Compute relation to camera Use corners of marker to compute relative position
12 AR with arbitrary planar marker Match an arbitrary surface Describe local texture Robust matching Less constrained can use existing textures from real world Posters Building facades
13 Applications of marker AR Catalogs Books Tourism Gaming
14 Detecting binary marker Machine vision approach Finding possible candidates Adaptive threshold Trace contours Estimate contours as polygons Contours with 4 corners
15 Recognizing binary marker Threshold on similarity Project region to reference position Normalized cross correlation Orientation test all four options
16 Generalizing transformation Determine camera extrinsics using a detected marker Transformation between planes (homography) Marker plane (reference) Camera plane (observed)
17 Required linear algebra Homogeneous coordinates Can describe point in infinity Same point if multiplied with scalar Transformation matrix contains translation Matrix form of vector (cross) product Express product as matrix multiplication of skew-symmetric matrix and vector
18 Systems of linear equations Get a set of variables from a set of equality relations Known pairs of vectors correspondences Linear transformation contains unknowns Formulate problem in matrix form: Sufficient data - unique solution: Not enough data underdetermined system Too much data overdetermined system
19 Direct Linear Transform Similarity relations Unknown multiplicative factor Rewritten as homogeneous equations Get rid of scaling factor
20 DLT for Homography Homography is 3x3 matrix 8 unknowns Assume Need 4 correspondences
21 The top two equations are independent, the last one is not. We can reorganize the system
22 Solving homogeneous system N correspondences give us 2*N equations We can get solution for the system using SVD The solution is the last column of V Minimal mean square error Reorganize vector into matrix
23 Optimizing extrinsics Homography projects points between two planes Determine camera position Inaccurate R and t Objective function is MSR, not reprojection error
24 Minimizing reprojection error Projection of 3D point to image coordinates is written as When R and t are optimal the difference between image point and projection of 3D point should be zero Cost function is sum of all distances, it is non-linear
25 Iterative optimization of R and t Initial R ant t from homography Fix t and minimize R Use quaternions Optimize with non-linear methods (e.g. Levenberg-Marquardt, Simplex) Fix R and minimize t Using least-squares optimization Repeat steps until convergence
26 Binary marker example Simple (no normalization) With normalization
27 Augmented reality with planar marker Natural surfaces Unable to detect corners robustly Partial occlusions Can detect feature-points Over-sample reference points Not all points will match correctly Robustly estimate homography
28 Matching keypoints Detector of keypoints Descriptor of regions SIFT, SURF BRIEF, ORB Matching descriptors Distance function Symmetric matches
29 Robust estimation of homography Many correspondences Over-determined system Not all correspondences are correct Robust matching Exclude outliers from calculation Find sub-set of correspondences that agree on a model
30 RANSAC Random Sample Consensus Meta algorithm (used for many tasks) Probabilistic interpretation Repeat k times Select random set of 4 correspondences Estimate model homography (DLT) Look which other pairs agree with the model (projection from one plane to the other is small enough) Take the model with largest support (inliers) RANSAC for line fitting (source: F. Moreno)
31 Reference plane example Disney AR coloring book Urban AR
32 Beyond planar markers Reference objects Deformable surfaces No reference
33 Realistic rendering Acquired images are degraded by various factors Augmented reality will be more immersive if these factors are replicated on virtual objects
34 Motion blur Exposure time + rapid motion Simulated by smoothing image with directional filter
35 Chromatic aberration Different wavelenghts bend under different angles Simulated by distorting individual color channels Image center Image border
36 Spherical aberration Spherical lenses do not focus light perfectly Rays on the edge are focused closer Compensated by using multiple lenses No abberation With abberation
37 Vignetting Optical vignetting multiple lenses Pixel vignetting Digital sensors Angle dependence Software compensation
38 Radial distortion Imperfect lenses Deviations are apparent near the edge of image
39 Realistic rendering pipeline Gerhard Reitmayr, Axel Pinz, Visual Coherence in Augmented Reality
40
41 Diminished reality Use AR to determine which part of the image to erase using inpainting Herling, J. and Broll, W., Advanced Self-contained Object Removal for Realizing Real-time Diminished Reality in Unconstrained Environments. ISMAR 2010
Outline. Introduction System Overview Camera Calibration Marker Tracking Pose Estimation of Markers Conclusion. Media IC & System Lab Po-Chen Wu 2
Outline Introduction System Overview Camera Calibration Marker Tracking Pose Estimation of Markers Conclusion Media IC & System Lab Po-Chen Wu 2 Outline Introduction System Overview Camera Calibration
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 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 informationImage correspondences and structure from motion
Image correspondences and structure from motion http://graphics.cs.cmu.edu/courses/15-463 15-463, 15-663, 15-862 Computational Photography Fall 2017, Lecture 20 Course announcements Homework 5 posted.
More informationToday s lecture. Image Alignment and Stitching. Readings. Motion models
Today s lecture Image Alignment and Stitching Computer Vision CSE576, Spring 2005 Richard Szeliski Image alignment and stitching motion models cylindrical and spherical warping point-based alignment global
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 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 informationPin Hole Cameras & Warp Functions
Pin Hole Cameras & Warp Functions Instructor - Simon Lucey 16-423 - Designing Computer Vision Apps Today Pinhole Camera. Homogenous Coordinates. Planar Warp Functions. Example of SLAM for AR Taken from:
More information3D Visualization through Planar Pattern Based Augmented Reality
NATIONAL TECHNICAL UNIVERSITY OF ATHENS SCHOOL OF RURAL AND SURVEYING ENGINEERS DEPARTMENT OF TOPOGRAPHY LABORATORY OF PHOTOGRAMMETRY 3D Visualization through Planar Pattern Based Augmented Reality Dr.
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 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 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 informationSrikumar Ramalingam. Review. 3D Reconstruction. Pose Estimation Revisited. School of Computing University of Utah
School of Computing University of Utah Presentation Outline 1 2 3 Forward Projection (Reminder) u v 1 KR ( I t ) X m Y m Z m 1 Backward Projection (Reminder) Q K 1 q Q K 1 u v 1 What is pose estimation?
More informationImage Stitching. Slides from Rick Szeliski, Steve Seitz, Derek Hoiem, Ira Kemelmacher, Ali Farhadi
Image Stitching Slides from Rick Szeliski, Steve Seitz, Derek Hoiem, Ira Kemelmacher, Ali Farhadi Combine two or more overlapping images to make one larger image Add example Slide credit: Vaibhav Vaish
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 informationHartley - Zisserman reading club. Part I: Hartley and Zisserman Appendix 6: Part II: Zhengyou Zhang: Presented by Daniel Fontijne
Hartley - Zisserman reading club Part I: Hartley and Zisserman Appendix 6: Iterative estimation methods Part II: Zhengyou Zhang: A Flexible New Technique for Camera Calibration Presented by Daniel Fontijne
More informationHomographies and RANSAC
Homographies and RANSAC Computer vision 6.869 Bill Freeman and Antonio Torralba March 30, 2011 Homographies and RANSAC Homographies RANSAC Building panoramas Phototourism 2 Depth-based ambiguity of position
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 informationTopics to be Covered in the Rest of the Semester. CSci 4968 and 6270 Computational Vision Lecture 15 Overview of Remainder of the Semester
Topics to be Covered in the Rest of the Semester CSci 4968 and 6270 Computational Vision Lecture 15 Overview of Remainder of the Semester Charles Stewart Department of Computer Science Rensselaer Polytechnic
More informationUNIVERSITY OF TORONTO Faculty of Applied Science and Engineering. ROB501H1 F: Computer Vision for Robotics. Midterm Examination.
UNIVERSITY OF TORONTO Faculty of Applied Science and Engineering ROB501H1 F: Computer Vision for Robotics October 26, 2016 Student Name: Student Number: Instructions: 1. Attempt all questions. 2. The value
More informationEpipolar Geometry and Stereo Vision
Epipolar Geometry and Stereo Vision Computer Vision Jia-Bin Huang, Virginia Tech Many slides from S. Seitz and D. Hoiem Last class: Image Stitching Two images with rotation/zoom but no translation. X x
More informationPlanar homographies. Can we reconstruct another view from one image? vgg/projects/singleview/
Planar homographies Goal: Introducing 2D Homographies Motivation: What is the relation between a plane in the world and a perspective image of it? Can we reconstruct another view from one image? Readings:
More informationEECS 442: Final Project
EECS 442: Final Project Structure From Motion Kevin Choi Robotics Ismail El Houcheimi Robotics Yih-Jye Jeffrey Hsu Robotics Abstract In this paper, we summarize the method, and results of our projective
More informationCamera Registration in a 3D City Model. Min Ding CS294-6 Final Presentation Dec 13, 2006
Camera Registration in a 3D City Model Min Ding CS294-6 Final Presentation Dec 13, 2006 Goal: Reconstruct 3D city model usable for virtual walk- and fly-throughs Virtual reality Urban planning Simulation
More informationCS6670: Computer Vision
CS6670: Computer Vision Noah Snavely Lecture 7: Image Alignment and Panoramas What s inside your fridge? http://www.cs.washington.edu/education/courses/cse590ss/01wi/ Projection matrix intrinsics projection
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 informationRigid Body Motion and Image Formation. Jana Kosecka, CS 482
Rigid Body Motion and Image Formation Jana Kosecka, CS 482 A free vector is defined by a pair of points : Coordinates of the vector : 1 3D Rotation of Points Euler angles Rotation Matrices in 3D 3 by 3
More informationEpipolar Geometry CSE P576. Dr. Matthew Brown
Epipolar Geometry CSE P576 Dr. Matthew Brown Epipolar Geometry Epipolar Lines, Plane Constraint Fundamental Matrix, Linear solution + RANSAC Applications: Structure from Motion, Stereo [ Szeliski 11] 2
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 informationSrikumar Ramalingam. Review. 3D Reconstruction. Pose Estimation Revisited. School of Computing University of Utah
School of Computing University of Utah Presentation Outline 1 2 3 Forward Projection (Reminder) u v 1 KR ( I t ) X m Y m Z m 1 Backward Projection (Reminder) Q K 1 q Presentation Outline 1 2 3 Sample Problem
More information3D Computer Vision. Structure from Motion. Prof. Didier Stricker
3D Computer Vision Structure from Motion Prof. Didier Stricker Kaiserlautern University http://ags.cs.uni-kl.de/ DFKI Deutsches Forschungszentrum für Künstliche Intelligenz http://av.dfki.de 1 Structure
More informationStructure from Motion. Introduction to Computer Vision CSE 152 Lecture 10
Structure from Motion CSE 152 Lecture 10 Announcements Homework 3 is due May 9, 11:59 PM Reading: Chapter 8: Structure from Motion Optional: Multiple View Geometry in Computer Vision, 2nd edition, Hartley
More informationParameter estimation. Christiano Gava Gabriele Bleser
Parameter estimation Christiano Gava Christiano.Gava@dfki.de Gabriele Bleser gabriele.bleser@dfki.de Introduction Previous lectures: P-matrix 2D projective transformations Estimation (direct linear transform)
More informationCamera Geometry II. COS 429 Princeton University
Camera Geometry II COS 429 Princeton University Outline Projective geometry Vanishing points Application: camera calibration Application: single-view metrology Epipolar geometry Application: stereo correspondence
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 informationWhat have we leaned so far?
What have we leaned so far? Camera structure Eye structure Project 1: High Dynamic Range Imaging What have we learned so far? Image Filtering Image Warping Camera Projection Model Project 2: Panoramic
More informationCameras and Stereo CSE 455. Linda Shapiro
Cameras and Stereo CSE 455 Linda Shapiro 1 Müller-Lyer Illusion http://www.michaelbach.de/ot/sze_muelue/index.html What do you know about perspective projection? Vertical lines? Other lines? 2 Image formation
More informationLecture 14: Basic Multi-View Geometry
Lecture 14: Basic Multi-View Geometry Stereo If I needed to find out how far point is away from me, I could use triangulation and two views scene point image plane optical center (Graphic from Khurram
More informationCSE 252B: Computer Vision II
CSE 252B: Computer Vision II Lecturer: Serge Belongie Scribes: Jeremy Pollock and Neil Alldrin LECTURE 14 Robust Feature Matching 14.1. Introduction Last lecture we learned how to find interest points
More informationStereo and Epipolar geometry
Previously Image Primitives (feature points, lines, contours) Today: Stereo and Epipolar geometry How to match primitives between two (multiple) views) Goals: 3D reconstruction, recognition Jana Kosecka
More information3D Reconstruction of a Hopkins Landmark
3D Reconstruction of a Hopkins Landmark Ayushi Sinha (461), Hau Sze (461), Diane Duros (361) Abstract - This paper outlines a method for 3D reconstruction from two images. Our procedure is based on known
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 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 informationCS664 Lecture #19: Layers, RANSAC, panoramas, epipolar geometry
CS664 Lecture #19: Layers, RANSAC, panoramas, epipolar geometry Some material taken from: David Lowe, UBC Jiri Matas, CMP Prague http://cmp.felk.cvut.cz/~matas/papers/presentations/matas_beyondransac_cvprac05.ppt
More informationSegmentation and Tracking of Partial Planar Templates
Segmentation and Tracking of Partial Planar Templates Abdelsalam Masoud William Hoff Colorado School of Mines Colorado School of Mines Golden, CO 800 Golden, CO 800 amasoud@mines.edu whoff@mines.edu Abstract
More information3D Sensing and Reconstruction Readings: Ch 12: , Ch 13: ,
3D Sensing and Reconstruction Readings: Ch 12: 12.5-6, Ch 13: 13.1-3, 13.9.4 Perspective Geometry Camera Model Stereo Triangulation 3D Reconstruction by Space Carving 3D Shape from X means getting 3D coordinates
More informationMosaics. Today s Readings
Mosaics VR Seattle: http://www.vrseattle.com/ Full screen panoramas (cubic): http://www.panoramas.dk/ Mars: http://www.panoramas.dk/fullscreen3/f2_mars97.html Today s Readings Szeliski and Shum paper (sections
More informationAugmented Reality, Advanced SLAM, Applications
Augmented Reality, Advanced SLAM, Applications Prof. Didier Stricker & Dr. Alain Pagani alain.pagani@dfki.de Lecture 3D Computer Vision AR, SLAM, Applications 1 Introduction Previous lectures: Basics (camera,
More informationStereo Vision. MAN-522 Computer Vision
Stereo Vision MAN-522 Computer Vision What is the goal of stereo vision? The recovery of the 3D structure of a scene using two or more images of the 3D scene, each acquired from a different viewpoint in
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 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 informationPART IV: RS & the Kinect
Computer Vision on Rolling Shutter Cameras PART IV: RS & the Kinect Per-Erik Forssén, Erik Ringaby, Johan Hedborg Computer Vision Laboratory Dept. of Electrical Engineering Linköping University Tutorial
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 informationRobot Vision: Camera calibration
Robot Vision: Camera calibration Ass.Prof. Friedrich Fraundorfer SS 201 1 Outline Camera calibration Cameras with lenses Properties of real lenses (distortions, focal length, field-of-view) Calibration
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 informationStructure from motion
Structure from motion Structure from motion Given a set of corresponding points in two or more images, compute the camera parameters and the 3D point coordinates?? R 1,t 1 R 2,t R 2 3,t 3 Camera 1 Camera
More informationAUGMENTED REALITY. Antonino Furnari
IPLab - Image Processing Laboratory Dipartimento di Matematica e Informatica Università degli Studi di Catania http://iplab.dmi.unict.it AUGMENTED REALITY Antonino Furnari furnari@dmi.unict.it http://dmi.unict.it/~furnari
More informationarxiv: v1 [cs.cv] 28 Sep 2018
Extrinsic camera calibration method and its performance evaluation Jacek Komorowski 1 and Przemyslaw Rokita 2 arxiv:1809.11073v1 [cs.cv] 28 Sep 2018 1 Maria Curie Sklodowska University Lublin, Poland jacek.komorowski@gmail.com
More informationEpipolar Geometry and Stereo Vision
Epipolar Geometry and Stereo Vision Computer Vision Shiv Ram Dubey, IIIT Sri City Many slides from S. Seitz and D. Hoiem Last class: Image Stitching Two images with rotation/zoom but no translation. X
More informationMulti-Projector Display with Continuous Self-Calibration
Multi-Projector Display with Continuous Self-Calibration Jin Zhou Liang Wang Amir Akbarzadeh Ruigang Yang Graphics and Vision Technology Lab (GRAVITY Lab) Center for Visualization and Virtual Environments,
More informationCS 6320 Computer Vision Homework 2 (Due Date February 15 th )
CS 6320 Computer Vision Homework 2 (Due Date February 15 th ) 1. Download the Matlab calibration toolbox from the following page: http://www.vision.caltech.edu/bouguetj/calib_doc/ Download the calibration
More informationMERGING POINT CLOUDS FROM MULTIPLE KINECTS. Nishant Rai 13th July, 2016 CARIS Lab University of British Columbia
MERGING POINT CLOUDS FROM MULTIPLE KINECTS Nishant Rai 13th July, 2016 CARIS Lab University of British Columbia Introduction What do we want to do? : Use information (point clouds) from multiple (2+) Kinects
More informationMaster Automática y Robótica. Técnicas Avanzadas de Vision: Visual Odometry. by Pascual Campoy Computer Vision Group
Master Automática y Robótica Técnicas Avanzadas de Vision: by Pascual Campoy Computer Vision Group www.vision4uav.eu Centro de Automá
More informationCamera Models and Image Formation. Srikumar Ramalingam School of Computing University of Utah
Camera Models and Image Formation Srikumar Ramalingam School of Computing University of Utah srikumar@cs.utah.edu Reference Most slides are adapted from the following notes: Some lecture notes on geometric
More informationTwo-view geometry Computer Vision Spring 2018, Lecture 10
Two-view geometry http://www.cs.cmu.edu/~16385/ 16-385 Computer Vision Spring 2018, Lecture 10 Course announcements Homework 2 is due on February 23 rd. - Any questions about the homework? - How many of
More informationCorrecting Radial Distortion of Cameras With Wide Angle Lens Using Point Correspondences
Correcting Radial istortion of Cameras With Wide Angle Lens Using Point Correspondences Leonardo Romero and Cuauhtemoc Gomez Universidad Michoacana de San Nicolas de Hidalgo Morelia, Mich., 58000, Mexico
More informationMultiple View Geometry
Multiple View Geometry CS 6320, Spring 2013 Guest Lecture Marcel Prastawa adapted from Pollefeys, Shah, and Zisserman Single view computer vision Projective actions of cameras Camera callibration Photometric
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 informationImage-based Modeling and Rendering: 8. Image Transformation and Panorama
Image-based Modeling and Rendering: 8. Image Transformation and Panorama I-Chen Lin, Assistant Professor Dept. of CS, National Chiao Tung Univ, Taiwan Outline Image transformation How to represent the
More informationChaplin, Modern Times, 1936
Chaplin, Modern Times, 1936 [A Bucket of Water and a Glass Matte: Special Effects in Modern Times; bonus feature on The Criterion Collection set] Multi-view geometry problems Structure: Given projections
More information3D Reconstruction from Two Views
3D Reconstruction from Two Views Huy Bui UIUC huybui1@illinois.edu Yiyi Huang UIUC huang85@illinois.edu Abstract In this project, we study a method to reconstruct a 3D scene from two views. First, we extract
More informationPin Hole Cameras & Warp Functions
Pin Hole Cameras & Warp Functions Instructor - Simon Lucey 16-423 - Designing Computer Vision Apps Today Pinhole Camera. Homogenous Coordinates. Planar Warp Functions. Motivation Taken from: http://img.gawkerassets.com/img/18w7i1umpzoa9jpg/original.jpg
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 informationStitching and Blending
Stitching and Blending Kari Pulli VP Computational Imaging Light First project Build your own (basic) programs panorama HDR (really, exposure fusion) The key components register images so their features
More informationVideo Analysis for Augmented and Mixed Reality. Kiyoshi Kiyokawa Osaka University
Video Analysis for Augmented and Mixed Reality Kiyoshi Kiyokawa Osaka University Introduction Who am I? A researcher on AR / MR / VR / 3DUI / CSCW / Wearable Comp. Visualization / Wearable computing /
More informationAugmenting Reality, Naturally:
Augmenting Reality, Naturally: Scene Modelling, Recognition and Tracking with Invariant Image Features by Iryna Gordon in collaboration with David G. Lowe Laboratory for Computational Intelligence Department
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 informationBIL Computer Vision Apr 16, 2014
BIL 719 - Computer Vision Apr 16, 2014 Binocular Stereo (cont d.), Structure from Motion Aykut Erdem Dept. of Computer Engineering Hacettepe University Slide credit: S. Lazebnik Basic stereo matching algorithm
More informationCS 231A Computer Vision (Winter 2018) Problem Set 3
CS 231A Computer Vision (Winter 2018) Problem Set 3 Due: Feb 28, 2018 (11:59pm) 1 Space Carving (25 points) Dense 3D reconstruction is a difficult problem, as tackling it from the Structure from Motion
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 informationStereo II CSE 576. Ali Farhadi. Several slides from Larry Zitnick and Steve Seitz
Stereo II CSE 576 Ali Farhadi Several slides from Larry Zitnick and Steve Seitz Camera parameters A camera is described by several parameters Translation T of the optical center from the origin of world
More informationComputer Vision. Exercise 3 Panorama Stitching 09/12/2013. Compute Vision : Exercise 3 Panorama Stitching
Computer Vision Exercise 3 Panorama Stitching 09/12/2013 Compute Vision : Exercise 3 Panorama Stitching The task Compute Vision : Exercise 3 Panorama Stitching 09/12/2013 2 Pipeline Compute Vision : Exercise
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 informationFundamental Matrix & Structure from Motion
Fundamental Matrix & Structure from Motion Instructor - Simon Lucey 16-423 - Designing Computer Vision Apps Today Transformations between images Structure from Motion The Essential Matrix The Fundamental
More informationCS 231A Computer Vision (Winter 2014) Problem Set 3
CS 231A Computer Vision (Winter 2014) Problem Set 3 Due: Feb. 18 th, 2015 (11:59pm) 1 Single Object Recognition Via SIFT (45 points) In his 2004 SIFT paper, David Lowe demonstrates impressive object recognition
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 informationGeometric camera models and calibration
Geometric camera models and calibration http://graphics.cs.cmu.edu/courses/15-463 15-463, 15-663, 15-862 Computational Photography Fall 2018, Lecture 13 Course announcements Homework 3 is out. - Due October
More informationImage stitching. Digital Visual Effects Yung-Yu Chuang. with slides by Richard Szeliski, Steve Seitz, Matthew Brown and Vaclav Hlavac
Image stitching Digital Visual Effects Yung-Yu Chuang with slides by Richard Szeliski, Steve Seitz, Matthew Brown and Vaclav Hlavac Image stitching Stitching = alignment + blending geometrical registration
More informationWATERMARKING FOR LIGHT FIELD RENDERING 1
ATERMARKING FOR LIGHT FIELD RENDERING 1 Alper Koz, Cevahir Çığla and A. Aydın Alatan Department of Electrical and Electronics Engineering, METU Balgat, 06531, Ankara, TURKEY. e-mail: koz@metu.edu.tr, cevahir@eee.metu.edu.tr,
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 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 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 informationMachine vision. Summary # 11: Stereo vision and epipolar geometry. u l = λx. v l = λy
1 Machine vision Summary # 11: Stereo vision and epipolar geometry STEREO VISION The goal of stereo vision is to use two cameras to capture 3D scenes. There are two important problems in stereo vision:
More informationExperiments with Edge Detection using One-dimensional Surface Fitting
Experiments with Edge Detection using One-dimensional Surface Fitting Gabor Terei, Jorge Luis Nunes e Silva Brito The Ohio State University, Department of Geodetic Science and Surveying 1958 Neil Avenue,
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 informationCamera Model and Calibration
Camera Model and Calibration Lecture-10 Camera Calibration Determine extrinsic and intrinsic parameters of camera Extrinsic 3D location and orientation of camera Intrinsic Focal length The size of the
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 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 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 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 information