Field of View (Zoom)

Size: px
Start display at page:

Download "Field of View (Zoom)"

Transcription

1 Image Projection

2 Field of View (Zoom)

3

4 Large Focal Length compresses depth 400 mm 200 mm 100 mm 50 mm 28 mm 17 mm Michael Reichmann

5 FOV depends of Focal Length f f Smaller FOV = larger Focal Length

6 Field of View (Zoom)

7 Field of View (Zoom)

8

9

10 Fisheye lens distortion

11

12 Camera Model

13 Lens Pixel CCD sensor 3D object

14 3D Point Projection (Metric Space) 3D point ( X, Y, Z) ( u, v ) f m : Focal length in meter ( u, v ) ( X, YZ, ) X Y ( u, v ) ( fm, fm ) Z Z 2D projection onto CCD plane

15 3D Point Projection (Metric Space) Projection plane 3D point ( X, Y, Z) ( u, v ) ( u, v ) f m ( u, v ) Focal length in meter ( u, v ) ( X, YZ, ) X Y ( u, v ) ( fm, fm ) Z Z 2D projection onto CCD plane

16 3D Point Projection (Metric Space) Projection plane 3D point ( X, Y, Z) ( u, v ) ( u, v ) f m Focal length in meter ( X, YZ, ) X Y ( u, v ) ( fm, fm ) Z Z 2D projection onto CCD plane

17 3D Point Projection (Pixel Space) ( u, v ) (0,0) w h ( u, v ) w (0,0) h ( px, p ) y : Image principal point CCD sensor (mm) Image (pixel) u w u p x w v h v p y h w u u p w x h v v p h y

18 3D Point Projection (Pixel Space) O ( u, v ) w f m Projection plane h (0,0) ( u, v ) Z CCD sensor (mm) h (0,0) ( X, YZ, ) ( u, v ) w ( p, p ) x Image (pixel) y X Y ( XY,, Z) ( u, v ) ( fm, fm ) Z Z w w X u u px fm p w w Z Focal length in pixel h h Y v v py fm p h h Z Focal length in pixel y x

19 3D Point Projection (Pixel Space) O ( u, v ) w f m Projection plane h (0,0) ( u, v ) Z CCD sensor (mm) h (0,0) ( X, YZ, ) ( u, v ) w ( p, p ) x Image (pixel) y X Y ( XY,, Z) ( u, v ) ( fm, fm ) Z Z w w X u u px f f m x p w w Z Focal length in pixel h h Y v v py f f m y p h h Z Focal length in pixel y x

20 3D Point Projection (Pixel Space) Projection plane 3D point ( X, Y, Z) ( u, v ) ( u, v ) f m Focal length in meter ( X, YZ, ) w X h Y ( u, v ) ( fm, fm ) w Z h Z

21 Homogeneous Coordinate Projection plane ( X, YZ, ) 2D point =: 3D ray λ( xy,,1) O ( fx, fy, f ) f 2 ( xy, ) ( xy,,1) : A point in Euclidean space ( ) can be represented by fxy (,,1) ( xy,,1) 2 a homogeneous representation in Projective space ( P ) (3 numbers).

22 Homogeneous Coordinate 2D point =: 3D ray Projection plane ( X, YZ, ) λ( xy,,1) O ( fx, fy, f ) f λ( xy,,1) ( XYZ,, ) Homogeneous coordinate : 3D point lies in the 3D ray passing 2D image point.

23 3D Point Projection (Metric Space) 2D point =: 3D ray Projection plane ( X, YZ, ) λ( xy,,1) O f m ( fx, fy, f ) Z X Y ( xy,,1) ( fxfyf m, m, m) ( fm, fm, fm) Z Z

24 3D Point Projection (Pixel Space) O ( u, v ) w f m Projection plane h (0,0) ( u, v ) Z CCD sensor (mm) h (0,0) ( X, YZ, ) ( u, v ) w ( p, p ) x Image (pixel) y X Y ( XY,, Z) ( u, v ) ( fm, fm ) Z Z X Y u fx px v fy p Z Z u fx px X v fy py Y 1 1 Z Homogeneous representation y

25 Camera Intrinsic Parameter Pixel space Metric space O f m ( u, v ) Z ( X, YZ, ) u fx px X v fk y py Y 1 1 Z + Projection plane Camera intrinsic parameter : metric space to pixel space

26 2D Inverse Projection O f m ( u, v ) Z Projection plane ( X, YZ, ) 2D point == 3D ray K -1 u v 1 Note: arrow direction Pixel space Metric space u fx px X v fk y py Y 1 1 Z K -1 u X v Y 1 Z 3D ray The 3D point must lie in the 3D ray passing through the origin and 2D image point.

27 3D Point Projection (Pixel Space) Projection plane 3D point ( X, Y, Z) ( u, v ) ( u, v ) f m Focal length in meter ( X, YZ, ) w X h Y ( u, v ) ( fm, fm ) w Z h Z

28 Exercise What f to make the height of Eifel tower appear 960 pixel distance? 960 pix 21.8 mm 1280 pix 324 m size fm? Y h Y y f f m Z h Z 1500 m fm fm m

29 Exercise What f to make the height of Eifel tower appear 960 pixel distance? 960 pix 21.8 mm 1280 pix 324 m size fm = 50 mm Y h Y y f f m Z h Z Z? Z m Z

30 Exercise What Zp to make the height of Eifel tower appear twice of the person? h e h p 324 m fm = 50 mm Z p m Y f h e Y Z p f Y Z f p 2 Y Z h p f Z p p s.t. h p h e Z p m 234

31 Where Was I? 324 m 670 pix fm = 50 mm 0.9 m Z pix Circa 1984 Z 2 Y h Y h 1 Y y1 f fm Z1 fm m Z h Z h y Y h h 2 Y2 Y y2 f fm Z2 fm m Z h Z h y

32 Where Was I? Y h h 2 Y2 Y y2 f fm Z2 fm m Z h Z h y pix 250 pix Circa m 400m 600m 800m 1000m

33 Where Was I? 800 m

34 f Focal Length

35 f Focal Length

36 f Focal Length

37 Focal Length

38 Dolly Zoom (Vertigo Effect) (Jaws 1975)

39 Dolly Zoom Given focal length (fm=100mm), what Z100 to make the height of the person remain the same as fm=50mm? h p H p f100 = 100 mm Z 100? Z 50 =157.41m

40 Dolly Zoom Given focal length (fm=100mm), what Z100 to make the height of the person remain the same as fm=50mm? h p H p f100 = 100 mm h f Y Z 50 h f Y Z 100 Z 100? s.t. h h Z 50 =157.41m

41 Dolly Zoom Given focal length (fm=100mm), what Z100 to make the height of the person remain the same as fm=50mm? h p H p f100 = 100 mm h f Y Z 50 h f Y Z 100 Z 100? s.t. h h Z 50 =157.41m Z f f Z Z m 50

42 Dolly Zoom (Jaws 1975) Dolly Zoom (Vertigo Effect)

Single View Geometry. Camera model & Orientation + Position estimation. What am I?

Single View Geometry. Camera model & Orientation + Position estimation. What am I? Single View Geometry Camera model & Orientation + Position estimation What am I? Vanishing point Mapping from 3D to 2D Point & Line Goal: Point Homogeneous coordinates represent coordinates in 2 dimensions

More information

CIS 580, Machine Perception, Spring 2015 Homework 1 Due: :59AM

CIS 580, Machine Perception, Spring 2015 Homework 1 Due: :59AM CIS 580, Machine Perception, Spring 2015 Homework 1 Due: 2015.02.09. 11:59AM Instructions. Submit your answers in PDF form to Canvas. This is an individual assignment. 1 Camera Model, Focal Length and

More information

Single View Geometry. Camera model & Orientation + Position estimation. What am I?

Single View Geometry. Camera model & Orientation + Position estimation. What am I? Single View Geometry Camera model & Orientation + Position estimation What am I? Vanishing points & line http://www.wetcanvas.com/ http://pennpaint.blogspot.com/ http://www.joshuanava.biz/perspective/in-other-words-the-observer-simply-points-in-thesame-direction-as-the-lines-in-order-to-find-their-vanishing-point.html

More information

Camera Model and Calibration

Camera 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 information

Augmented Reality II - Camera Calibration - Gudrun Klinker May 11, 2004

Augmented Reality II - Camera Calibration - Gudrun Klinker May 11, 2004 Augmented Reality II - Camera Calibration - Gudrun Klinker May, 24 Literature Richard Hartley and Andrew Zisserman, Multiple View Geometry in Computer Vision, Cambridge University Press, 2. (Section 5,

More information

Robot Vision: Camera calibration

Robot 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 information

Camera Model and Calibration. Lecture-12

Camera Model and Calibration. Lecture-12 Camera Model and Calibration Lecture-12 Camera Calibration Determine extrinsic and intrinsic parameters of camera Extrinsic 3D location and orientation of camera Intrinsic Focal length The size of the

More information

Homogeneous Coordinates. Lecture18: Camera Models. Representation of Line and Point in 2D. Cross Product. Overall scaling is NOT important.

Homogeneous Coordinates. Lecture18: Camera Models. Representation of Line and Point in 2D. Cross Product. Overall scaling is NOT important. Homogeneous Coordinates Overall scaling is NOT important. CSED44:Introduction to Computer Vision (207F) Lecture8: Camera Models Bohyung Han CSE, POSTECH bhhan@postech.ac.kr (",, ) ()", ), )) ) 0 It is

More information

Image Formation. Antonino Furnari. Image Processing Lab Dipartimento di Matematica e Informatica Università degli Studi di Catania

Image Formation. Antonino Furnari. Image Processing Lab Dipartimento di Matematica e Informatica Università degli Studi di Catania Image Formation Antonino Furnari Image Processing Lab Dipartimento di Matematica e Informatica Università degli Studi di Catania furnari@dmi.unict.it 18/03/2014 Outline Introduction; Geometric Primitives

More information

Camera model and calibration

Camera model and calibration and calibration AVIO tristan.moreau@univ-rennes1.fr Laboratoire de Traitement du Signal et des Images (LTSI) Université de Rennes 1. Mardi 21 janvier 1 AVIO tristan.moreau@univ-rennes1.fr and calibration

More information

Rigid Body Motion and Image Formation. Jana Kosecka, CS 482

Rigid 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 information

Camera model and multiple view geometry

Camera 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 information

Computer Vision: Lecture 3

Computer Vision: Lecture 3 Computer Vision: Lecture 3 Carl Olsson 2019-01-29 Carl Olsson Computer Vision: Lecture 3 2019-01-29 1 / 28 Todays Lecture Camera Calibration The inner parameters - K. Projective vs. Euclidean Reconstruction.

More information

Camera models and calibration

Camera 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 information

Computer Vision cmput 428/615

Computer Vision cmput 428/615 Computer Vision cmput 428/615 Basic 2D and 3D geometry and Camera models Martin Jagersand The equation of projection Intuitively: How do we develop a consistent mathematical framework for projection calculations?

More information

CIS 580, Machine Perception, Spring 2016 Homework 2 Due: :59AM

CIS 580, Machine Perception, Spring 2016 Homework 2 Due: :59AM CIS 580, Machine Perception, Spring 2016 Homework 2 Due: 2015.02.24. 11:59AM Instructions. Submit your answers in PDF form to Canvas. This is an individual assignment. 1 Recover camera orientation By observing

More information

Introduction to Computer Vision. Introduction CMPSCI 591A/691A CMPSCI 570/670. Image Formation

Introduction 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 information

CS201 Computer Vision Camera Geometry

CS201 Computer Vision Camera Geometry CS201 Computer Vision Camera Geometry John Magee 25 November, 2014 Slides Courtesy of: Diane H. Theriault (deht@bu.edu) Question of the Day: How can we represent the relationships between cameras and the

More information

Lagrange Multipliers. Lagrange Multipliers. Lagrange Multipliers. Lagrange Multipliers. Lagrange Multipliers. Lagrange Multipliers

Lagrange Multipliers. Lagrange Multipliers. Lagrange Multipliers. Lagrange Multipliers. Lagrange Multipliers. Lagrange Multipliers In this section we present Lagrange s method for maximizing or minimizing a general function f(x, y, z) subject to a constraint (or side condition) of the form g(x, y, z) = k. Figure 1 shows this curve

More information

Projective Geometry and Camera Models

Projective Geometry and Camera Models /2/ Projective Geometry and Camera Models Computer Vision CS 543 / ECE 549 University of Illinois Derek Hoiem Note about HW Out before next Tues Prob: covered today, Tues Prob2: covered next Thurs Prob3:

More information

CS535 Fall Department of Computer Science Purdue University

CS535 Fall Department of Computer Science Purdue University Camera Models CS535 Fall 21 Daniel G Aliaga Daniel G. Aliaga Department of Computer Science Purdue University Biology 11 Some animals are capable of panoramic vision e.g., certain insects, crustaceans

More information

5LSH0 Advanced Topics Video & Analysis

5LSH0 Advanced Topics Video & Analysis 1 Multiview 3D video / Outline 2 Advanced Topics Multimedia Video (5LSH0), Module 02 3D Geometry, 3D Multiview Video Coding & Rendering Peter H.N. de With, Sveta Zinger & Y. Morvan ( p.h.n.de.with@tue.nl

More information

Camera Projection Models We will introduce different camera projection models that relate the location of an image point to the coordinates of the

Camera Projection Models We will introduce different camera projection models that relate the location of an image point to the coordinates of the Camera Projection Models We will introduce different camera projection models that relate the location of an image point to the coordinates of the corresponding 3D points. The projection models include:

More information

Module 4F12: Computer Vision and Robotics Solutions to Examples Paper 2

Module 4F12: Computer Vision and Robotics Solutions to Examples Paper 2 Engineering Tripos Part IIB FOURTH YEAR Module 4F2: Computer Vision and Robotics Solutions to Examples Paper 2. Perspective projection and vanishing points (a) Consider a line in 3D space, defined in camera-centered

More information

ECE-161C Cameras. Nuno Vasconcelos ECE Department, UCSD

ECE-161C Cameras. Nuno Vasconcelos ECE Department, UCSD ECE-161C Cameras Nuno Vasconcelos ECE Department, UCSD Image formation all image understanding starts with understanding of image formation: projection of a scene from 3D world into image on 2D plane 2

More information

Perspective projection and Transformations

Perspective projection and Transformations Perspective projection and Transformations The pinhole camera The pinhole camera P = (X,,) p = (x,y) O λ = 0 Q λ = O λ = 1 Q λ = P =-1 Q λ X = 0 + λ X 0, 0 + λ 0, 0 + λ 0 = (λx, λ, λ) The pinhole camera

More information

Pin Hole Cameras & Warp Functions

Pin 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 information

Computer Vision Projective Geometry and Calibration. Pinhole cameras

Computer Vision Projective Geometry and Calibration. Pinhole cameras Computer Vision Projective Geometry and Calibration Professor Hager http://www.cs.jhu.edu/~hager Jason Corso http://www.cs.jhu.edu/~jcorso. Pinhole cameras Abstract camera model - box with a small hole

More information

CS 664 Slides #9 Multi-Camera Geometry. Prof. Dan Huttenlocher Fall 2003

CS 664 Slides #9 Multi-Camera Geometry. Prof. Dan Huttenlocher Fall 2003 CS 664 Slides #9 Multi-Camera Geometry Prof. Dan Huttenlocher Fall 2003 Pinhole Camera Geometric model of camera projection Image plane I, which rays intersect Camera center C, through which all rays pass

More information

Cameras and Radiometry. Last lecture in a nutshell. Conversion Euclidean -> Homogenous -> Euclidean. Affine Camera Model. Simplified Camera Models

Cameras and Radiometry. Last lecture in a nutshell. Conversion Euclidean -> Homogenous -> Euclidean. Affine Camera Model. Simplified Camera Models Cameras and Radiometry Last lecture in a nutshell CSE 252A Lecture 5 Conversion Euclidean -> Homogenous -> Euclidean In 2-D Euclidean -> Homogenous: (x, y) -> k (x,y,1) Homogenous -> Euclidean: (x, y,

More information

DD2423 Image Analysis and Computer Vision IMAGE FORMATION. Computational Vision and Active Perception School of Computer Science and Communication

DD2423 Image Analysis and Computer Vision IMAGE FORMATION. Computational Vision and Active Perception School of Computer Science and Communication DD2423 Image Analysis and Computer Vision IMAGE FORMATION Mårten Björkman Computational Vision and Active Perception School of Computer Science and Communication November 8, 2013 1 Image formation Goal:

More information

Full Field Displacement and Strain Measurement. On a Charpy Specimen. Using Digital Image Correlation.

Full Field Displacement and Strain Measurement. On a Charpy Specimen. Using Digital Image Correlation. Full Field Displacement and Strain Measurement On a Charpy Specimen Using Digital Image Correlation. Chapter 1: Introduction to Digital Image Correlation D.I.C. The method of 3-D DIGITAL IMAGE CORRELATION

More information

Geometric camera models and calibration

Geometric 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 information

Jump Stitch Metadata & Depth Maps Version 1.1

Jump Stitch Metadata & Depth Maps Version 1.1 Jump Stitch Metadata & Depth Maps Version 1.1 jump-help@google.com Contents 1. Introduction 1 2. Stitch Metadata File Format 2 3. Coverage Near the Poles 4 4. Coordinate Systems 6 5. Camera Model 6 6.

More information

Robotics - Single view, Epipolar geometry, Image Features. Simone Ceriani

Robotics - Single view, Epipolar geometry, Image Features. Simone Ceriani Robotics - Single view, Epipolar geometry, Image Features Simone Ceriani ceriani@elet.polimi.it Dipartimento di Elettronica e Informazione Politecnico di Milano 12 April 2012 2/67 Outline 1 Pin Hole Model

More information

Computational Photography

Computational Photography Computational Photography Photography and Imaging Michael S. Brown Brown - 1 Part 1 Overview Photography Preliminaries Traditional Film Imaging (Camera) Part 2 General Imaging 5D Plenoptic Function (McMillan)

More information

Today. Stereo (two view) reconstruction. Multiview geometry. Today. Multiview geometry. Computational Photography

Today. 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

ROBOTICS 01PEEQW. Basilio Bona DAUIN Politecnico di Torino

ROBOTICS 01PEEQW. Basilio Bona DAUIN Politecnico di Torino ROBOTICS 01PEEQW DAUIN Politecnico di Torino Mobile & Service Robotics Sensors for Robotics 4 Vision Vision is the most important sense in humans and is becoming important also in robotics not expensive

More information

A Stereo Machine Vision System for. displacements when it is subjected to elasticplastic

A Stereo Machine Vision System for. displacements when it is subjected to elasticplastic A Stereo Machine Vision System for measuring three-dimensional crack-tip displacements when it is subjected to elasticplastic deformation Arash Karpour Supervisor: Associate Professor K.Zarrabi Co-Supervisor:

More information

Stereo SLAM. Davide Migliore, PhD Department of Electronics and Information, Politecnico di Milano, Italy

Stereo SLAM. Davide Migliore, PhD Department of Electronics and Information, Politecnico di Milano, Italy Stereo SLAM, PhD migliore@elet.polimi.it Department of Electronics and Information, Politecnico di Milano, Italy What is a Stereo Camera? Slide n 2 Do you remember the pin-hole camera? What is a Stereo

More information

Computer Vision Projective Geometry and Calibration. Pinhole cameras

Computer Vision Projective Geometry and Calibration. Pinhole cameras Computer Vision Projective Geometry and Calibration Professor Hager http://www.cs.jhu.edu/~hager Jason Corso http://www.cs.jhu.edu/~jcorso. Pinhole cameras Abstract camera model - box with a small hole

More information

Pin Hole Cameras & Warp Functions

Pin 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 information

CSE 252B: Computer Vision II

CSE 252B: Computer Vision II CSE 252B: Computer Vision II Lecturer: Serge Belongie Scribe: Sameer Agarwal LECTURE 1 Image Formation 1.1. The geometry of image formation We begin by considering the process of image formation when a

More information

Module 6: Pinhole camera model Lecture 32: Coordinate system conversion, Changing the image/world coordinate system

Module 6: Pinhole camera model Lecture 32: Coordinate system conversion, Changing the image/world coordinate system The Lecture Contains: Back-projection of a 2D point to 3D 6.3 Coordinate system conversion file:///d /...(Ganesh%20Rana)/MY%20COURSE_Ganesh%20Rana/Prof.%20Sumana%20Gupta/FINAL%20DVSP/lecture%2032/32_1.htm[12/31/2015

More information

The Lens. Refraction and The Lens. Figure 1a:

The Lens. Refraction and The Lens. Figure 1a: Lenses are used in many different optical devices. They are found in telescopes, binoculars, cameras, camcorders and eyeglasses. Even your eye contains a lens that helps you see objects at different distances.

More information

COSC579: Scene Geometry. Jeremy Bolton, PhD Assistant Teaching Professor

COSC579: 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 information

Assignment 2 : Projection and Homography

Assignment 2 : Projection and Homography TECHNISCHE UNIVERSITÄT DRESDEN EINFÜHRUNGSPRAKTIKUM COMPUTER VISION Assignment 2 : Projection and Homography Hassan Abu Alhaija November 7,204 INTRODUCTION In this exercise session we will get a hands-on

More information

Computer Vision Projective Geometry and Calibration

Computer Vision Projective Geometry and Calibration Computer Vision Projective Geometry and Calibration Professor Hager http://www.cs.jhu.edu/~hager Jason Corso http://www.cs.jhu.edu/~jcorso. Pinhole cameras Abstract camera model - box with a small hole

More information

Information page for written examinations at Linköping University TER2

Information 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 information

EM225 Projective Geometry Part 2

EM225 Projective Geometry Part 2 EM225 Projective Geometry Part 2 eview In projective geometry, we regard figures as being the same if they can be made to appear the same as in the diagram below. In projective geometry: a projective point

More information

Single-view 3D Reconstruction

Single-view 3D Reconstruction Single-view 3D Reconstruction 10/12/17 Computational Photography Derek Hoiem, University of Illinois Some slides from Alyosha Efros, Steve Seitz Notes about Project 4 (Image-based Lighting) You can work

More information

CS4670: Computer Vision

CS4670: Computer Vision CS467: Computer Vision Noah Snavely Lecture 13: Projection, Part 2 Perspective study of a vase by Paolo Uccello Szeliski 2.1.3-2.1.6 Reading Announcements Project 2a due Friday, 8:59pm Project 2b out Friday

More information

Introduction to Computer Vision

Introduction to Computer Vision Introduction to Computer Vision Michael J. Black Nov 2009 Perspective projection and affine motion Goals Today Perspective projection 3D motion Wed Projects Friday Regularization and robust statistics

More information

Introduction to Homogeneous coordinates

Introduction to Homogeneous coordinates Last class we considered smooth translations and rotations of the camera coordinate system and the resulting motions of points in the image projection plane. These two transformations were expressed mathematically

More information

Chapters 1-4: Summary

Chapters 1-4: Summary Chapters 1-4: Summary So far, we have been investigating the image acquisition process. Chapter 1: General introduction Chapter 2: Radiation source and properties Chapter 3: Radiation interaction with

More information

3D Sensing and Reconstruction Readings: Ch 12: , Ch 13: ,

3D 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 information

3D Geometry and Camera Calibration

3D Geometry and Camera Calibration 3D Geometry and Camera Calibration 3D Coordinate Systems Right-handed vs. left-handed x x y z z y 2D Coordinate Systems 3D Geometry Basics y axis up vs. y axis down Origin at center vs. corner Will often

More information

Camera Calibration. Schedule. Jesus J Caban. Note: You have until next Monday to let me know. ! Today:! Camera calibration

Camera 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 information

calibrated coordinates Linear transformation pixel coordinates

calibrated 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 information

Theory of Stereo vision system

Theory of Stereo vision system Theory of Stereo vision system Introduction Stereo vision is a technique aimed at extracting depth information of a scene from two camera images. Difference in pixel position in two image produces the

More information

Outline. ETN-FPI Training School on Plenoptic Sensing

Outline. 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 information

Agenda. Perspective projection. Rotations. Camera models

Agenda. Perspective projection. Rotations. Camera models Image formation Agenda Perspective projection Rotations Camera models Light as a wave + particle Light as a wave (ignore for now) Refraction Diffraction Image formation Digital Image Film Human eye Pixel

More information

Digital Imaging Study Questions Chapter 8 /100 Total Points Homework Grade

Digital Imaging Study Questions Chapter 8 /100 Total Points Homework Grade Name: Class: Date: Digital Imaging Study Questions Chapter 8 _/100 Total Points Homework Grade True/False Indicate whether the sentence or statement is true or false. 1. You can change the lens on most

More information

Pinhole Camera Model 10/05/17. Computational Photography Derek Hoiem, University of Illinois

Pinhole Camera Model 10/05/17. Computational Photography Derek Hoiem, University of Illinois Pinhole Camera Model /5/7 Computational Photography Derek Hoiem, University of Illinois Next classes: Single-view Geometry How tall is this woman? How high is the camera? What is the camera rotation? What

More information

Understanding Variability

Understanding Variability Understanding Variability Why so different? Light and Optics Pinhole camera model Perspective projection Thin lens model Fundamental equation Distortion: spherical & chromatic aberration, radial distortion

More information

CV: 3D to 2D mathematics. Perspective transformation; camera calibration; stereo computation; and more

CV: 3D to 2D mathematics. Perspective transformation; camera calibration; stereo computation; and more CV: 3D to 2D mathematics Perspective transformation; camera calibration; stereo computation; and more Roadmap of topics n Review perspective transformation n Camera calibration n Stereo methods n Structured

More information

September 18, B Math Test Chapter 1 Name: x can be expressed as: {y y 0, y R}.

September 18, B Math Test Chapter 1 Name: x can be expressed as: {y y 0, y R}. September 8, 208 62B Math Test Chapter Name: Part : Objective Questions [ mark each, total 2 marks]. State whether each of the following statements is TRUE or FALSE a) The mapping rule (x, y) (-x, y) represents

More information

Robotics - Projective Geometry and Camera model. Marcello Restelli

Robotics - Projective Geometry and Camera model. Marcello Restelli Robotics - Projective Geometr and Camera model Marcello Restelli marcello.restelli@polimi.it Dipartimento di Elettronica, Informazione e Bioingegneria Politecnico di Milano Ma 2013 Inspired from Matteo

More information

CS6670: Computer Vision

CS6670: 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 information

An introduction to 3D image reconstruction and understanding concepts and ideas

An 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 information

Viewing. Reading: Angel Ch.5

Viewing. Reading: Angel Ch.5 Viewing Reading: Angel Ch.5 What is Viewing? Viewing transform projects the 3D model to a 2D image plane 3D Objects (world frame) Model-view (camera frame) View transform (projection frame) 2D image View

More information

Cameras and Stereo CSE 455. Linda Shapiro

Cameras 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 information

Camera 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 Camera Models and Image Formation Srikumar Ramalingam School of Computing University of Utah srikumar@cs.utah.edu VisualFunHouse.com 3D Street Art Image courtesy: Julian Beaver (VisualFunHouse.com) 3D

More information

NAME :... Signature :... Desk no. :... Question Answer

NAME :... Signature :... Desk no. :... Question Answer Written test Tuesday 19th of December 2000. Aids allowed : All usual aids Weighting : All questions are equally weighted. NAME :................................................... Signature :...................................................

More information

3-D D Euclidean Space - Vectors

3-D D Euclidean Space - Vectors 3-D D Euclidean Space - Vectors Rigid Body Motion and Image Formation A free vector is defined by a pair of points : Jana Kosecka http://cs.gmu.edu/~kosecka/cs682.html Coordinates of the vector : 3D Rotation

More information

CSE328 Fundamentals of Computer Graphics

CSE328 Fundamentals of Computer Graphics CSE328 Fundamentals of Computer Graphics Hong Qin State University of New York at Stony Brook (Stony Brook University) Stony Brook, New York 794--44 Tel: (63)632-845; Fax: (63)632-8334 qin@cs.sunysb.edu

More information

CS 6320 Computer Vision Homework 2 (Due Date February 15 th )

CS 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 information

3D Sensing. 3D Shape from X. Perspective Geometry. Camera Model. Camera Calibration. General Stereo Triangulation.

3D Sensing. 3D Shape from X. Perspective Geometry. Camera Model. Camera Calibration. General Stereo Triangulation. 3D Sensing 3D Shape from X Perspective Geometry Camera Model Camera Calibration General Stereo Triangulation 3D Reconstruction 3D Shape from X shading silhouette texture stereo light striping motion mainly

More information

Review Exercise. 1. Determine vector and parametric equations of the plane that contains the

Review Exercise. 1. Determine vector and parametric equations of the plane that contains the Review Exercise 1. Determine vector and parametric equations of the plane that contains the points A11, 2, 12, B12, 1, 12, and C13, 1, 42. 2. In question 1, there are a variety of different answers possible,

More information

3. The domain of a function of 2 or 3 variables is a set of pts in the plane or space respectively.

3. The domain of a function of 2 or 3 variables is a set of pts in the plane or space respectively. Math 2204 Multivariable Calculus Chapter 14: Partial Derivatives Sec. 14.1: Functions of Several Variables I. Functions and Variables A. Def n : Suppose D is a set of n-tuples of real numbers (x 1, x 2,

More information

Rendering. Generate an image from geometric primitives II. Rendering III. Modeling IV. Animation. (Michael Bostock, CS426, Fall99)

Rendering. Generate an image from geometric primitives II. Rendering III. Modeling IV. Animation. (Michael Bostock, CS426, Fall99) 1 Course Syllabus 2 I. Image processing 3D Adam Finkelstein Princeton University C0S 426, Fall 2001 II. III. Modeling IV. Animation Image Processing (Rusty Coleman, CS426, Fall99) (Michael Bostock, CS426,

More information

Specifying Complex Scenes

Specifying Complex Scenes Transformations Specifying Complex Scenes (x,y,z) (r x,r y,r z ) 2 (,,) Specifying Complex Scenes Absolute position is not very natural Need a way to describe relative relationship: The lego is on top

More information

solidthinking User Interface

solidthinking User Interface Lesson 1 solidthinking User Interface This lesson introduces you to the solidthinking interface. The functions described represent the tools necessary for effectively managing the modeling of a project.

More information

Lecture 7 Measurement Using a Single Camera. Lin ZHANG, PhD School of Software Engineering Tongji University Fall 2016

Lecture 7 Measurement Using a Single Camera. Lin ZHANG, PhD School of Software Engineering Tongji University Fall 2016 Lecture 7 Measurement Using a Single Camera Lin ZHANG, PhD School of Software Engineering Tongji University Fall 2016 If I have an image containing a coin, can you tell me the diameter of that coin? Contents

More information

3D Rendering. Course Syllabus. Where Are We Now? Rendering. 3D Rendering Example. Overview. Rendering. I. Image processing II. Rendering III.

3D Rendering. Course Syllabus. Where Are We Now? Rendering. 3D Rendering Example. Overview. Rendering. I. Image processing II. Rendering III. Course Syllabus I. Image processing II. Rendering III. Modeling 3D Rendering Rendering I. Animation (Michael Bostock, CS426, Fall99) Image Processing Adam Finkelstein Princeton University COS 426, Spring

More information

Realistic Camera Model

Realistic Camera Model Realistic Camera Model Shan-Yung Yang November 2, 2006 Shan-Yung Yang () Realistic Camera Model November 2, 2006 1 / 25 Outline Introduction Lens system Thick lens approximation Radiometry Sampling Assignment

More information

Announcements. Mosaics. How to do it? Image Mosaics

Announcements. Mosaics. How to do it? Image Mosaics Announcements Mosaics Project artifact voting Project 2 out today (help session at end of class) http://www.destination36.com/start.htm http://www.vrseattle.com/html/vrview.php?cat_id=&vrs_id=vrs38 Today

More information

Computer Vision, Laboratory session 1

Computer Vision, Laboratory session 1 Centre for Mathematical Sciences, january 2007 Computer Vision, Laboratory session 1 Overview In this laboratory session you are going to use matlab to look at images, study projective geometry representations

More information

Vision Review: Image Formation. Course web page:

Vision 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 information

Computer Vision I - Algorithms and Applications: Multi-View 3D reconstruction

Computer Vision I - Algorithms and Applications: Multi-View 3D reconstruction Computer Vision I - Algorithms and Applications: Multi-View 3D reconstruction Carsten Rother 09/12/2013 Computer Vision I: Multi-View 3D reconstruction Roadmap this lecture Computer Vision I: Multi-View

More information

Introduction to 3D Machine Vision

Introduction to 3D Machine Vision Introduction to 3D Machine Vision 1 Many methods for 3D machine vision Use Triangulation (Geometry) to Determine the Depth of an Object By Different Methods: Single Line Laser Scan Stereo Triangulation

More information

For 3CCD/3CMOS/4CCD Line Scan Cameras. Designed to be suitable for PRISM based 3CCD/CMOS/4CCD line scan cameras

For 3CCD/3CMOS/4CCD Line Scan Cameras. Designed to be suitable for PRISM based 3CCD/CMOS/4CCD line scan cameras BV-L series lenses For 3CCD/3CMOS/4CCD Line Scan Cameras Common Features Designed to be suitable for PRISM based 3CCD/CMOS/4CCD line scan cameras New optics design to improve the longitudinal chromatic

More information

Epipolar geometry. x x

Epipolar geometry. x x Two-view geometry Epipolar geometry X x x Baseline line connecting the two camera centers Epipolar Plane plane containing baseline (1D family) Epipoles = intersections of baseline with image planes = projections

More information

Computer Vision CS 776 Fall 2018

Computer Vision CS 776 Fall 2018 Computer Vision CS 776 Fall 2018 Cameras & Photogrammetry 1 Prof. Alex Berg (Slide credits to many folks on individual slides) Cameras & Photogrammetry 1 Albrecht Dürer early 1500s Brunelleschi, early

More information

More Mosaic Madness. CS194: Image Manipulation & Computational Photography. Steve Seitz and Rick Szeliski. Jeffrey Martin (jeffrey-martin.

More Mosaic Madness. CS194: Image Manipulation & Computational Photography. Steve Seitz and Rick Szeliski. Jeffrey Martin (jeffrey-martin. More Mosaic Madness Jeffrey Martin (jeffrey-martin.com) CS194: Image Manipulation & Computational Photography with a lot of slides stolen from Alexei Efros, UC Berkeley, Fall 2018 Steve Seitz and Rick

More information

Scene Modeling for a Single View

Scene Modeling for a Single View on to 3D Scene Modeling for a Single View We want real 3D scene walk-throughs: rotation translation Can we do it from a single photograph? Reading: A. Criminisi, I. Reid and A. Zisserman, Single View Metrology

More information

CS 563 Advanced Topics in Computer Graphics Camera Models. by Kevin Kardian

CS 563 Advanced Topics in Computer Graphics Camera Models. by Kevin Kardian CS 563 Advanced Topics in Computer Graphics Camera Models by Kevin Kardian Introduction Pinhole camera is insufficient Everything in perfect focus Less realistic Different camera models are possible Create

More information

Face Recognition At-a-Distance Based on Sparse-Stereo Reconstruction

Face Recognition At-a-Distance Based on Sparse-Stereo Reconstruction Face Recognition At-a-Distance Based on Sparse-Stereo Reconstruction Ham Rara, Shireen Elhabian, Asem Ali University of Louisville Louisville, KY {hmrara01,syelha01,amali003}@louisville.edu Mike Miller,

More information

M12VD1240IR M123VD4510IR M125VD3410IRCS M125VD922IRCS

M12VD1240IR M123VD4510IR M125VD3410IRCS M125VD922IRCS Sports, an area of -Focal Lenses Non Distortion Lens Machine Vision Lens ITS Lens Automotive Lens Fisheye Lens Model name coding rule Terminology M12VM1240IR M123VM4510IR M125VM3410IR M125VM922IR Back

More information

Scene Reconstruction from Uncontrolled Motion using a Low Cost 3D Sensor

Scene Reconstruction from Uncontrolled Motion using a Low Cost 3D Sensor Scene Reconstruction from Uncontrolled Motion using a Low Cost 3D Sensor Pierre Joubert and Willie Brink Applied Mathematics Department of Mathematical Sciences University of Stellenbosch, South Africa

More information