Image Formation. 2. Camera Geometry. Focal Length, Field Of View. Pinhole Camera Model. Computer Vision. Zoltan Kato
|
|
- Frederick Wilson
- 6 years ago
- Views:
Transcription
1 Image Formation 2. amera Geometr omuter Vision oltan Kato htt:// seged.hu/~kato/ 3D Scene Surace Light (Energ) Source inhole Lens Imaging lane World Otics Sensor Signal amera: Sec & ose 2D Image Slide adoted rom higang hu omuter Vision - S I676 2 inhole amera Model Otical Ais inhole lens in-hole is the basis or most grahics and vision Derived rom hsical construction o earlameras Mathematics is ver straightorward 3D World rojected to 2D Image Image inverted, sie reduced Image is a 2D lane: No direct deth inormation ersective rojection called the ocal length o the lens Image lane Focal Length, Field O View onsider case with object on the otical ais: viewoint Otical ais: the direction o imaging Image lane: a lane erendicular to the otical ais enter o rojection (inhole), ocal oint, viewoint, nodal oint Focal length: distance rom ocal oint to the image lane FOV : Field o View viewing angles in horiontal and vertical directions Increasing will enlarge igures, but decrease FOV Image lane Out o view Slide adoted rom higang hu omuter Vision - S I676 3 Slide adoted rom higang hu omuter Vision - S I676 4
2 Equivalent Geometr onsider case with object on the otical ais: More convenienith uright image: ersective rojection omute the image coordinates o in terms o the camera coordinates o. (, ) X ( X,, ) rojection lane Equivalent mathematicall Slide adoted rom higang hu omuter Vision - S I676 5 Origin o camera at center o rojection ais along otical ais Image lane at ; X and Slide adoted rom higang hu omuter Vision - S I676 6 inhole camera model Reverse rojection Given a center o rojection and image coordinates o a oint, it is not ossible to recover the 3D deth o the oint rom a single image. (X,,) can be anwhere along this line (,) X a X X X a X All oints on this line have image coordinates (,). In general, at least two images o the same oint taken rom two dierent locations are required to recover deth. 7 Slide adoted rom higang hu omuter Vision - S I676 8
3 Stereo Geometr l (X,,) entral rojection Ras Vergence Angle Object oint Deth obtained b triangulation orresondence roblem: l and r must corresond to the let and right rojections o, resectivel. Slide adoted rom higang hu omuter Vision - S I676 9 r inhole camera image straight line sie arallelism/angle shae shae o lanes deth Amsterdam : what do ou see in this icture? Slide adoted rom higang hu omuter Vision - S I676 0 hoto b Robert Kosara, robert@kosara.net htt:// inhole camera image inhole camera image Amsterdam Amsterdam straight line sie arallelism/angle shae shae o lanes deth hoto b Robert Kosara, robert@kosara.net htt:// straight line sie arallelism/angle shae shae o lanes deth hoto b Robert Kosara, robert@kosara.net htt:// Slide adoted rom higang hu omuter Vision - S I676 Slide adoted rom higang hu omuter Vision - S I676 2
4 inhole camera image Amsterdam hoto b Robert Kosara, robert@kosara.net htt:// straight line sie arallelism/angle shae shae o lanes deth Slide adoted rom higang hu omuter Vision - S I676 3 inhole camera image Amsterdam hoto b Robert Kosara, robert@kosara.net htt:// inhole camera image Amsterdam straight line sie arallelism/angle shae shae o lanes arallel to image deth Slide adoted rom higang hu omuter Vision - S I676 5 straight line sie arallelism/angle shae shae o lanes deth Slide adoted rom higang hu omuter Vision - S I676 4 inhole camera image straight line sie arallelism/angle shae shae o lanes arallel to image Deth? stereo motion sie structure Amsterdam: what do ou see? - We see satial shaes rather than individual iels - Knowledge: to-down vision belongs to human - Stereo &Motion most successul in 3D V & alication - ou can see it but ou don't know how Slide adoted rom higang hu omuter Vision - S I676 6 hoto b Robert Kosara, robert@kosara.net htt:// hoto b Robert Kosara, robert@kosara.net htt://
5 amera arameters im Image rame ( im, im ) im D oordinate Sstems Frame coordinates ( im, im ) iels Image coordinates (,) amera coordinates (X,,) World coordinates (X w, w, w ) amera arameters Intrinsic arameters (o the camera and the rame grabber): link the rame coordinates o an image oinith its corresonding camera coordinates Etrinsic arameters: deine the location and orientation o the camera coordinate sstem with resect to the world coordinate sstem ose / amera X X w w Object / World w w Intrinsic arameters () X From rame to image Image center Directions o aes iel sie Intrinsic arameters Sie: (s,s ) im X X + o s a + o (o,o ): rincial oint (image center) (s,s ): eective sie o the iel : ocal length (,,) o (0,0) o iel ( im, im ) im s o o X Slide adoted rom higang hu omuter Vision - S I676 7 Slide adoted rom higang hu omuter Vision - S I676 8 Intrinsic arameters (2) amera rotation and translation (, ) d d k,k2 ( + k r ( + k r Lens Distortions 2 2 (d, d) + k 2 + k 2 r r 4 4 ) ) Modeled as simle radial distortions r 2 d 2 +d 2 (d, d) distorted oints k, k2: distortion coeicients A model with k2 0 is still accurate or a D sensor o with ~5 iels distortion on the outer boundar im ( im, im ) im From World to amera R w + T T Etrinsic arameters A 3D translation vector, T, describing the relative locations o the origins o the two coordinate sstems A 33 rotation matri, R, an orthogonal matri that brings the corresonding aes o the two sstems onto each other X X w T T T R R, i. e. RR R R I w w w Slide adoted rom higang hu omuter Vision - S I676 9 Slide adoted rom higang hu omuter Vision - S I676 20
6 Etrinsic arameters: [R T] X cam R 0 X X ~ cam R ~ X R R X 0 K[ R T] T R ~ R X ~ [ 0] ( - ~ ) K I X KR[ I ~ cam ]X ~ rojection center in world coordinate rame. 2 Finite rojective camera K s KR[ I - ~ ] γ arctan(/s) degree o reedom (5+3+3) non-singular decomose in K,R,? [ M 4 ] ~ [ K, R] RQ( M) M 4 ~ rojection center in world coordinate rame. {inite cameras}{ 43 det M 0} I rank 3, but rank M<3, then camera is at ininit s skew or D/MOS, alwas s0 22 amera matri decomosition olumn vectors Finding the camera center 0 Algebraicall, ma be obtained as: (use SVD to ind null-sace) X det( [ 2,3, 4] ) det( [,3, 4] ) det( [,, ]) W det( [,, ]) Finding the camera orientation and internal arameters [ ] [ ] Image oints corresonding to X,, directions and origin ( 4 ) M KR (use RQ decomosition ~QR) (i onl QR, invert) ( Q R ) - R - - Q 23 24
7 Row vectors 0 T 2T 3T X Aine cameras 0 w T 2T 3T X note:, 2 deendent on image rearametriation Weak ersective rojection Weak ersective rojection Average deth is much larger than the relative distance between an two scene oints measured along the otical ais (, ) X (X,,) α r r 0 t t / k T 2T α 2 (7 degrees o reedom) 0 A sequence o two transormations Orthograhic rojection : arallel ras Isotroic scaling : / Linear Model reserve angles and shaes X Slide adoted rom higang hu omuter Vision - S I
8 Aine camera α r r 0 t t / k T s T A α 2 m m 0 m m 0 m m 0 t t 2 3 A A [ 3 3 aine] 0 0 0[ 4 4 aine] (8do). Aine cameracamera with rincial lane coinciding with the lan at ininit Π 2. Aine camera mas arallel lines to arallel lines 3. No center o rojection, but direction o rojection A D0 (oint on Π ) 29 Slides adoted rom: S 395/495-25: Sring 2004 IBMR: R 3 R 2 and 3 2 : The rojective amera Matri Jack Tumblin jet@cs.northwestern.edu ameras Revisited ameras Revisited lent o Terminolog: Image lane or Focal lane Focal Distance amera enter rincial oint rincial Ais rincial lane amera oords (,, ) Image oords (,, ) lent o Terminolog: Image lane or Focal lane Focal Distance amera enter rincial oint rincial Ais rincial lane amera oords (,, ) Image oords (,, )
9 ameras Revisited ameras Revisited lent o Terminolog: Image lane or Focal lane Focal Distance amera enter rincial oint rincial Ais rincial lane amera oords (,, ) Image oords (,, ) lent o Terminolog: Image lane or Focal lane Focal Distance amera enter rincial oint rincial Ais rincial lane amera oords (,, ) Image oords (,, ) ameras Revisited ameras Revisited lent o Terminolog: Image lane or Focal lane Focal Distance amera enter rincial oint rincial Ais rincial lane amera oords (,, ) Image oords (,, ) lent o Terminolog: Image lane or Focal lane Focal Distance amera enter rincial oint rincial Ais rincial lane amera oords (,, ) Image oords (,, )
10 ameras Revisited ameras Revisited lent o Terminolog: Image lane or Focal lane Focal Distance amera enter rincial oint rincial Ais rincial lane amera oords (,, ) Image oords (,, ) [0,0,0] lent o Terminolog: Image lane or Focal lane Focal Distance amera enter rincial oint rincial Ais rincial lane amera oords (,, ) Image oords (,, ) ameras Revisited Basic amera 0 : 3 2 (oror camera R 3 ) Goal: Formalie rojective 3D 2D maing Homogeneous coords handles ininities well: rojective cameras ( convergent ee ras) Aine cameras (arallel ee ras) omosed, controlled as matri roduct Recall Euclidian R 3 R 2 : / / (Much Better: 3 2 ) Basic amera 0 is a 34 matri: α s 0 0 α K (33 submatri) [K 0] 0 0 X Non-square iels? change scaling (α, α ) arallelogram iels? set nonero skew s K matri: : (internal) camera calib.. matri
11 omlete amera Matri K matri: : internal camera calib.. matri R T T matri: : eternal camera calib.. matri T matri: Translate world to cam. origin R matri: 3D rotate world to it cam. aes ombine: write ( 0 R T) T) X X as X X Inut: X 3D World Sace X (world sace) Outut: 2D amera Image (camera sace) The ieces o amera Matri X X, or w w olumns o matri image o world-sace aes:, 2, 3 image o,, ais vanishing oints Direction D [ 0 0 0] T oint on 3 s s ais, at iniinit D st column o. Reeat or and aes. 4 image o the world-sace origin t. roo: let D [0 0 0 ]T direction to world origin D 4 th column o image o origin t., or The ieces o amera Matri X X, or w w Rows o matri: camera lanes in world sace row T image -ais lane row 2 2T image -ais lane row 3T camera s s rincial lane, or T 2T 3T The ieces o amera Matri X X, or w w Rows o matri: lanes in world sace row world lane whose image is 0 row 2 2 world lane whose image is 0 row 3 3 camera s s rincial lane, or T 2T 3T Wh? Recall that in 3, oint X is on lane π i and onl i πt X X 0, so
12 The ieces o amera Matri X X, or w w Rows o matri: lanes in world sace row world lane whose image is 0 row 2 2 world lane whose image is 0 row 3 3 camera s s rincial lane, or T 2T 3T The ieces o amera Matri X X, or w w, or Rows o matri: lanes in world sace row image s s 0 lane row 2 2 image s s 0 lane areul! Shiting the image origin b, shits the 0,0 lanes! 2T 3T T The ieces o amera Matri The ieces o amera Matri X X, or w w, or Rows o matri: lanes in world sace row image s s 0 lane row 2 2 image s s 0 lane row 3 3 camera s s rincial lane T 2T 3T X X, or w w, or Rows o matri: lanes in world sace row image -ais lane row 2 2 image -ais lane row 3 3 camera s s rincial lane rinci.. lane 3 [ ] T its normal direction: [ ] T Wh is it normal? It s s the world-sace 3 direction o the ais T 2T 3T rincial lane 3
13 The ieces o amera Matri X X, or w w M rincial Ais Vector (( c ) in world sace:, or Normal o rincial lane: m 3 [ Scaling Ambiguous direction!! +/- m 3? Solution: use det(m) m 3 as ront o camera rincial oint in image sace: image o (ininit oint on ais m 3 ) M m 3 0 (isserman 33 ] T isserman book renames as 0 ) m T m 2T m 3T 0 4 The ieces o amera Matri X X, or w w Where is camera in world sace? at : amera center is at camera origin( c,, )0 ~ amera transorms world-sace oint to 0 ~ But how do we ind? It is the Null Sace o : ~ 0 ~ (solve or. SVD works, but here s s an easier wa:) amera s s osition in the World: ~ -M - 4 ~ m T m 2T m 3T 4 M
Camera Models. Acknowledgements Used slides/content with permission from
Camera Models Acknowledgements Used slides/content with ermission rom Marc Polleeys or the slides Hartley and isserman: book igures rom the web Matthew Turk: or the slides Single view geometry Camera model
More informationRemember: The equation of projection. Imaging Geometry 1. Basic Geometric Coordinate Transforms. C306 Martin Jagersand
Imaging Geometr 1. Basic Geometric Coordinate Transorms emember: The equation o rojection Cartesian coordinates: (,, z) ( z, z ) C36 Martin Jagersand How do we develo a consistent mathematical ramework
More information3D Computer Vision Camera Models
3D Comuter Vision Camera Models Nassir Navab based on a course given at UNC by Marc Pollefeys & the book Multile View Geometry by Hartley & Zisserman July 2, 202 chair for comuter aided medical rocedures
More informationCalibration Issues. Linear Models. Interest Point Detection + Description Algorithms
Calibration Issues Linear Models Homograhy estimation H Eiolar geometry F, E Interior camera arameters K Eterior camera arameters R,t Camera ose R,t h 2 H h 3 h 2 3 4 y ~ 2 22 23 24 3 32 33 34 Interest
More informationGeometry of a single camera. Odilon Redon, Cyclops, 1914
Geometr o a single camera Odilon Redon, Cclops, 94 Our goal: Recover o 3D structure Recover o structure rom one image is inherentl ambiguous??? Single-view ambiguit Single-view ambiguit Rashad Alakbarov
More informationCS 450: COMPUTER GRAPHICS 2D TRANSFORMATIONS SPRING 2016 DR. MICHAEL J. REALE
CS 45: COMUTER GRAHICS 2D TRANSFORMATIONS SRING 26 DR. MICHAEL J. REALE INTRODUCTION Now that we hae some linear algebra under our resectie belts, we can start ug it in grahics! So far, for each rimitie,
More informationAnnouncements. Equation of Perspective Projection. Image Formation and Cameras
Announcements Image ormation and Cameras Introduction to Computer Vision CSE 52 Lecture 4 Read Trucco & Verri: pp. 22-4 Irfanview: http://www.irfanview.com/ is a good Windows utilit for manipulating images.
More informationStructure from Motion
04/4/ Structure from Motion Comuter Vision CS 543 / ECE 549 University of Illinois Derek Hoiem Many slides adated from Lana Lazebnik, Silvio Saverese, Steve Seitz his class: structure from motion Reca
More informationTo Do. Computer Graphics (Fall 2004) Course Outline. Course Outline. Motivation. Motivation
Comuter Grahics (Fall 24) COMS 416, Lecture 3: ransformations 1 htt://www.cs.columbia.edu/~cs416 o Do Start (thinking about) assignment 1 Much of information ou need is in this lecture (slides) Ask A NOW
More information521466S Machine Vision Exercise #1 Camera models
52466S Machine Vision Exercise # Camera models. Pinhole camera. The perspective projection equations or a pinhole camera are x n = x c, = y c, where x n = [x n, ] are the normalized image coordinates,
More informationRealtime 3D Computer Graphics Virtual Reality
Realtime 3D Comuter Grahics Virtual Realit Viewing an rojection Classical an General Viewing Transformation Pieline CPU CPU Pol. Pol. DL DL Piel Piel Per Per Verte Verte Teture Teture Raster Raster Frag
More informationPerception of Shape from Shading
1 Percetion of Shae from Shading Continuous image brightness variation due to shae variations is called shading Our ercetion of shae deends on shading Circular region on left is erceived as a flat disk
More informationComputer Graphics. Viewing. Fundamental Types of Viewing. Perspective views. Parallel views. October 12, finite COP (center of projection)
Comuter Grahics Viewing October 2, 25 htt://www.hallm.ac.kr/~sunkim/teach/25/cga Funamental Tes of Viewing Persective views finite COP (center of rojection) Parallel views COP at infinit DOP (irection
More information3D Geometry and Camera Calibration
3D Geometr and Camera Calibration 3D Coordinate Sstems Right-handed vs. left-handed 2D Coordinate Sstems ais up vs. ais down Origin at center vs. corner Will often write (u, v) for image coordinates v
More informationAnnouncements. The equation of projection. Image Formation and Cameras
Announcements Image ormation and Cameras Introduction to Computer Vision CSE 52 Lecture 4 Read Trucco & Verri: pp. 5-4 HW will be on web site tomorrow or Saturda. Irfanview: http://www.irfanview.com/ is
More informationL ENSES. Lenses Spherical refracting surfaces. n 1 n 2
Lenses 2 L ENSES 2. Sherical reracting suraces In order to start discussing lenses uantitatively, it is useul to consider a simle sherical surace, as shown in Fig. 2.. Our lens is a semi-ininte rod with
More informationLast time: Disparity. Lecture 11: Stereo II. Last time: Triangulation. Last time: Multi-view geometry. Last time: Epipolar geometry
Last time: Disarity Lecture 11: Stereo II Thursday, Oct 4 CS 378/395T Prof. Kristen Grauman Disarity: difference in retinal osition of same item Case of stereo rig for arallel image lanes and calibrated
More informationGeometric Model of Camera
Geometric Model of Camera Dr. Gerhard Roth COMP 42A Winter 25 Version 2 Similar Triangles 2 Geometric Model of Camera Perspective projection P(X,Y,Z) p(,) f X Z f Y Z 3 Parallel lines aren t 4 Figure b
More informationMAN-522: COMPUTER VISION SET-2 Projections and Camera Calibration
MAN-522: COMPUTER VISION SET-2 Projections and Camera Calibration Image formation How are objects in the world captured in an image? Phsical parameters of image formation Geometric Tpe of projection Camera
More informationComputer Graphics. Geometric Transformations
Contents coordinate sstems scalar values, points, vectors, matrices right-handed and left-handed coordinate sstems mathematical foundations transformations mathematical descriptions of geometric changes,
More informationComputer Graphics. Geometric Transformations
Computer Graphics Geometric Transformations Contents coordinate sstems scalar values, points, vectors, matrices right-handed and left-handed coordinate sstems mathematical foundations transformations mathematical
More informationGabriel Taubin. Desktop 3D Photography
Sring 06 ENGN50 --- D Photograhy Lecture 7 Gabriel Taubin Brown University Deskto D Photograhy htt://www.vision.caltech.edu/bouguetj/iccv98/.index.html D triangulation: ray-lane Intersection lane ray intersection
More informationMAPI Computer Vision. Multiple View Geometry
MAPI Computer Vision Multiple View Geometry Geometry o Multiple Views 2- and 3- view geometry p p Kpˆ [ K R t]p Geometry o Multiple Views 2- and 3- view geometry Epipolar Geometry The epipolar geometry
More informationHomogeneous 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 informationCamera calibration. Robotic vision. Ville Kyrki
Camera calibration Robotic vision 19.1.2017 Where are we? Images, imaging Image enhancement Feature extraction and matching Image-based tracking Camera models and calibration Pose estimation Motion analysis
More informationD-Calib: Calibration Software for Multiple Cameras System
D-Calib: Calibration Software for Multiple Cameras Sstem uko Uematsu Tomoaki Teshima Hideo Saito Keio Universit okohama Japan {u-ko tomoaki saito}@ozawa.ics.keio.ac.jp Cao Honghua Librar Inc. Japan cao@librar-inc.co.jp
More informationHomographies and Mosaics
Tri reort Homograhies and Mosaics Jeffrey Martin (jeffrey-martin.com) CS94: Image Maniulation & Comutational Photograhy with a lot of slides stolen from Alexei Efros, UC Berkeley, Fall 06 Steve Seitz and
More informationWhat is Perspective?
Fall 25 M ss =M screen * M ersective * M view What is Persective? A mechanism for ortraing 3D in 2D True Persective corresons to rojection onto a lane True Persective corresons to an ieal camera image
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 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 informationPerspective Projection Transformation
Perspective Projection Transformation Where does a point of a scene appear in an image?? p p Transformation in 3 steps:. scene coordinates => camera coordinates. projection of camera coordinates into image
More informationLecture 3: Camera Calibration, DLT, SVD
Computer Vision Lecture 3 23--28 Lecture 3: Camera Calibration, DL, SVD he Inner Parameters In this section we will introduce the inner parameters of the cameras Recall from the camera equations λx = P
More informationWave optics treats light as a wave and uses a similar analytical framework as sound waves and other mechanical waves.
Otics 1. Intro: Models o Light 2. The Ray Model 1. Relection 2. Reraction 3. Total Internal Relection 3. Images 1. The lane mirror 2. Sherical Mirrors (concave) 4. Lenses: an introduction 1. The arameters
More informationBasic principles - Geometry. Marc Pollefeys
Basic principles - Geometr Marc Pollees Basic principles - Geometr Projective geometr Projective, Aine, Homograph Pinhole camera model and triangulation Epipolar geometr Essential and Fundamental Matri
More informationPHOTOINTERPRETATION AND SMALL SCALE STEREOPLOTTING WITH DIGITALLY RECTIFIED PHOTOGRAPHS WITH GEOMETRICAL CONSTRAINTS 1
PHOTOINTERPRETATION AND SMALL SALE STEREOPLOTTING WITH DIGITALL RETIFIED PHOTOGRAPHS WITH GEOMETRIAL ONSTRAINTS Gabriele FANGI, Gianluca GAGLIARDINI, Eva Savina MALINVERNI Universit of Ancona, via Brecce
More informationGeometry of image formation
Geometr of image formation Tomáš Svoboda, svoboda@cmp.felk.cvut.c ech Technical Universit in Prague, enter for Machine Perception http://cmp.felk.cvut.c Last update: November 0, 2008 Talk Outline Pinhole
More informationCS 428: Fall Introduction to. Geometric Transformations. Andrew Nealen, Rutgers, /15/2010 1
CS 428: Fall 21 Introduction to Comuter Grahics Geometric Transformations Andrew Nealen, Rutgers, 21 9/15/21 1 Toic overview Image formation and OenGL (last week) Modeling the image formation rocess OenGL
More informationRobert Collins CSE486, Penn State. Robert Collins CSE486, Penn State. Image Point Y. O.Camps, PSU. Robert Collins CSE486, Penn State.
Stereo Vision Inerring depth rom images taken at the same time b two or more s. Lecture 08: Introduction to Stereo Reading: T&V Section 7.1 Scene Point Image Point p = (,,) O Basic Perspective Projection
More informationWhat and Why Transformations?
2D transformations What and Wh Transformations? What? : The geometrical changes of an object from a current state to modified state. Changing an object s position (translation), orientation (rotation)
More informationCS F-07 Objects in 2D 1
CS420-2010F-07 Objects in 2D 1 07-0: Representing Polgons We want to represent a simple polgon Triangle, rectangle, square, etc Assume for the moment our game onl uses these simple shapes No curves for
More informationCS5620 Intro to Computer Graphics
CS560 Reminder - Pieline Polgon at [(,9), (5,7), (8,9)] Polgon at [ ] D Model Transformations Reminder - Pieline Object Camera Cli Normalied device Screen Inut: Polgons in normalied device Model-view Projection
More information3D Viewing and Projec5on. Taking Pictures with a Real Camera. Steps: Graphics does the same thing for rendering an image for 3D geometric objects
3D Vieing and Projec5on Taking Pictures ith a Real Camera Steps: Iden5 interes5ng objects Rotate and translate the camera to desired viepoint Adjust camera seings such as ocal length Choose desired resolu5on
More informationIoannis PITAS b. TIMC-IMAG Grenoble laboratory, UMR CNRS 5525, Domaine de la Merci, LA TRONCHE Cedex, FRANCE,
ylindrical Surface Localization in Monocular Vision William PUEH a Jean-Marc HASSERY a and Ioannis PITAS b a TIM-IMAG Grenoble laboratory, UMR NRS 5525, Domaine de la Merci, 38706 LA TRONHE edex, FRANE,
More informationRobotics - 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 informationMotivation. What we ve seen so far. Demo (Projection Tutorial) Outline. Projections. Foundations of Computer Graphics
Foundations of Computer Graphics Online Lecture 5: Viewing Orthographic Projection Ravi Ramamoorthi Motivation We have seen transforms (between coord sstems) But all that is in 3D We still need to make
More informationLearning Motion Patterns in Crowded Scenes Using Motion Flow Field
Learning Motion Patterns in Crowded Scenes Using Motion Flow Field Min Hu, Saad Ali and Mubarak Shah Comuter Vision Lab, University of Central Florida {mhu,sali,shah}@eecs.ucf.edu Abstract Learning tyical
More informationDetermining the 2d transformation that brings one image into alignment (registers it) with another. And
Last two lectures: Representing an image as a weighted combination of other images. Toda: A different kind of coordinate sstem change. Solving the biggest problem in using eigenfaces? Toda Recognition
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 informationP Z. parametric surface Q Z. 2nd Image T Z
Direct recovery of shae from multile views: a arallax based aroach Rakesh Kumar. Anandan Keith Hanna Abstract Given two arbitrary views of a scene under central rojection, if the motion of oints on a arametric
More informationLecture 4: Viewing. Topics:
Lecture 4: Viewing Topics: 1. Classical viewing 2. Positioning the camera 3. Perspective and orthogonal projections 4. Perspective and orthogonal projections in OpenGL 5. Perspective and orthogonal projection
More informationCHAPTER 3. Single-view Geometry. 1. Consequences of Projection
CHAPTER 3 Single-view Geometry When we open an eye or take a photograph, we see only a flattened, two-dimensional projection of the physical underlying scene. The consequences are numerous and startling.
More information3D Sensing. Translation and Scaling in 3D. Rotation about Arbitrary Axis. Rotation in 3D is about an axis
3D Sensing Camera Model: Recall there are 5 Different Frames of Reference c Camera Model and 3D Transformations Camera Calibration (Tsai s Method) Depth from General Stereo (overview) Pose Estimation from
More informationTo Do. Demo (Projection Tutorial) Motivation. What we ve seen so far. Outline. Foundations of Computer Graphics (Fall 2012) CS 184, Lecture 5: Viewing
Foundations of Computer Graphics (Fall 0) CS 84, Lecture 5: Viewing http://inst.eecs.berkele.edu/~cs84 To Do Questions/concerns about assignment? Remember it is due Sep. Ask me or TAs re problems Motivation
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 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 information3D Photography: Epipolar geometry
3D Photograph: Epipolar geometr Kalin Kolev, Marc Pollefes Spring 203 http://cvg.ethz.ch/teaching/203spring/3dphoto/ Schedule (tentative) Feb 8 Feb 25 Mar 4 Mar Mar 8 Mar 25 Apr Apr 8 Apr 5 Apr 22 Apr
More informationAn idea which can be used once is a trick. If it can be used more than once it becomes a method
An idea which can be used once is a trick. If it can be used more than once it becomes a method - George Polya and Gabor Szego University of Texas at Arlington Rigid Body Transformations & Generalized
More informationTo Do. Motivation. Demo (Projection Tutorial) What we ve seen so far. Computer Graphics. Summary: The Whole Viewing Pipeline
Computer Graphics CSE 67 [Win 9], Lecture 5: Viewing Ravi Ramamoorthi http://viscomp.ucsd.edu/classes/cse67/wi9 To Do Questions/concerns about assignment? Remember it is due tomorrow! (Jan 6). Ask me or
More informationMulti-view geometry problems
Multi-view geometry Multi-view geometry problems Structure: Given projections o the same 3D point in two or more images, compute the 3D coordinates o that point? Camera 1 Camera 2 R 1,t 1 R 2,t 2 Camera
More informationComputer 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 informationPart I: Single and Two View Geometry Internal camera parameters
!! 43 1!???? Imaging eometry Multiple View eometry Perspective projection Richard Hartley Andrew isserman O p y VPR June 1999 where image plane This can be written as a linear mapping between homogeneous
More informationRecovering structure from a single view Pinhole perspective projection
EPIPOLAR GEOMETRY The slides are from several sources through James Hays (Brown); Silvio Savarese (U. of Michigan); Svetlana Lazebnik (U. Illinois); Bill Freeman and Antonio Torralba (MIT), including their
More informationD - Motion. EECS 4422/5323 Computer Vision
7.-7. 3D - Motion! Outline riangulation wo-frame Structure from Motion! Outline riangulation wo-frame Structure from Motion!3 Structure from Motion Pose Estimation Geometric Camera Calibration: Given known
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 informationAffine Invariance Contour Descriptor Based on the Equal Area Normalization
IAENG International Journal o Alied Mathematics, 36:, IJAM_36 5 Aine Invariance Contour Descritor Based on the Equal Area Normalization Yang Mingqiang, Kalma Kidio, Ronsin Joseh Abstract In this stud,
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 informationCS231M Mobile Computer Vision Structure from motion
CS231M Mobile Computer Vision Structure from motion - Cameras - Epipolar geometry - Structure from motion Pinhole camera Pinhole perspective projection f o f = focal length o = center of the camera z y
More informationUnderstanding 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 informationA STUDY ON CALIBRATION OF DIGITAL CAMERA
A STUDY ON CALIBRATION OF DIGITAL CAMERA Ryuji Matsuoka a, *, Kiyonari Fukue a, Kohei Cho a, Haruhisa Shimoda a, Yoshiaki Matsumae a, Kenji Hongo b, Seiju Fujiwara b a Tokai University Research & Information
More informationComputer 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 informationCMSC 425: Lecture 16 Motion Planning: Basic Concepts
: Lecture 16 Motion lanning: Basic Concets eading: Today s material comes from various sources, including AI Game rogramming Wisdom 2 by S. abin and lanning Algorithms by S. M. LaValle (Chats. 4 and 5).
More informationMATHEMATICAL MODELING OF COMPLEX MULTI-COMPONENT MOVEMENTS AND OPTICAL METHOD OF MEASUREMENT
MATHEMATICAL MODELING OF COMPLE MULTI-COMPONENT MOVEMENTS AND OPTICAL METHOD OF MEASUREMENT V.N. Nesterov JSC Samara Electromechanical Plant, Samara, Russia Abstract. The rovisions of the concet of a multi-comonent
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 2 R 3,t 3 Camera 1 Camera
More informationTRANSVERSAL LASER ROAD PROFILER
Nonconventional Technologies Review Romania, December, 014 014 Romanian Association of Nonconventional Technologies TRANSVERSAL LASER ROAD PROFILER M. N. Tautan 1, S. Miclos, D. Savastru 3 and A. Stoica
More informationComputer 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 informationSTRAND J: TRANSFORMATIONS, VECTORS and MATRICES
Mathematics SKE, Strand J UNIT J Further Transformations: Tet STRND J: TRNSFORMTIONS, VETORS and MTRIES J Further Transformations Tet ontents Section J.1 Translations * J. ombined Transformations Mathematics
More information1. We ll look at: Types of geometrical transformation. Vector and matrix representations
Tob Howard COMP272 Computer Graphics and Image Processing 3: Transformations Tob.Howard@manchester.ac.uk Introduction We ll look at: Tpes of geometrical transformation Vector and matri representations
More informationMultiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision Prasanna Sahoo Department of Mathematics University of Louisville 1 Projective 3D Geometry (Back to Chapter 2) Lecture 6 2 Singular Value Decomposition Given a
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 informationCIS 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 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 informationCSE528 Computer Graphics: Theory, Algorithms, and Applications
CSE528 Computer Graphics: Theor, Algorithms, and Applications Hong Qin State Universit of New York at Ston Brook (Ston Brook Universit) Ston Brook, New York 794--44 Tel: (63)632-845; Fa: (63)632-8334 qin@cs.sunsb.edu
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 informationCIS 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 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 information2 DETERMINING THE VANISHING POINT LOCA- TIONS
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL.??, NO.??, DATE 1 Equidistant Fish-Eye Calibration and Rectiication by Vanishing Point Extraction Abstract In this paper we describe
More informationAnnouncements. Tutorial this week Life of the polygon A1 theory questions
Announcements Assignment programming (due Frida) submission directories are ied use (submit -N Ab cscd88 a_solution.tgz) theor will be returned (Wednesda) Midterm Will cover all o the materials so ar including
More informationStructure and Motion from Uncalibrated Catadioptric Views
Structure and Motion from Uncalibrated Catadiotric Views Christoher Geyer and Kostas Daniilidis Λ GRASP Laboratory, University of Pennsylvania, Philadelhia, PA 94 fcgeyer,kostasgseas.uenn.edu Abstract
More informationStructure from motion
Multi-view geometry Structure rom motion Camera 1 Camera 2 R 1,t 1 R 2,t 2 Camera 3 R 3,t 3 Figure credit: Noah Snavely Structure rom motion? Camera 1 Camera 2 R 1,t 1 R 2,t 2 Camera 3 R 3,t 3 Structure:
More information521493S Computer Graphics Exercise 3 (Chapters 6-8)
521493S Comuter Grahics Exercise 3 (Chaters 6-8) 1 Most grahics systems and APIs use the simle lighting and reflection models that we introduced for olygon rendering Describe the ways in which each of
More informationStereo. Stereo: 3D from Two Views. Stereo Correspondence. Fundamental Matrix. Fundamental Matrix
Stereo: 3D from wo Views Stereo scene oint otica center image ane Basic rincie: rianguation Gives reconstruction as intersection of two ras equires caibration oint corresondence Stereo Corresondence Determine
More informationComputer Graphics. Bing-Yu Chen National Taiwan University The University of Tokyo
Computer Graphics Bing-Yu Chen National Taiwan Universit The Universit of Toko Viewing in 3D 3D Viewing Process Classical Viewing and Projections 3D Snthetic Camera Model Parallel Projection Perspective
More informationConvex Hulls. Helen Cameron. Helen Cameron Convex Hulls 1/101
Convex Hulls Helen Cameron Helen Cameron Convex Hulls 1/101 What Is a Convex Hull? Starting Point: Points in 2D y x Helen Cameron Convex Hulls 3/101 Convex Hull: Informally Imagine that the x, y-lane is
More informationIntroduction 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 informationCENTRAL AND PARALLEL PROJECTIONS OF REGULAR SURFACES: GEOMETRIC CONSTRUCTIONS USING 3D MODELING SOFTWARE
CENTRAL AND PARALLEL PROJECTIONS OF REGULAR SURFACES: GEOMETRIC CONSTRUCTIONS USING 3D MODELING SOFTWARE Petra Surynková Charles University in Prague, Faculty of Mathematics and Physics, Sokolovská 83,
More informationCS6670: Computer Vision
CS6670: Computer Vision Noah Snavely Lecture 5: Projection Reading: Szeliski 2.1 Projection Reading: Szeliski 2.1 Projection Müller Lyer Illusion http://www.michaelbach.de/ot/sze_muelue/index.html Modeling
More informationCalibrating a Structured Light System Dr Alan M. McIvor Robert J. Valkenburg Machine Vision Team, Industrial Research Limited P.O. Box 2225, Auckland
Calibrating a Structured Light System Dr Alan M. McIvor Robert J. Valkenburg Machine Vision Team, Industrial Research Limited P.O. Box 2225, Auckland New Zealand Tel: +64 9 3034116, Fax: +64 9 302 8106
More informationChap 7, 2009 Spring Yeong Gil Shin
Three-Dimensional i Viewingi Chap 7, 29 Spring Yeong Gil Shin Viewing i Pipeline H d fi i d? How to define a window? How to project onto the window? Rendering "Create a picture (in a snthetic camera) Specification
More informationSTRAND I: Geometry and Trigonometry. UNIT 37 Further Transformations: Student Text Contents. Section Reflections. 37.
MEP Jamaica: STRN I UNIT 7 Further Transformations: Student Tet ontents STRN I: Geometr and Trigonometr Unit 7 Further Transformations Student Tet ontents Section 7. Reflections 7. Rotations 7. Translations
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 information