Projective geometry- 2D
|
|
- Octavia Harrison
- 5 years ago
- Views:
Transcription
1 Projecive geomer- D Acknowledgemens Marc Pollefes: for allowing e use of is ecellen slides on is opic p:// Ricard Harle and Andrew Zisserman, "Muliple View Geomer in Compuer Vision"
2 Homogeneous coordinaes Homogeneous represenaion of lines a + b + c ( a,b,c) ( ka ) + ( kb) + kc, k ( a,b,c ) ~ k( a,b,c) equivalence class of vecors, an vecor is represenaive Se of all equivalence classes in R 3 (,,) forms P Homogeneous represenaion of poins (, ) on l ( a,b,c) if and onl if a + b + c (,, )( a,b,c) (,, ) l (,,) ~ k(,,), k e poin lies on e line l if and onl if ll ( ) Homogeneous coordinaes,, Inomogeneous coordinaes (, ) 3 bu onl DOF 4//4 Projecive Geomer D
3 Poins from lines and vice-versa Inersecions of lines e inersecion of wo lines l and l' is l l' Line joining wo poins e line roug wo poins and ' is l ' Eample 4//4 Projecive Geomer D 3
4 Ideal poins and e line a infini Inersecions of parallel lines l ( a, b, c) and l' ( a, b, c' ) l l' ( b, a,) Eample Ideal poins (,,) Line a infini l (,,) Noe a is se lies on a single line, P R l Noe a in P ere is no disincion beween ideal poins and oers 4//4 Projecive Geomer D 4
5 Summar e se of ideal poins lies on e line a infini, inersecs e line a infini in e ideal poin A line parallel o l also inersecs in e same ideal poin, irrespecive of e value of c. In inomogeneous noaion, is a vecor angen o e line. I is orogonal o (a, b) -- e line normal. us i represens e line direcion. As e line s direcion varies, e ideal poin varies over. --> line a infini can be oug of as e se of direcions of lines in e plane. 4//4 Projecive Geomer D 5
6 A model for e projecive plane Poins represened b ras roug origin Lines represened b planes roug origin plane represens line a infini eacl one line roug wo poins eacl one poin a inersecion of wo lines 4//4 Projecive Geomer D 6
7 Duali l l l l l' l ' Duali principle: o an eorem of -dimensional projecive geomer ere corresponds a dual eorem, wic ma be derived b inercanging e role of poins and lines in e original eorem 4//4 Projecive Geomer D 7
8 Conics Curve described b nd -degree equaion in e plane a + b + c + d + e + f or omogenized a a, 3 a + b + c + d3 + e3 + f3 3 or in mari form C wi C a b / d / b / c e / d / e / f 5DOF: { a : b : c : d : e : f } 4//4 Projecive Geomer D 8
9 4//4 Projecive Geomer D 9 Five poins define a conic For eac poin e conic passes roug f e d c b a i i i i i i or ( ),,,,, c f i i i i i i ( ) f e d c b a,,,,, c c sacking consrains ields
10 angen lines o conics e line l angen o C a poin on C is given b lc l C 4//4 Projecive Geomer D
11 Dual conics A line angen o e conic C saisfies l C * l In general (C full rank): * C C Dual conics line conics conic envelopes 4//4 Projecive Geomer D
12 Degenerae conics A conic is degenerae if mari C is no of full rank e.g. wo lines (rank ) C lm + ml e.g. repeaed line (rank ) m l C ll l Degenerae line conics: poins (rank ), double poin (rank) Noe a for degenerae conics ( * C ) * C 4//4 Projecive Geomer D
13 Projecive ransformaions Definiion: eorem: A projecivi is an inverible mapping from P o iself suc a ree poins,, 3 lie on e same line if and onl if ( ),( ),( 3 ) do. A mapping :P P is a projecivi if and onl if ere eis a non-singular 33 mari H suc a for an poin in P reprened b a vecor i is rue a ()H Definiion: Projecive ransformaion ' ' ' or ' H 8DOF projecivicollineaionprojecive ransformaionomograp 4//4 Projecive Geomer D 3
14 Mapping beween planes cenral projecion ma be epressed b H (applicaion of eorem) 4//4 Projecive Geomer D 4
15 Removing projecive disorion selec four poins in a plane wi know coordinaes ' ' ' ' ' + + ' ( ) + 3 ( ) + 3 ' + ' (linear in ij ) 3 33 ( consrains/poin, 8DOF 4 poins needed) Remark: no calibraion a all necessar, beer was o compue (see laer) 4//4 Projecive Geomer D 5
16 ransformaion of lines and conics For a poin ransformaion ' H ransformaion for lines l' H - ransformaion for conics l C ' H - CH - ransformaion for dual conics * C' * HC H 4//4 Projecive Geomer D 6
17 Disorions under cener projecion Similari: squares imaged as squares. Affine: parallel lines remain parallel; circles become ellipses. Projecive: Parallel lines converge. 4//4 Projecive Geomer D 7
18 Class I: Isomeries (isosame, mericmeasure) ' ' ε cosθ ε sinθ sinθ cosθ ε ± orienaion preserving: orienaion reversing: ε ε ' H E R 3DOF ( roaion, ranslaion) R R special cases: pure roaion, pure ranslaion Invarians: leng, angle, area 4//4 Projecive Geomer D 8 I
19 4//4 Projecive Geomer D 9 Class II: Similariies (isomer + scale) cos sin sin cos ' ' s s s s θ θ θ θ ' R H s S I R R also know as equi-form (sape preserving) meric srucure srucure up o similari (in lieraure) 4DOF ( scale, roaion, ranslaion) Invarians: raios of leng, angle, raios of areas, parallel lines
20 4//4 Projecive Geomer D Class III: Affine ransformaions ' ' a a a a ' A H A non-isoropic scaling! (DOF: scale raio and orienaion) 6DOF ( scale, roaion, ranslaion) Invarians: parallel lines, raios of parallel lengs, raios of areas ( ) ( ) ( ) φ φ θ DR R R A λ λ D
21 Class VI: Projecive ransformaions ' H P A v v v ( v v ), 8DOF ( scale, roaion, ranslaion, line a infini) Acion non-omogeneous over e plane Invarians: cross-raio of four poins on a line (raio of raio) 4//4 Projecive Geomer D
22 4//4 Projecive Geomer D Acion of affiniies and projeciviies on line a infini + v v v v A A v A A Line a infini becomes finie, allows o observe vanising poins, orizon. Line a infini sas a infini, bu poins move along line
23 4//4 Projecive Geomer D 3 Decomposiion of projecive ransformaions v v s P A S v v A I K R H H H H RK + v A s K de K upper-riangular, decomposiion unique (if cosen s>) H.5. cos 45 sin 45. sin 45 cos 45 o o o o H Eample:
24 Overview ransformaions Projecive 8dof Concurrenc, collineari, order of conac (inersecion, angenc, inflecion, ec.), cross raio Affine 6dof Similari 4dof Euclidean 3dof a a sr sr r r a a r r sr sr Parallellism, raio of areas, raio of lengs on parallel lines (e.g midpoins), linear combinaions of vecors (cenroids). e line a infini l Raios of lengs, angles. e circular poins I,J lengs, areas. 4//4 Projecive Geomer D 4
M y. Image Warping. Targil 7 : Image Warping. Image Warping. 2D Geometric Transformations. image filtering: change range of image g(x) = T(f(x))
Hebrew Universi Image Processing - 6 Image Warping Hebrew Universi Image Processing - 6 argil 7 : Image Warping D Geomeric ransormaions hp://www.jere-marin.com Man slides rom Seve Seiz and Aleei Eros Image
More information3D Computer Vision II. Reminder Projective Geometry, Transformations. Nassir Navab. October 27, 2009
3D Computer Vision II Reminder Projective Geometr, Transformations Nassir Navab based on a course given at UNC b Marc Pollefes & the book Multiple View Geometr b Hartle & Zisserman October 27, 29 2D Transformations
More information4.1 3D GEOMETRIC TRANSFORMATIONS
MODULE IV MCA - 3 COMPUTER GRAPHICS ADMN 29- Dep. of Compuer Science And Applicaions, SJCET, Palai 94 4. 3D GEOMETRIC TRANSFORMATIONS Mehods for geomeric ransformaions and objec modeling in hree dimensions
More informationProjective 2D Geometry
Projective D Geometry Multi View Geometry (Spring '08) Projective D Geometry Prof. Kyoung Mu Lee SoEECS, Seoul National University Homogeneous representation of lines and points Projective D Geometry Line
More informationCENG 477 Introduction to Computer Graphics. Modeling Transformations
CENG 477 Inroducion o Compuer Graphics Modeling Transformaions Modeling Transformaions Model coordinaes o World coordinaes: Model coordinaes: All shapes wih heir local coordinaes and sies. world World
More informationCAMERA CALIBRATION BY REGISTRATION STEREO RECONSTRUCTION TO 3D MODEL
CAMERA CALIBRATION BY REGISTRATION STEREO RECONSTRUCTION TO 3D MODEL Klečka Jan Docoral Degree Programme (1), FEEC BUT E-mail: xkleck01@sud.feec.vubr.cz Supervised by: Horák Karel E-mail: horak@feec.vubr.cz
More informationSTEREO PLANE MATCHING TECHNIQUE
STEREO PLANE MATCHING TECHNIQUE Commission III KEY WORDS: Sereo Maching, Surface Modeling, Projecive Transformaion, Homography ABSTRACT: This paper presens a new ype of sereo maching algorihm called Sereo
More informationCS 428: Fall Introduction to. Geometric Transformations (continued) Andrew Nealen, Rutgers, /20/2010 1
CS 428: Fall 2 Inroducion o Compuer Graphic Geomeric Tranformaion (coninued) Andrew Nealen, Ruger, 2 9/2/2 Tranlaion Tranlaion are affine ranformaion The linear par i he ideni mari The 44 mari for he ranlaion
More informationEECS 487: Interactive Computer Graphics
EECS 487: Ineracive Compuer Graphics Lecure 7: B-splines curves Raional Bézier and NURBS Cubic Splines A represenaion of cubic spline consiss of: four conrol poins (why four?) hese are compleely user specified
More information3D Computer Vision II. Reminder Projective Geometry, Transformations
3D Computer Vision II Reminder Projective Geometry, Transformations Nassir Navab" based on a course given at UNC by Marc Pollefeys & the book Multiple View Geometry by Hartley & Zisserman" October 21,
More informationGeometry Transformation
Geomer Transformaion Januar 26 Prof. Gar Wang Dep. of Mechanical and Manufacuring Engineering Universi of Manioba Wh geomer ransformaion? Beer undersanding of he design Communicaion wih cusomers Generaing
More informationImage warping/morphing
Image arping/morphing Image arping Digial Visual Effecs Yung-Yu Chuang ih slides b Richard Szeliski, Seve Seiz, Tom Funkhouser and leei Efros Image formaion Sampling and quanizaion B Wha is an image We
More informationgeometric transformations
geomeric ranformaion comuer grahic ranform 28 fabio ellacini linear algebra review marice noaion baic oeraion mari-vecor mulilicaion comuer grahic ranform 28 fabio ellacini 2 marice noaion for marice and
More informationMAPI Computer Vision
MAPI Computer Vision Multiple View Geometry In tis module we intend to present several tecniques in te domain of te 3D vision Manuel Joao University of Mino Dep Industrial Electronics - Applications -
More informationImage warping Li Zhang CS559
Wha is an image Image arping Li Zhang S559 We can hink of an image as a funcion, f: R 2 R: f(, ) gives he inensi a posiion (, ) defined over a recangle, ih a finie range: f: [a,b][c,d] [,] f Slides solen
More informationEffects needed for Realism. Ray Tracing. Ray Tracing: History. Outline. Foundations of Computer Graphics (Fall 2012)
Foundaions of ompuer Graphics (Fall 2012) S 184, Lecure 16: Ray Tracing hp://ins.eecs.berkeley.edu/~cs184 Effecs needed for Realism (Sof) Shadows Reflecions (Mirrors and Glossy) Transparency (Waer, Glass)
More informationComputer Vision I - Appearance-based Matching and Projective Geometry
Computer Vision I - Appearance-based Matching and Projective Geometry Carsten Rother 05/11/2015 Computer Vision I: Image Formation Process Roadmap for next four lectures Computer Vision I: Image Formation
More informationAML710 CAD LECTURE 11 SPACE CURVES. Space Curves Intrinsic properties Synthetic curves
AML7 CAD LECTURE Space Curves Inrinsic properies Synheic curves A curve which may pass hrough any region of hreedimensional space, as conrased o a plane curve which mus lie on a single plane. Space curves
More informationSpline Curves. Color Interpolation. Normal Interpolation. Last Time? Today. glshademodel (GL_SMOOTH); Adjacency Data Structures. Mesh Simplification
Las Time? Adjacency Daa Srucures Spline Curves Geomeric & opologic informaion Dynamic allocaion Efficiency of access Mesh Simplificaion edge collapse/verex spli geomorphs progressive ransmission view-dependen
More informationGauss-Jordan Algorithm
Gauss-Jordan Algorihm The Gauss-Jordan algorihm is a sep by sep procedure for solving a sysem of linear equaions which may conain any number of variables and any number of equaions. The algorihm is carried
More informationIn Proceedings of CVPR '96. Structure and Motion of Curved 3D Objects from. using these methods [12].
In Proceedings of CVPR '96 Srucure and Moion of Curved 3D Objecs from Monocular Silhouees B Vijayakumar David J Kriegman Dep of Elecrical Engineering Yale Universiy New Haven, CT 652-8267 Jean Ponce Compuer
More informationSystems & Biomedical Engineering Department. Transformation
Sem & Biomedical Engineering Deparmen SBE 36B: Compuer Sem III Compuer Graphic Tranformaion Dr. Aman Eldeib Spring 28 Tranformaion Tranformaion i a fundamenal corner one of compuer graphic and i a cenral
More informationToday. Curves & Surfaces. Can We Disguise the Facets? Limitations of Polygonal Meshes. Better, but not always good enough
Today Curves & Surfaces Moivaion Limiaions of Polygonal Models Some Modeling Tools & Definiions Curves Surfaces / Paches Subdivision Surfaces Limiaions of Polygonal Meshes Can We Disguise he Faces? Planar
More informationTraditional Rendering (Ray Tracing and Radiosity)
Tradiional Rendering (Ray Tracing and Radiosiy) CS 517 Fall 2002 Compuer Science Cornell Universiy Bidirecional Reflecance (BRDF) λ direcional diffuse specular θ uniform diffuse τ σ BRDF Bidirecional Reflecance
More informationA METHOD OF MODELING DEFORMATION OF AN OBJECT EMPLOYING SURROUNDING VIDEO CAMERAS
A METHOD OF MODELING DEFORMATION OF AN OBJECT EMLOYING SURROUNDING IDEO CAMERAS Joo Kooi TAN, Seiji ISHIKAWA Deparmen of Mechanical and Conrol Engineering Kushu Insiue of Technolog, Japan ehelan@is.cnl.kuech.ac.jp,
More informationToday s class. Geometric objects and transformations. Informationsteknologi. Wednesday, November 7, 2007 Computer Graphics - Class 5 1
Toda s class Geometric objects and transformations Wednesda, November 7, 27 Computer Graphics - Class 5 Vector operations Review of vector operations needed for working in computer graphics adding two
More informationImage Content Representation
Image Conen Represenaion Represenaion for curves and shapes regions relaionships beween regions E.G.M. Perakis Image Represenaion & Recogniion 1 Reliable Represenaion Uniqueness: mus uniquely specify an
More informationHow much does the migration aperture actually contribute to the migration result?
Migraion aperure conribuion How muc does e migraion aperure acually conribue o e migraion resul? Suang Sun and Jon C. Bancrof ABSTRACT Reflecion energy from a linear reflecor comes from e inegran oer an
More informationProjection & Interaction
Projecion & Ineracion Algebra of projecion Canonical viewing volume rackball inerface ransform Hierarchies Preview of Assignmen #2 Lecure 8 Comp 236 Spring 25 Projecions Our lives are grealy simplified
More informationComputer representations of piecewise
Edior: Gabriel Taubin Inroducion o Geomeric Processing hrough Opimizaion Gabriel Taubin Brown Universiy Compuer represenaions o piecewise smooh suraces have become vial echnologies in areas ranging rom
More informationMETR Robotics Tutorial 2 Week 2: Homogeneous Coordinates
METR4202 -- Robotics Tutorial 2 Week 2: Homogeneous Coordinates The objective of this tutorial is to explore homogenous transformations. The MATLAB robotics toolbox developed by Peter Corke might be a
More informationCurves & Surfaces. Last Time? Today. Readings for Today (pick one) Limitations of Polygonal Meshes. Today. Adjacency Data Structures
Las Time? Adjacency Daa Srucures Geomeric & opologic informaion Dynamic allocaion Efficiency of access Curves & Surfaces Mesh Simplificaion edge collapse/verex spli geomorphs progressive ransmission view-dependen
More informationA Principled Approach to. MILP Modeling. Columbia University, August Carnegie Mellon University. Workshop on MIP. John Hooker.
Slide A Principled Approach o MILP Modeling John Hooer Carnegie Mellon Universiy Worshop on MIP Columbia Universiy, Augus 008 Proposal MILP modeling is an ar, bu i need no be unprincipled. Slide Proposal
More informationDigital Geometry Processing Differential Geometry
Digial Geomery Processing Differenial Geomery Moivaion Undersand he srucure of he surface Differenial Geomery Properies: smoohness, curviness, imporan direcions How o modify he surface o change hese properies
More informationRay Casting. Outline. Outline in Code
Foundaions of ompuer Graphics Online Lecure 10: Ray Tracing 2 Nus and ols amera Ray asing Ravi Ramamoorhi Ouline amera Ray asing (choose ray direcions) Ray-objec inersecions Ray-racing ransformed objecs
More informationLast Time: Curves & Surfaces. Today. Questions? Limitations of Polygonal Meshes. Can We Disguise the Facets?
Las Time: Curves & Surfaces Expeced value and variance Mone-Carlo in graphics Imporance sampling Sraified sampling Pah Tracing Irradiance Cache Phoon Mapping Quesions? Today Moivaion Limiaions of Polygonal
More informationRobot Vision: Projective Geometry
Robot Vision: Projective Geometry Ass.Prof. Friedrich Fraundorfer SS 2018 1 Learning goals Understand homogeneous coordinates Understand points, line, plane parameters and interpret them geometrically
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 informationSchedule. Curves & Surfaces. Questions? Last Time: Today. Limitations of Polygonal Meshes. Acceleration Data Structures.
Schedule Curves & Surfaces Sunday Ocober 5 h, * 3-5 PM *, Room TBA: Review Session for Quiz 1 Exra Office Hours on Monday (NE43 Graphics Lab) Tuesday Ocober 7 h : Quiz 1: In class 1 hand-wrien 8.5x11 shee
More information4. Minimax and planning problems
CS/ECE/ISyE 524 Inroducion o Opimizaion Spring 2017 18 4. Minima and planning problems ˆ Opimizing piecewise linear funcions ˆ Minima problems ˆ Eample: Chebyshev cener ˆ Muli-period planning problems
More informationProjective geometry for Computer Vision
Department of Computer Science and Engineering IIT Delhi NIT, Rourkela March 27, 2010 Overview Pin-hole camera Why projective geometry? Reconstruction Computer vision geometry: main problems Correspondence
More informationFinite Difference Methods, Grid Staggering, and Truncation Error. h t. h u x
Finie Difference Meods, Grid Saggering, and Truncaion Error Inroducion o Temporal Differencing Meods Tere eis wo basic ypes of emporal differencing meods: eplici and implici meods. Wi eplici meods, a model
More informationPart 0. The Background: Projective Geometry, Transformations and Estimation
Part 0 The Background: Projective Geometry, Transformations and Estimation La reproduction interdite (The Forbidden Reproduction), 1937, René Magritte. Courtesy of Museum Boijmans van Beuningen, Rotterdam.
More informationPrecise Voronoi Cell Extraction of Free-form Rational Planar Closed Curves
Precise Voronoi Cell Exracion of Free-form Raional Planar Closed Curves Iddo Hanniel, Ramanahan Muhuganapahy, Gershon Elber Deparmen of Compuer Science Technion, Israel Insiue of Technology Haifa 32000,
More informationSegmentation by Level Sets and Symmetry
Segmenaion by Level Ses and Symmery Tammy Riklin-Raviv Nahum Kiryai Nir Sochen Tel Aviv Universiy, Tel Aviv 69978, Israel ammy@eng.au.ac.il nk@eng.au.ac.il sochen@pos.au.ac.il Absrac Shape symmery is an
More informationComputer Vision I - Appearance-based Matching and Projective Geometry
Computer Vision I - Appearance-based Matching and Projective Geometry Carsten Rother 01/11/2016 Computer Vision I: Image Formation Process Roadmap for next four lectures Computer Vision I: Image Formation
More informationComputer Vision. 2. Projective Geometry in 3D. Lars Schmidt-Thieme
Computer Vision 2. Projective Geometry in 3D Lars Schmidt-Thieme Information Systems and Machine Learning Lab (ISMLL) Institute for Computer Science University of Hildesheim, Germany 1 / 26 Syllabus Mon.
More informationVisual Recognition: Image Formation
Visual Recognition: Image Formation Raquel Urtasun TTI Chicago Jan 5, 2012 Raquel Urtasun (TTI-C) Visual Recognition Jan 5, 2012 1 / 61 Today s lecture... Fundamentals of image formation You should know
More informationEM225 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 informationparametric spline curves
arameric sline curves comuer grahics arameric curves 9 fabio ellacini curves used in many conexs fons animaion ahs shae modeling differen reresenaion imlici curves arameric curves mosly used comuer grahics
More informationEngineering Mathematics 2018
Engineering Mahemaics 08 SUBJET NAME : Mahemaics II SUBJET ODE : MA65 MATERIAL NAME : Par A quesions REGULATION : R03 UPDATED ON : November 06 TEXTBOOK FOR REFERENE To buy he book visi : Sri Hariganesh
More informationTHE micro-lens array (MLA) based light field cameras,
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL., NO., A Generic Muli-Projecion-Cener Model and Calibraion Mehod for Ligh Field Cameras Qi hang, Chunping hang, Jinbo Ling, Qing Wang,
More informationInteractive Computer Graphics. Warping and morphing. Warping and Morphing. Warping and Morphing. Lecture 14+15: Warping and Morphing. What is.
Interactive Computer Graphics Warping and morphing Lecture 14+15: Warping and Morphing Lecture 14: Warping and Morphing: Slide 1 Lecture 14: Warping and Morphing: Slide 2 Warping and Morphing What is Warping
More informationJorge Salvador Marques, Stereo Reconstruction
Jorge Slvdor Mrques, Sereo Reconsrucion roblem Gol: reconsruc he D she of objecs in he scene from or more imges. Jorge Slvdor Mrques, Jorge Slvdor Mrques, secil cse f f f f b model reconsrucion, b - fb,
More informationGraphics and Interaction Transformation geometry and homogeneous coordinates
433-324 Graphics and Interaction Transformation geometry and homogeneous coordinates Department of Computer Science and Software Engineering The Lecture outline Introduction Vectors and matrices Translation
More informationCOMP30019 Graphics and Interaction Transformation geometry and homogeneous coordinates
COMP30019 Graphics and Interaction Transformation geometry and homogeneous coordinates Department of Computer Science and Software Engineering The Lecture outline Introduction Vectors and matrices Translation
More informationInvariance of l and the Conic Dual to Circular Points C
Invariance of l and the Conic Dual to Circular Points C [ ] A t l = (0, 0, 1) is preserved under H = v iff H is an affinity: w [ ] l H l H A l l v 0 [ t 0 v! = = w w] 0 0 v = 0 1 1 C = diag(1, 1, 0) is
More informationQuantitative macro models feature an infinite number of periods A more realistic (?) view of time
INFINIE-HORIZON CONSUMPION-SAVINGS MODEL SEPEMBER, Inroducion BASICS Quaniaive macro models feaure an infinie number of periods A more realisic (?) view of ime Infinie number of periods A meaphor for many
More informationKinematic Synthesis. October 6, 2015 Mark Plecnik
Kinematic Synthesis October 6, 2015 Mark Plecnik Classifying Mechanisms Several dichotomies Serial and Parallel Few DOFS and Many DOFS Planar/Spherical and Spatial Rigid and Compliant Mechanism Trade-offs
More information3-D Object Modeling and Recognition for Telerobotic Manipulation
Research Showcase @ CMU Roboics Insiue School of Compuer Science 1995 3-D Objec Modeling and Recogniion for Teleroboic Manipulaion Andrew Johnson Parick Leger Regis Hoffman Marial Heber James Osborn Follow
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 informationSimultaneous Precise Solutions to the Visibility Problem of Sculptured Models
Simulaneous Precise Soluions o he Visibiliy Problem of Sculpured Models Joon-Kyung Seong 1, Gershon Elber 2, and Elaine Cohen 1 1 Universiy of Uah, Sal Lake Ciy, UT84112, USA, seong@cs.uah.edu, cohen@cs.uah.edu
More informationImage Warping : Computational Photography Alexei Efros, CMU, Fall Some slides from Steve Seitz
Image Warping http://www.jeffre-martin.com Some slides from Steve Seitz 5-463: Computational Photograph Aleei Efros, CMU, Fall 2 Image Transformations image filtering: change range of image g() T(f())
More informationAuto-calibration. Computer Vision II CSE 252B
Auto-calibration Computer Vision II CSE 252B 2D Affine Rectification Solve for planar projective transformation that maps line (back) to line at infinity Solve as a Householder matrix Euclidean Projective
More informationImage Registration in Medical Imaging
2/2/202 Image Regisraion in Medical Imaging BI260 VALERIE CARDENAS NICOLSON, PH.D ACKNOWLEDGEMENTS: COLIN STUDHOLME, PH.D. Medical Imaging Analysis Developing mahemaical algorihms o erac and relae informaion
More informationTwo Dimensional Viewing
Two Dimensional Viewing Dr. S.M. Malaek Assistant: M. Younesi Two Dimensional Viewing Basic Interactive Programming Basic Interactive Programming User controls contents, structure, and appearance of objects
More informationImage Warping. Many slides from Alyosha Efros + Steve Seitz. Photo by Sean Carroll
Image Warping Man slides from Alosha Efros + Steve Seitz Photo b Sean Carroll Morphing Blend from one object to other with a series of local transformations Image Transformations image filtering: change
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 informationShortest Path Algorithms. Lecture I: Shortest Path Algorithms. Example. Graphs and Matrices. Setting: Dr Kieran T. Herley.
Shores Pah Algorihms Background Seing: Lecure I: Shores Pah Algorihms Dr Kieran T. Herle Deparmen of Compuer Science Universi College Cork Ocober 201 direced graph, real edge weighs Le he lengh of a pah
More informationScattering at an Interface: Normal Incidence
Course Insrucor Dr. Raymond C. Rumpf Office: A 337 Phone: (915) 747 6958 Mail: rcrumpf@uep.edu 4347 Applied lecromagneics Topic 3f Scaering a an Inerface: Normal Incidence Scaering These Normal noes Incidence
More informationImage warping , , Computational Photography Fall 2017, Lecture 10
Image warping http://graphics.cs.cmu.edu/courses/15-463 15-463, 15-663, 15-862 Computational Photography Fall 2017, Lecture 10 Course announcements Second make-up lecture on Friday, October 6 th, noon-1:30
More informationRepresenting Non-Manifold Shapes in Arbitrary Dimensions
Represening Non-Manifold Shapes in Arbirary Dimensions Leila De Floriani,2 and Annie Hui 2 DISI, Universiy of Genova, Via Dodecaneso, 35-646 Genova (Ialy). 2 Deparmen of Compuer Science, Universiy of Maryland,
More informationEditing and Transformation
Lecture 5 Editing and Transformation Modeling Model can be produced b the combination of entities that have been edited. D: circle, arc, line, ellipse 3D: primitive bodies, etrusion and revolved of a profile
More informationAffine and Projective Transformations
CS 674: Intro to Computer Vision Affine and Projective Transformations Prof. Adriana Kovaska Universit of Pittsburg October 3, 26 Alignment problem We previousl discussed ow to matc features across images,
More informationLAMP: 3D Layered, Adaptive-resolution and Multiperspective Panorama - a New Scene Representation
Submission o Special Issue of CVIU on Model-based and Image-based 3D Scene Represenaion for Ineracive Visualizaion LAMP: 3D Layered, Adapive-resoluion and Muliperspecive Panorama - a New Scene Represenaion
More informationCamera 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 informationOcclusion-Free Hand Motion Tracking by Multiple Cameras and Particle Filtering with Prediction
58 IJCSNS Inernaional Journal of Compuer Science and Nework Securiy, VOL.6 No.10, Ocober 006 Occlusion-Free Hand Moion Tracking by Muliple Cameras and Paricle Filering wih Predicion Makoo Kao, and Gang
More informationImplementing Ray Casting in Tetrahedral Meshes with Programmable Graphics Hardware (Technical Report)
Implemening Ray Casing in Terahedral Meshes wih Programmable Graphics Hardware (Technical Repor) Marin Kraus, Thomas Erl March 28, 2002 1 Inroducion Alhough cell-projecion, e.g., [3, 2], and resampling,
More informationA Review on Block Matching Motion Estimation and Automata Theory based Approaches for Fractal Coding
Regular Issue A Review on Block Maching Moion Esimaion and Auomaa Theory based Approaches for Fracal Coding Shailesh D Kamble 1, Nileshsingh V Thakur 2, and Preei R Bajaj 3 1 Compuer Science & Engineering,
More informationImage Warping. Some slides from Steve Seitz
Image Warping http://www.jeffre-martin.com Some slides from Steve Seitz 5-463: Computational Photograph Aleei Efros, CMU, Fall 26 Image Warping image filtering: change range of image g() T(f()) f T f image
More information2D Image Transforms Computer Vision (Kris Kitani) Carnegie Mellon University
2D Image Transforms 16-385 Computer Vision (Kris Kitani) Carnegie Mellon University Extract features from an image what do we do next? Feature matching (object recognition, 3D reconstruction, augmented
More informationIntroduction to Data-Driven Animation: Programming with Motion Capture Jehee Lee
Inroducion o Daa-Driven Animaion: Programming wih Moion Caure Jehee Lee Seoul Naional Universiy Daa-Driven Animaion wih Moion Caure Programming wih Moion Caure Why is i difficul? Encomass a lo of heerogeneous
More informationRay Tracing II. Improving Raytracing Speed. Improving Computational Complexity. Raytracing Computational Complexity
Ra Tracing II Iproving Raracing Speed Copuer Graphics Ra Tracing II 2005 Fabio Pellacini 1 Copuer Graphics Ra Tracing II 2005 Fabio Pellacini 2 Raracing Copuaional Coplei ra-scene inersecion is epensive
More informationInteractive Graphical Systems HT2005
Ineracive Graphical Ssems HT25 Lesson 2 : Graphics Primer Sefan Seipel Sefan Seipel, Deparmen of Informaion Technolog, Uppsala Universi Ke issues of his lecure Represenaions of 3D models Repeiion of basic
More informationDouble Integrals in Polar Coordinates
Double Integrals in Polar Coordinates. A flat plate is in the shape of the region in the first quadrant ling between the circles + and +. The densit of the plate at point, is + kilograms per square meter
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 informationMath 26: Fall (part 1) The Unit Circle: Cosine and Sine (Evaluating Cosine and Sine, and The Pythagorean Identity)
Math : Fall 0 0. (part ) The Unit Circle: Cosine and Sine (Evaluating Cosine and Sine, and The Pthagorean Identit) Cosine and Sine Angle θ standard position, P denotes point where the terminal side of
More informationGeodesic, Flow Front and Voronoi Diagram
11 Geodesic, Flow Fron and Voronoi Diagram C. K. Au Nannag Technological Uniersi, mckau@nu.edu.sg ABSTRACT Geodesics and flow frons are orhogonal o each oher. These wo ses consiue he space ime funcion
More informationCEE598 - Visual Sensing for Civil Infrastructure Eng. & Mgmt.
CEE598 - Visul Sensing for Civil Infrsrucure Eng. & Mgm. Session 2 Review of Liner Algebr nd Geomeric Trnsformions Mni Golprvr-Frd Deprmen of Civil nd Environmenl Engineering Deprmen of Compuer Science
More informationMatrix Transformations. Affine Transformations
Matri ransformations Basic Graphics ransforms ranslation Scaling Rotation Reflection Shear All Can be Epressed As Linear Functions of the Original Coordinates : A + B + C D + E + F ' A ' D 1 B E C F 1
More informationProceeding of the 6 th International Symposium on Artificial Intelligence and Robotics & Automation in Space: i-sairas 2001, Canadian Space Agency,
Proceeding of he 6 h Inernaional Symposium on Arificial Inelligence and Roboics & Auomaion in Space: i-sairas 00, Canadian Space Agency, S-Huber, Quebec, Canada, June 8-, 00. Muli-resoluion Mapping Using
More informationName: [20 points] Consider the following OpenGL commands:
Name: 2 1. [20 points] Consider the following OpenGL commands: glmatrimode(gl MODELVIEW); glloadidentit(); glrotatef( 90.0, 0.0, 1.0, 0.0 ); gltranslatef( 2.0, 0.0, 0.0 ); glscalef( 2.0, 1.0, 1.0 ); What
More informationMotion Level-of-Detail: A Simplification Method on Crowd Scene
Moion Level-of-Deail: A Simplificaion Mehod on Crowd Scene Absrac Junghyun Ahn VR lab, EECS, KAIST ChocChoggi@vr.kais.ac.kr hp://vr.kais.ac.kr/~zhaoyue Recen echnological improvemen in characer animaion
More informationReal-time 2D Video/3D LiDAR Registration
Real-ime 2D Video/3D LiDAR Regisraion C. Bodenseiner Fraunhofer IOSB chrisoph.bodenseiner@iosb.fraunhofer.de M. Arens Fraunhofer IOSB michael.arens@iosb.fraunhofer.de Absrac Progress in LiDAR scanning
More informationCoordinate transformations. 5554: Packet 8 1
Coordinate transformations 5554: Packet 8 1 Overview Rigid transformations are the simplest Translation, rotation Preserve sizes and angles Affine transformation is the most general linear case Homogeneous
More informationCSE 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 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 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 informationIMAGE SAMPLING AND IMAGE QUANTIZATION
Digial image processing IMAGE SAMPLING AND IMAGE QUANTIZATION. Inrodcion. Sampling in he wo-dimensional space Basics on image sampling The concep of spaial freqencies Images of limied bandwidh Two-dimensional
More information