Feature Matching and Heat Flow in Centro-Affine Geometry
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Globally Optimal Affine and Metric Upgrades in Stratified Autocalibration
Globally Optimal Affine and Metric Upgrades in Stratified Autocalibration Manmohan Chandrakery Sameer Agarwalz David Kriegmany Serge Belongiey [email protected] [email protected] [email protected] [email protected] y University of California, San Diego z University of Washington, Seattle Abstract parameters of the cameras, which is commonly approached by estimating the dual image of the absolute conic (DIAC). We present a practical, stratified autocalibration algo- A variety of linear methods exist towards this end, how- rithm with theoretical guarantees of global optimality. Given ever, they are known to perform poorly in the presence of a projective reconstruction, the first stage of the algorithm noise [10]. Perhaps more significantly, most methods a pos- upgrades it to affine by estimating the position of the plane teriori impose the positive semidefiniteness of the DIAC, at infinity. The plane at infinity is computed by globally which might lead to a spurious calibration. Thus, it is im- minimizing a least squares formulation of the modulus con- portant to impose the positive semidefiniteness of the DIAC straints. In the second stage, the algorithm upgrades this within the optimization, not as a post-processing step. affine reconstruction to a metric one by globally minimizing This paper proposes global minimization algorithms for the infinite homography relation to compute the dual image both stages of stratified autocalibration that furnish theoreti- of the absolute conic (DIAC). The positive semidefiniteness cal certificates of optimality. That is, they return a solution at of the DIAC is explicitly enforced as part of the optimization most away from the global minimum, for arbitrarily small . -
EXAM QUESTIONS (Part Two)
Created by T. Madas MATRICES EXAM QUESTIONS (Part Two) Created by T. Madas Created by T. Madas Question 1 (**) Find the eigenvalues and the corresponding eigenvectors of the following 2× 2 matrix. 7 6 A = . 6 2 2 3 λ= −2, u = α , λ=11, u = β −3 2 Question 2 (**) A transformation in three dimensional space is defined by the following 3× 3 matrix, where x is a scalar constant. 2− 2 4 C =5x − 2 2 . −1 3 x Show that C is non singular for all values of x . FP1-N , proof Created by T. Madas Created by T. Madas Question 3 (**) The 2× 2 matrix A is given below. 1 8 A = . 8− 11 a) Find the eigenvalues of A . b) Determine an eigenvector for each of the corresponding eigenvalues of A . c) Find a 2× 2 matrix P , so that λ1 0 PT AP = , 0 λ2 where λ1 and λ2 are the eigenvalues of A , with λ1< λ 2 . 1 2 1 2 5 5 λ1= −15, λ 2 = 5 , u= , v = , P = − 2 1 − 2 1 5 5 Created by T. Madas Created by T. Madas Question 4 (**) Describe fully the transformation given by the following 3× 3 matrix. 0.28− 0.96 0 0.96 0.28 0 . 0 0 1 rotation in the z axis, anticlockwise, by arcsin() 0.96 Question 5 (**) A transformation in three dimensional space is defined by the following 3× 3 matrix, where k is a scalar constant. 1− 2 k A = k 2 0 . 2 3 1 Show that the transformation defined by A can be inverted for all values of k . -
Affine Reflection Group Codes
Affine Reflection Group Codes Terasan Niyomsataya1, Ali Miri1,2 and Monica Nevins2 School of Information Technology and Engineering (SITE)1 Department of Mathematics and Statistics2 University of Ottawa, Ottawa, Canada K1N 6N5 email: {tniyomsa,samiri}@site.uottawa.ca, [email protected] Abstract This paper presents a construction of Slepian group codes from affine reflection groups. The solution to the initial vector and nearest distance problem is presented for all irreducible affine reflection groups of rank n ≥ 2, for varying stabilizer subgroups. Moreover, we use a detailed analysis of the geometry of affine reflection groups to produce an efficient decoding algorithm which is equivalent to the maximum-likelihood decoder. Its complexity depends only on the dimension of the vector space containing the codewords, and not on the number of codewords. We give several examples of the decoding algorithm, both to demonstrate its correctness and to show how, in small rank cases, it may be further streamlined by exploiting additional symmetries of the group. 1 1 Introduction Slepian [11] introduced group codes whose codewords represent a finite set of signals combining coding and modulation, for the Gaussian channel. A thorough survey of group codes can be found in [8]. The codewords lie on a sphere in n−dimensional Euclidean space Rn with equal nearest-neighbour distances. This gives congruent maximum-likelihood (ML) decoding regions, and hence equal error probability, for all codewords. Given a group G with a representation (action) on Rn, that is, an 1Keywords: Group codes, initial vector problem, decoding schemes, affine reflection groups 1 orthogonal n × n matrix Og for each g ∈ G, a group code generated from G is given by the set of all cg = Ogx0 (1) n for all g ∈ G where x0 = (x1, . -
Projective Geometry: a Short Introduction
Projective Geometry: A Short Introduction Lecture Notes Edmond Boyer Master MOSIG Introduction to Projective Geometry Contents 1 Introduction 2 1.1 Objective . .2 1.2 Historical Background . .3 1.3 Bibliography . .4 2 Projective Spaces 5 2.1 Definitions . .5 2.2 Properties . .8 2.3 The hyperplane at infinity . 12 3 The projective line 13 3.1 Introduction . 13 3.2 Projective transformation of P1 ................... 14 3.3 The cross-ratio . 14 4 The projective plane 17 4.1 Points and lines . 17 4.2 Line at infinity . 18 4.3 Homographies . 19 4.4 Conics . 20 4.5 Affine transformations . 22 4.6 Euclidean transformations . 22 4.7 Particular transformations . 24 4.8 Transformation hierarchy . 25 Grenoble Universities 1 Master MOSIG Introduction to Projective Geometry Chapter 1 Introduction 1.1 Objective The objective of this course is to give basic notions and intuitions on projective geometry. The interest of projective geometry arises in several visual comput- ing domains, in particular computer vision modelling and computer graphics. It provides a mathematical formalism to describe the geometry of cameras and the associated transformations, hence enabling the design of computational ap- proaches that manipulates 2D projections of 3D objects. In that respect, a fundamental aspect is the fact that objects at infinity can be represented and manipulated with projective geometry and this in contrast to the Euclidean geometry. This allows perspective deformations to be represented as projective transformations. Figure 1.1: Example of perspective deformation or 2D projective transforma- tion. Another argument is that Euclidean geometry is sometimes difficult to use in algorithms, with particular cases arising from non-generic situations (e.g. -
Arxiv:Gr-Qc/0611154 V1 30 Nov 2006 Otx Fcra Geometry
MacDowell–Mansouri Gravity and Cartan Geometry Derek K. Wise Department of Mathematics University of California Riverside, CA 92521, USA email: [email protected] November 29, 2006 Abstract The geometric content of the MacDowell–Mansouri formulation of general relativity is best understood in terms of Cartan geometry. In particular, Cartan geometry gives clear geomet- ric meaning to the MacDowell–Mansouri trick of combining the Levi–Civita connection and coframe field, or soldering form, into a single physical field. The Cartan perspective allows us to view physical spacetime as tangentially approximated by an arbitrary homogeneous ‘model spacetime’, including not only the flat Minkowski model, as is implicitly used in standard general relativity, but also de Sitter, anti de Sitter, or other models. A ‘Cartan connection’ gives a prescription for parallel transport from one ‘tangent model spacetime’ to another, along any path, giving a natural interpretation of the MacDowell–Mansouri connection as ‘rolling’ the model spacetime along physical spacetime. I explain Cartan geometry, and ‘Cartan gauge theory’, in which the gauge field is replaced by a Cartan connection. In particular, I discuss MacDowell–Mansouri gravity, as well as its recent reformulation in terms of BF theory, in the arXiv:gr-qc/0611154 v1 30 Nov 2006 context of Cartan geometry. 1 Contents 1 Introduction 3 2 Homogeneous spacetimes and Klein geometry 8 2.1 Kleingeometry ................................... 8 2.2 MetricKleingeometry ............................. 10 2.3 Homogeneousmodelspacetimes. ..... 11 3 Cartan geometry 13 3.1 Ehresmannconnections . .. .. .. .. .. .. .. .. 13 3.2 Definition of Cartan geometry . ..... 14 3.3 Geometric interpretation: rolling Klein geometries . .............. 15 3.4 ReductiveCartangeometry . 17 4 Cartan-type gauge theory 20 4.1 Asequenceofbundles ............................. -
Robot 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 . Understand point, line, plane interactions geometrically . Analytical calculations with lines, points and planes . Understand the difference between Euclidean and projective space . Understand the properties of parallel lines and planes in projective space . Understand the concept of the line and plane at infinity 2 Outline . 1D projective geometry . 2D projective geometry ▫ Homogeneous coordinates ▫ Points, Lines ▫ Duality . 3D projective geometry ▫ Points, Lines, Planes ▫ Duality ▫ Plane at infinity 3 Literature . Multiple View Geometry in Computer Vision. Richard Hartley and Andrew Zisserman. Cambridge University Press, March 2004. Mundy, J.L. and Zisserman, A., Geometric Invariance in Computer Vision, Appendix: Projective Geometry for Machine Vision, MIT Press, Cambridge, MA, 1992 . Available online: www.cs.cmu.edu/~ph/869/papers/zisser-mundy.pdf 4 Motivation – Image formation [Source: Charles Gunn] 5 Motivation – Parallel lines [Source: Flickr] 6 Motivation – Epipolar constraint X world point epipolar plane x x’ x‘TEx=0 C T C’ R 7 Euclidean geometry vs. projective geometry Definitions: . Geometry is the teaching of points, lines, planes and their relationships and properties (angles) . Geometries are defined based on invariances (what is changing if you transform a configuration of points, lines etc.) . Geometric transformations -
Two-Dimensional Geometric Transformations
Two-Dimensional Geometric Transformations 1 Basic Two-Dimensional Geometric Transformations 2 Matrix Representations and Homogeneous Coordinates 3 Inverse Transformations 4 Two-Dimensional Composite Transformations 5 Other Two-Dimensional Transformations 6 Raster Methods for Geometric Transformations 7 OpenGL Raster Transformations 8 Transformations between Two-Dimensional Coordinate Systems 9 OpenGL Functions for Two-Dimensional Geometric Transformations 10 OpenGL Geometric-Transformation o far, we have seen how we can describe a scene in Programming Examples S 11 Summary terms of graphics primitives, such as line segments and fill areas, and the attributes associated with these primitives. Also, we have explored the scan-line algorithms for displaying output primitives on a raster device. Now, we take a look at transformation operations that we can apply to objects to reposition or resize them. These operations are also used in the viewing routines that convert a world-coordinate scene description to a display for an output device. In addition, they are used in a variety of other applications, such as computer-aided design (CAD) and computer animation. An architect, for example, creates a layout by arranging the orientation and size of the component parts of a design, and a computer animator develops a video sequence by moving the “camera” position or the objects in a scene along specified paths. Operations that are applied to the geometric description of an object to change its position, orientation, or size are called geometric transformations. Sometimes geometric transformations are also referred to as modeling transformations, but some graphics packages make a From Chapter 7 of Computer Graphics with OpenGL®, Fourth Edition, Donald Hearn, M. -
The Affine Group of a Lie Group
THE AFFINE GROUP OF A LIE GROUP JOSEPH A. WOLF1 1. If G is a Lie group, then the group Aut(G) of all continuous auto- morphisms of G has a natural Lie group structure. This gives the semi- direct product A(G) = G-Aut(G) the structure of a Lie group. When G is a vector group R", A(G) is the ordinary affine group A(re). Follow- ing L. Auslander [l ] we will refer to A(G) as the affine group of G, and regard it as a transformation group on G by (g, a): h-^g-a(h) where g, hEG and aGAut(G) ; in the case of a vector group, this is the usual action on A(») on R". If B is a compact subgroup of A(n), then it is well known that B has a fixed point on R", i.e., that there is a point xGR" such that b(x)=x for every bEB. For A(ra) is contained in the general linear group GL(« + 1, R) in the usual fashion, and B (being compact) must be conjugate to a subgroup of the orthogonal group 0(w + l). This conjugation can be done leaving fixed the (« + 1, w + 1)-place matrix entries, and is thus possible by an element of k(n). This done, the translation-parts of elements of B must be zero, proving the assertion. L. Auslander [l] has extended this theorem to compact abelian subgroups of A(G) when G is connected, simply connected and nil- potent. We will give a further extension. -
Gravity and Gauge
Gravity and Gauge Nicholas J. Teh June 29, 2011 Abstract Philosophers of physics and physicists have long been intrigued by the analogies and disanalogies between gravitational theories and (Yang-Mills-type) gauge theories. Indeed, repeated attempts to col- lapse these disanalogies have made us acutely aware that there are fairly general obstacles to doing so. Nonetheless, there is a special case (viz. that of (2+1) spacetime dimensions) in which gravity is often claimed to be identical to a gauge theory. We subject this claim to philosophical scrutiny in this paper: in particular, we (i) analyze how the standard disanalogies can be overcome in (2+1) dimensions, and (ii) consider whether (i) really licenses the interpretation of (2+1) gravity as a gauge theory. Our conceptual analysis reveals more subtle disanalogies between gravity and gauge, and connects these to interpretive issues in classical and quantum gravity. Contents 1 Introduction 2 1.1 Motivation . 4 1.2 Prospectus . 6 2 Disanalogies 6 3 3D Gravity and Gauge 8 3.1 (2+1) Gravity . 9 3.2 (2+1) Chern-Simons . 14 3.2.1 Cartan geometry . 15 3.2.2 Overcoming (Obst-Gauge) via Cartan connections . 17 3.3 Disanalogies collapsed . 21 1 4 Two more disanalogies 22 4.1 What about the symmetries? . 23 4.2 The phase spaces of the two theories . 25 5 Summary and conclusion 30 1 Introduction `The proper method of philosophy consists in clearly conceiving the insoluble problems in all their insolubility and then in simply contemplating them, fixedly and tirelessly, year after year, without any -
Estimating Projective Transformation Matrix (Collineation, Homography)
Estimating Projective Transformation Matrix (Collineation, Homography) Zhengyou Zhang Microsoft Research One Microsoft Way, Redmond, WA 98052, USA E-mail: [email protected] November 1993; Updated May 29, 2010 Microsoft Research Techical Report MSR-TR-2010-63 Note: The original version of this report was written in November 1993 while I was at INRIA. It was circulated among very few people, and never published. I am now publishing it as a tech report, adding Section 7, with the hope that it could be useful to more people. Abstract In many applications, one is required to estimate the projective transformation between two sets of points, which is also known as collineation or homography. This report presents a number of techniques for this purpose. Contents 1 Introduction 2 2 Method 1: Five-correspondences case 3 3 Method 2: Compute P together with the scalar factors 3 4 Method 3: Compute P only (a batch approach) 5 5 Method 4: Compute P only (an iterative approach) 6 6 Method 5: Compute P through normalization 7 7 Method 6: Maximum Likelihood Estimation 8 1 1 Introduction Projective Transformation is a concept used in projective geometry to describe how a set of geometric objects maps to another set of geometric objects in projective space. The basic intuition behind projective space is to add extra points (points at infinity) to Euclidean space, and the geometric transformation allows to move those extra points to traditional points, and vice versa. Homogeneous coordinates are used in projective space much as Cartesian coordinates are used in Euclidean space. A point in two dimensions is described by a 3D vector. -
Geometric Transformation Techniques for Digital I~Iages: a Survey
GEOMETRIC TRANSFORMATION TECHNIQUES FOR DIGITAL I~IAGES: A SURVEY George Walberg Department of Computer Science Columbia University New York, NY 10027 [email protected] December 1988 Technical Report CUCS-390-88 ABSTRACT This survey presents a wide collection of algorithms for the geometric transformation of digital images. Efficient image transformation algorithms are critically important to the remote sensing, medical imaging, computer vision, and computer graphics communities. We review the growth of this field and compare all the described algorithms. Since this subject is interdisci plinary, emphasis is placed on the unification of the terminology, motivation, and contributions of each technique to yield a single coherent framework. This paper attempts to serve a dual role as a survey and a tutorial. It is comprehensive in scope and detailed in style. The primary focus centers on the three components that comprise all geometric transformations: spatial transformations, resampling, and antialiasing. In addition, considerable attention is directed to the dramatic progress made in the development of separable algorithms. The text is supplemented with numerous examples and an extensive bibliography. This work was supponed in part by NSF grant CDR-84-21402. 6.5.1 Pyramids 68 6.5.2 Summed-Area Tables 70 6.6 FREQUENCY CLAMPING 71 6.7 ANTIALIASED LINES AND TEXT 71 6.8 DISCUSSION 72 SECTION 7 SEPARABLE GEOMETRIC TRANSFORMATION ALGORITHMS 7.1 INTRODUCTION 73 7.1.1 Forward Mapping 73 7.1.2 Inverse Mapping 73 7.1.3 Separable Mapping 74 7.2 2-PASS TRANSFORMS 75 7.2.1 Catmull and Smith, 1980 75 7.2.1.1 First Pass 75 7.2.1.2 Second Pass 75 7.2.1.3 2-Pass Algorithm 77 7.2.1.4 An Example: Rotation 77 7.2.1.5 Bottleneck Problem 78 7.2.1.6 Foldover Problem 79 7.2.2 Fraser, Schowengerdt, and Briggs, 1985 80 7.2.3 Fant, 1986 81 7.2.4 Smith, 1987 82 7.3 ROTATION 83 7.3.1 Braccini and Marino, 1980 83 7.3.2 Weiman, 1980 84 7.3.3 Paeth, 1986/ Tanaka. -
Perspectives on Projective Geometry • Jürgen Richter-Gebert
Perspectives on Projective Geometry • Jürgen Richter-Gebert Perspectives on Projective Geometry A Guided Tour Through Real and Complex Geometry 123 Jürgen Richter-Gebert TU München Zentrum Mathematik (M10) LS Geometrie Boltzmannstr. 3 85748 Garching Germany [email protected] ISBN 978-3-642-17285-4 e-ISBN 978-3-642-17286-1 DOI 10.1007/978-3-642-17286-1 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2011921702 Mathematics Subject Classification (2010): 51A05, 51A25, 51M05, 51M10 c Springer-Verlag Berlin Heidelberg 2011 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation,reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Cover design: deblik, Berlin Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com) About This Book Let no one ignorant of geometry enter here! Entrance to Plato’s academy Once or twice she had peeped into the book her sister was reading, but it had no pictures or conversations in it, “and what is the use of a book,” thought Alice, “without pictures or conversations?” Lewis Carroll, Alice’s Adventures in Wonderland Geometry is the mathematical discipline that deals with the interrelations of objects in the plane, in space, or even in higher dimensions.