A Modular Compactification of the General Linear Group
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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. -
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 -
Collineations in Perspective
Collineations in Perspective Now that we have a decent grasp of one-dimensional projectivities, we move on to their two di- mensional analogs. Although they are more complicated, in a sense, they may be easier to grasp because of the many applications to perspective drawing. Speaking of, let's return to the triangle on the window and its shadow in its full form instead of only looking at one line. Perspective Collineation In one dimension, a perspectivity is a bijective mapping from a line to a line through a point. In two dimensions, a perspective collineation is a bijective mapping from a plane to a plane through a point. To illustrate, consider the triangle on the window plane and its shadow on the ground plane as in Figure 1. We can see that every point on the triangle on the window maps to exactly one point on the shadow, but the collineation is from the entire window plane to the entire ground plane. We understand the window plane to extend infinitely in all directions (even going through the ground), the ground also extends infinitely in all directions (we will assume that the earth is flat here), and we map every point on the window to a point on the ground. Looking at Figure 2, we see that the lamp analogy breaks down when we consider all lines through O. Although it makes sense for the base of the triangle on the window mapped to its shadow on 1 the ground (A to A0 and B to B0), what do we make of the mapping C to C0, or D to D0? C is on the window plane, underground, while C0 is on the ground. -
4 Jul 2018 Is Spacetime As Physical As Is Space?
Is spacetime as physical as is space? Mayeul Arminjon Univ. Grenoble Alpes, CNRS, Grenoble INP, 3SR, F-38000 Grenoble, France Abstract Two questions are investigated by looking successively at classical me- chanics, special relativity, and relativistic gravity: first, how is space re- lated with spacetime? The proposed answer is that each given reference fluid, that is a congruence of reference trajectories, defines a physical space. The points of that space are formally defined to be the world lines of the congruence. That space can be endowed with a natural structure of 3-D differentiable manifold, thus giving rise to a simple notion of spatial tensor — namely, a tensor on the space manifold. The second question is: does the geometric structure of the spacetime determine the physics, in particular, does it determine its relativistic or preferred- frame character? We find that it does not, for different physics (either relativistic or not) may be defined on the same spacetime structure — and also, the same physics can be implemented on different spacetime structures. MSC: 70A05 [Mechanics of particles and systems: Axiomatics, founda- tions] 70B05 [Mechanics of particles and systems: Kinematics of a particle] 83A05 [Relativity and gravitational theory: Special relativity] 83D05 [Relativity and gravitational theory: Relativistic gravitational theories other than Einstein’s] Keywords: Affine space; classical mechanics; special relativity; relativis- arXiv:1807.01997v1 [gr-qc] 4 Jul 2018 tic gravity; reference fluid. 1 Introduction and Summary What is more “physical”: space or spacetime? Today, many physicists would vote for the second one. Whereas, still in 1905, spacetime was not a known concept. It has been since realized that, even in classical mechanics, space and time are coupled: already in the minimum sense that space does not exist 1 without time and vice-versa, and also through the Galileo transformation. -
Finite Projective Geometries 243
FINITE PROJECTÎVEGEOMETRIES* BY OSWALD VEBLEN and W. H. BUSSEY By means of such a generalized conception of geometry as is inevitably suggested by the recent and wide-spread researches in the foundations of that science, there is given in § 1 a definition of a class of tactical configurations which includes many well known configurations as well as many new ones. In § 2 there is developed a method for the construction of these configurations which is proved to furnish all configurations that satisfy the definition. In §§ 4-8 the configurations are shown to have a geometrical theory identical in most of its general theorems with ordinary projective geometry and thus to afford a treatment of finite linear group theory analogous to the ordinary theory of collineations. In § 9 reference is made to other definitions of some of the configurations included in the class defined in § 1. § 1. Synthetic definition. By a finite projective geometry is meant a set of elements which, for sugges- tiveness, are called points, subject to the following five conditions : I. The set contains a finite number ( > 2 ) of points. It contains subsets called lines, each of which contains at least three points. II. If A and B are distinct points, there is one and only one line that contains A and B. HI. If A, B, C are non-collinear points and if a line I contains a point D of the line AB and a point E of the line BC, but does not contain A, B, or C, then the line I contains a point F of the line CA (Fig. -
Affine and Euclidean Geometry Affine and Projective Geometry, E
AFFINE AND PROJECTIVE GEOMETRY, E. Rosado & S.L. Rueda CHAPTER II: AFFINE AND EUCLIDEAN GEOMETRY AFFINE AND PROJECTIVE GEOMETRY, E. Rosado & S.L. Rueda 1. AFFINE SPACE 1.1 Definition of affine space A real affine space is a triple (A; V; φ) where A is a set of points, V is a real vector space and φ: A × A −! V is a map verifying: 1. 8P 2 A and 8u 2 V there exists a unique Q 2 A such that φ(P; Q) = u: 2. φ(P; Q) + φ(Q; R) = φ(P; R) for everyP; Q; R 2 A. Notation. We will write φ(P; Q) = PQ. The elements contained on the set A are called points of A and we will say that V is the vector space associated to the affine space (A; V; φ). We define the dimension of the affine space (A; V; φ) as dim A = dim V: AFFINE AND PROJECTIVE GEOMETRY, E. Rosado & S.L. Rueda Examples 1. Every vector space V is an affine space with associated vector space V . Indeed, in the triple (A; V; φ), A =V and the map φ is given by φ: A × A −! V; φ(u; v) = v − u: 2. According to the previous example, (R2; R2; φ) is an affine space of dimension 2, (R3; R3; φ) is an affine space of dimension 3. In general (Rn; Rn; φ) is an affine space of dimension n. 1.1.1 Properties of affine spaces Let (A; V; φ) be a real affine space. The following statemens hold: 1. -
Chapter II. Affine Spaces
II.1. Spaces 1 Chapter II. Affine Spaces II.1. Spaces Note. In this section, we define an affine space on a set X of points and a vector space T . In particular, we use affine spaces to define a tangent space to X at point x. In Section VII.2 we define manifolds on affine spaces by mapping open sets of the manifold (taken as a Hausdorff topological space) into the affine space. Ultimately, we think of a manifold as pieces of the affine space which are “bent and pasted together” (like paper mache). We also consider subspaces of an affine space and translations of subspaces. We conclude with a discussion of coordinates and “charts” on an affine space. Definition II.1.01. An affine space with vector space T is a nonempty set X of points and a vector valued map d : X × X → T called a difference function, such that for all x, y, z ∈ X: (A i) d(x, y) + d(y, z) = d(x, z), (A ii) the restricted map dx = d|{x}×X : {x} × X → T defined as mapping (x, y) 7→ d(x, y) is a bijection. We denote both the set and the affine space as X. II.1. Spaces 2 Note. We want to think of a vector as an arrow between two points (the “head” and “tail” of the vector). So d(x, y) is the vector from point x to point y. Then we see that (A i) is just the usual “parallelogram property” of the addition of vectors. -
The Euler Characteristic of an Affine Space Form Is Zero by B
BULLETIN OF THE AMERICAN MATHEMATICAL SOCIETY Volume 81, Number 5, September 1975 THE EULER CHARACTERISTIC OF AN AFFINE SPACE FORM IS ZERO BY B. KOST ANT AND D. SULLIVAN Communicated by Edgar H. Brown, Jr., May 27, 1975 An affine space form is a compact manifold obtained by forming the quo tient of ordinary space i?" by a discrete group of affine transformations. An af fine manifold is one which admits a covering by coordinate systems where the overlap transformations are restrictions of affine transformations on Rn. Affine space forms are characterized among affine manifolds by a completeness property of straight lines in the universal cover. If n — 2m is even, it is unknown whether or not the Euler characteristic of a compact affine manifold can be nonzero. The curvature definition of charac teristic classes shows that all classes, except possibly the Euler class, vanish. This argument breaks down for the Euler class because the pfaffian polynomials which would figure in the curvature argument is not invariant for Gl(nf R) but only for SO(n, R). We settle this question for the subclass of affine space forms by an argument analogous to the potential curvature argument. The point is that all the affine transformations x \—> Ax 4- b in the discrete group of the space form satisfy: 1 is an eigenvalue of A. For otherwise, the trans formation would have a fixed point. But then our theorem is proved by the fol lowing p THEOREM. If a representation G h-• Gl(n, R) satisfies 1 is an eigenvalue of p(g) for all g in G, then any vector bundle associated by p to a principal G-bundle has Euler characteristic zero. -
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. -
Chapter 2 Affine Algebraic Geometry
Chapter 2 Affine Algebraic Geometry 2.1 The Algebraic-Geometric Dictionary The correspondence between algebra and geometry is closest in affine algebraic geom- etry, where the basic objects are solutions to systems of polynomial equations. For many applications, it suffices to work over the real R, or the complex numbers C. Since important applications such as coding theory or symbolic computation require finite fields, Fq , or the rational numbers, Q, we shall develop algebraic geometry over an arbitrary field, F, and keep in mind the important cases of R and C. For algebraically closed fields, there is an exact and easily motivated correspondence be- tween algebraic and geometric concepts. When the field is not algebraically closed, this correspondence weakens considerably. When that occurs, we will use the case of algebraically closed fields as our guide and base our definitions on algebra. Similarly, the strongest and most elegant results in algebraic geometry hold only for algebraically closed fields. We will invoke the hypothesis that F is algebraically closed to obtain these results, and then discuss what holds for arbitrary fields, par- ticularly the real numbers. Since many important varieties have structures which are independent of the field of definition, we feel this approach is justified—and it keeps our presentation elementary and motivated. Lastly, for the most part it will suffice to let F be R or C; not only are these the most important cases, but they are also the sources of our geometric intuitions. n Let A denote affine n-space over F. This is the set of all n-tuples (t1,...,tn) of elements of F. -
Affine Spaces Within Projective Spaces
Affine Spaces within Projective Spaces Andrea Blunck∗ Hans Havlicek Abstract We endow the set of complements of a fixed subspace of a projective space with the structure of an affine space, and show that certain lines of such an affine space are affine reguli or cones over affine reguli. Moreover, we apply our concepts to the problem of describing dual spreads. We do not assume that the projective space is finite- dimensional or pappian. Mathematics Subject Classification (1991): 51A30, 51A40, 51A45. Key Words: Affine space, regulus, dual spread. 1 Introduction The aim of this paper is to equip the set of complements of a distinguished subspace in an arbitrary projective space with the structure of an affine space. In this introductory section we first formulate the problem more exactly and then describe certain special cases that have already been treated in the literature. Let K be a not necessarily commutative field, and let V be a left vector space over K with arbitrary | not necessarily finite | dimension. Moreover, fix a subspace W of V . We are interested in the set := S W V = W S (1) S f ≤ j ⊕ g of all complements of W . Note that often we shall take the projective point of view; then we identify with the set S of all those projective subspaces of the projective space P(K; V ) that are complementary to the fixed projective subspace induced by W . In the special case that W is a hyperplane, the set obviously is the point set of an affine S space, namely, of the affine derivation of P(K; V ) w.r.t. -
Statistical Manifold As an Affine Space
ARTICLE IN PRESS Journal of Mathematical Psychology 50 (2006) 60–65 www.elsevier.com/locate/jmp Statistical manifold as an affine space: A functional equation approach Jun Zhanga,Ã, Peter Ha¨sto¨b,1 aDepartment of Psychology, 525 East University Ave., University of Michigan, Ann Arbor, MI 48109-1109, USA bDepartment of Mathematics and Statistics, P. O. Box 68, 00014 University of Helsinki, Finland Received 7 January 2005; received in revised form 9 July 2005 Available online 11 October 2005 Abstract A statistical manifold Mm consists of positive functions f such that fdm defines a probability measure. In order to define an atlas on the manifold, it is viewed as an affine space associated with a subspace of the Orlicz space LF. This leads to a functional equation whose solution, after imposing the linearity constrain in line with the vector space assumption, gives rise to a general form of mappings between the affine probability manifold and the vector (Orlicz) space. These results generalize the exponential statistical manifold and clarify some foundational issues in non-parametric information geometry. r 2005 Published by Elsevier Inc. Keywords: Probability density function; Exponential family; Non-parameteric; Information geometry 1. Introduction theory, stochastic process, neural computation, machine learning, Bayesian statistics, and other related fields (Amari, Information geometry investigates the differential geo- 1985; Amari & Nagaoka, 1993/2000). Recently, an interest in metric structure of the manifold of probability density the geometry associated with non-parametric probability functions with continuous support (or probability distribu- densities has arisen (Pistone & Sempi, 1995; Giblisco & tions with discrete support). Treating a family of parametric Pistone, 1998; Pistone & Rogantin, 1999; Grasselli, 2005).