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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. -
Slices, Slabs, and Sections of the Unit Hypercube
Slices, Slabs, and Sections of the Unit Hypercube Jean-Luc Marichal Michael J. Mossinghoff Institute of Mathematics Department of Mathematics University of Luxembourg Davidson College 162A, avenue de la Fa¨ıencerie Davidson, NC 28035-6996 L-1511 Luxembourg USA Luxembourg [email protected] [email protected] Submitted: July 27, 2006; Revised: August 6, 2007; Accepted: January 21, 2008; Published: January 29, 2008 Abstract Using combinatorial methods, we derive several formulas for the volume of convex bodies obtained by intersecting a unit hypercube with a halfspace, or with a hyperplane of codimension 1, or with a flat defined by two parallel hyperplanes. We also describe some of the history of these problems, dating to P´olya’s Ph.D. thesis, and we discuss several applications of these formulas. Subject Class: Primary: 52A38, 52B11; Secondary: 05A19, 60D05. Keywords: Cube slicing, hyperplane section, signed decomposition, volume, Eulerian numbers. 1 Introduction In this note we study the volumes of portions of n-dimensional cubes determined by hyper- planes. More precisely, we study slices created by intersecting a hypercube with a halfspace, slabs formed as the portion of a hypercube lying between two parallel hyperplanes, and sections obtained by intersecting a hypercube with a hyperplane. These objects occur natu- rally in several fields, including probability, number theory, geometry, physics, and analysis. In this paper we describe an elementary combinatorial method for calculating volumes of arbitrary slices, slabs, and sections of a unit cube. We also describe some applications that tie these geometric results to problems in analysis and combinatorics. Some of the results we obtain here have in fact appeared earlier in other contexts. -
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. -
The Projective Geometry of the Spacetime Yielded by Relativistic Positioning Systems and Relativistic Location Systems Jacques Rubin
The projective geometry of the spacetime yielded by relativistic positioning systems and relativistic location systems Jacques Rubin To cite this version: Jacques Rubin. The projective geometry of the spacetime yielded by relativistic positioning systems and relativistic location systems. 2014. hal-00945515 HAL Id: hal-00945515 https://hal.inria.fr/hal-00945515 Submitted on 12 Feb 2014 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. The projective geometry of the spacetime yielded by relativistic positioning systems and relativistic location systems Jacques L. Rubin (email: [email protected]) Université de Nice–Sophia Antipolis, UFR Sciences Institut du Non-Linéaire de Nice, UMR7335 1361 route des Lucioles, F-06560 Valbonne, France (Dated: February 12, 2014) As well accepted now, current positioning systems such as GPS, Galileo, Beidou, etc. are not primary, relativistic systems. Nevertheless, genuine, relativistic and primary positioning systems have been proposed recently by Bahder, Coll et al. and Rovelli to remedy such prior defects. These new designs all have in common an equivariant conformal geometry featuring, as the most basic ingredient, the spacetime geometry. In a first step, we show how this conformal aspect can be the four-dimensional projective part of a larger five-dimensional geometry. -
Linear Algebra Handout
Artificial Intelligence: 6.034 Massachusetts Institute of Technology April 20, 2012 Spring 2012 Recitation 10 Linear Algebra Review • A vector is an ordered list of values. It is often denoted using angle brackets: ha; bi, and its variable name is often written in bold (z) or with an arrow (~z). We can refer to an individual element of a vector using its index: for example, the first element of z would be z1 (or z0, depending on how we're indexing). Each element of a vector generally corresponds to a particular dimension or feature, which could be discrete or continuous; often you can think of a vector as a point in Euclidean space. p 2 2 2 • The magnitude (also called norm) of a vector x = hx1; x2; :::; xni is x1 + x2 + ::: + xn, and is denoted jxj or kxk. • The sum of a set of vectors is their elementwise sum: for example, ha; bi + hc; di = ha + c; b + di (so vectors can only be added if they are the same length). The dot product (also called scalar product) of two vectors is the sum of their elementwise products: for example, ha; bi · hc; di = ac + bd. The dot product x · y is also equal to kxkkyk cos θ, where θ is the angle between x and y. • A matrix is a generalization of a vector: instead of having just one row or one column, it can have m rows and n columns. A square matrix is one that has the same number of rows as columns. A matrix's variable name is generally a capital letter, often written in bold. -
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. -
A Modular Compactification of the General Linear Group
Documenta Math. 553 A Modular Compactification of the General Linear Group Ivan Kausz Received: June 2, 2000 Communicated by Peter Schneider Abstract. We define a certain compactifiction of the general linear group and give a modular description for its points with values in arbi- trary schemes. This is a first step in the construction of a higher rank generalization of Gieseker's degeneration of moduli spaces of vector bundles over a curve. We show that our compactification has simi- lar properties as the \wonderful compactification” of algebraic groups of adjoint type as studied by de Concini and Procesi. As a byprod- uct we obtain a modular description of the points of the wonderful compactification of PGln. 1991 Mathematics Subject Classification: 14H60 14M15 20G Keywords and Phrases: moduli of vector bundles on curves, modular compactification, general linear group 1. Introduction In this paper we give a modular description of a certain compactification KGln of the general linear group Gln. The variety KGln is constructed as follows: First one embeds Gln in the obvious way in the projective space which contains the affine space of n × n matrices as a standard open set. Then one succes- sively blows up the closed subschemes defined by the vanishing of the r × r subminors (1 ≤ r ≤ n), along with the intersection of these subschemes with the hyperplane at infinity. We were led to the problem of finding a modular description of KGln in the course of our research on the degeneration of moduli spaces of vector bundles. Let me explain in some detail the relevance of compactifications of Gln in this context. -
Cutting Hyperplane Arrangements*
Discrete Comput Geom 6:385 406 (1991) GDieometryscrete & Computational 1991 Springer-VerlagNew York Inc Cutting Hyperplane Arrangements* Jifi Matou~ek Department of Applied Mathematics, Charles University, Malostransk6 nfim. 25, 118 00 Praha 1. Czechoslovakia Abstract. We consider a collection H of n hyperplanes in E a (where the dimension d is fixed). An e.-cuttin9 for H is a collection of (possibly unbounded) d-dimensional simplices with disjoint interiors, which cover all E a and such that the interior of any simplex is intersected by at most en hyperplanes of H. We give a deterministic algorithm for finding a (1/r)-cutting with O(r d) simplices (which is asymptotically optimal). For r < n I 6, where 6 > 0 is arbitrary but fixed, the running time of this algorithm is O(n(log n)~ In the plane we achieve a time bound O(nr) for r _< n I-6, which is optimal if we also want to compute the collection of lines intersecting each simplex of the cutting. This improves a result of Agarwal, and gives a conceptually simpler algorithm. For an n point set X ~_ E d and a parameter r, we can deterministically compute a (1/r)-net of size O(r log r) for the range space (X, {X c~ R; R is a simplex}), in time O(n(log n)~ d- 1 + roe11). The size of the (1/r)-net matches the best known existence result. By a simple transformation, this allows us to find e-nets for other range spaces usually encountered in computational geometry. -
15 BASIC PROPERTIES of CONVEX POLYTOPES Martin Henk, J¨Urgenrichter-Gebert, and G¨Unterm
15 BASIC PROPERTIES OF CONVEX POLYTOPES Martin Henk, J¨urgenRichter-Gebert, and G¨unterM. Ziegler INTRODUCTION Convex polytopes are fundamental geometric objects that have been investigated since antiquity. The beauty of their theory is nowadays complemented by their im- portance for many other mathematical subjects, ranging from integration theory, algebraic topology, and algebraic geometry to linear and combinatorial optimiza- tion. In this chapter we try to give a short introduction, provide a sketch of \what polytopes look like" and \how they behave," with many explicit examples, and briefly state some main results (where further details are given in subsequent chap- ters of this Handbook). We concentrate on two main topics: • Combinatorial properties: faces (vertices, edges, . , facets) of polytopes and their relations, with special treatments of the classes of low-dimensional poly- topes and of polytopes \with few vertices;" • Geometric properties: volume and surface area, mixed volumes, and quer- massintegrals, including explicit formulas for the cases of the regular simplices, cubes, and cross-polytopes. We refer to Gr¨unbaum [Gr¨u67]for a comprehensive view of polytope theory, and to Ziegler [Zie95] respectively to Gruber [Gru07] and Schneider [Sch14] for detailed treatments of the combinatorial and of the convex geometric aspects of polytope theory. 15.1 COMBINATORIAL STRUCTURE GLOSSARY d V-polytope: The convex hull of a finite set X = fx1; : : : ; xng of points in R , n n X i X P = conv(X) := λix λ1; : : : ; λn ≥ 0; λi = 1 : i=1 i=1 H-polytope: The solution set of a finite system of linear inequalities, d T P = P (A; b) := x 2 R j ai x ≤ bi for 1 ≤ i ≤ m ; with the extra condition that the set of solutions is bounded, that is, such that m×d there is a constant N such that jjxjj ≤ N holds for all x 2 P . -
Exact Hyperplane Covers for Subsets of the Hypercube 1
EXACT HYPERPLANE COVERS FOR SUBSETS OF THE HYPERCUBE JAMES AARONSON, CARLA GROENLAND, ANDRZEJ GRZESIK, TOM JOHNSTON, AND BARTLOMIEJKIELAK Abstract. Alon and F¨uredi(1993) showed that the number of hyper- planes required to cover f0; 1gn n f0g without covering 0 is n. We initiate the study of such exact hyperplane covers of the hypercube for other sub- sets of the hypercube. In particular, we provide exact solutions for covering f0; 1gn while missing up to four points and give asymptotic bounds in the general case. Several interesting questions are left open. 1. Introduction A vector v 2 Rn and a scalar α 2 R determine the hyperplane n fx 2 R : hv; xi := v1x1 + ··· + vnxn = αg in Rn. How many hyperplanes are needed to cover f0; 1gn? Only two are required; for instance, fx : x1 = 0g and fx : x1 = 1g will do. What happens however if 0 2 Rn is not allowed on any of the hyperplanes? We can `exactly' cover f0; 1gn n f0g with n hyperplanes: for example, the collections ffx : Pn xi = 1g : i 2 [n]g or ffx : i=1 xi = jg : j 2 [n]g can be used, where [n] := f1; 2; : : : ; ng. Alon and F¨uredi[2] showed that in fact n hyperplanes are always necessary. Recently, a variation was studied by Clifton and Huang [5], in which they require that each point from f0; 1gn n f0g is covered at least k times for some k 2 N (while 0 is never covered). Another natural generalisation is to put more than just 0 to the set of points we wish to avoid in the cover.