4 Lines and Cross Ratios

Total Page:16

File Type:pdf, Size:1020Kb

4 Lines and Cross Ratios 4 Lines and cross ratios At this stage of this monograph we enter a significant didactic problem. There are three concepts which are intimately related and which unfold their full power only if they play together. These concepts are performing calculations with geometric objects, determinants and determinant algebra and geometric incidence theorems. The reader may excuse that in a beginners book that makes only little assumptions on the pre-knowledge one has to introduce these concepts one after the other. Thereby we will sacrifice some mathe- matical beauty for clearness of exposition. Still we highly recommend to read the following chapters (at least) twice. In order to get an impression of the interplay of the different concepts. This and the next section is devoted to the relations of RP2 to calcula- tions in the underlying field R. For this we will first find methods to relate points in a projective plane to the coordinates over R. Then we will show that elementary operations like addition and multiplication can be mimicked in a purely geometric fashion. Finally we will use these facts to derive in- teresting statements about the structure of projective planes and projective transformations. 4.1 Coordinates on a line Assume that two distinct points [p] and [q] in R are given. How can we describe the set of all points on the line throughP these two points. It is clear that we can implicitly describe them by first calculating the homogeneous coordinates of the line through p and q and then selecting all points that are incident to this line. However, there is also a very direct and explicit way of describing these points, as the following theorem shows: Lemma 4.1. Let [p] and [q] be two distinct points in R. The set of all points on the line through these points is given by P [λ p + µ q] λ, µ R with λ or µ being non-zero . { · · ∈ } ! ! 74 4 Lines and cross ratios Proof. The proof is an exercise in elementary linear algebra. For λ, µ R (with λ or µ being non-zero) let r = λ p + µ q be a representative∈ of a point. We have to show that this point is· on the· line through [p] and [q]. In other words we must prove that λ p + µ q, p q = 0. This is an immediate consequence of the arithmetic rules" · for the· scalar× $ and vector product. We have: λ p + µ q, p q = λ p, p q + µ q, p q " · · × $ = λ" p,· p ×q +$ µ"q, p· q× $ = λ" 0+×µ $0 " × $ =0· · The first two equations hold by multilinearity of the scalar product. The third equation comes from the fact that p, p q and q, p q are always zero. Conversely, assume that [r] is a" point× on$ the" line× spanned$ by [p] and [q]. This means that there is a vector l R3 with ∈ l, p = l, q = l, r =0. " $ " $ " $ The points [p] and [q] are distinct, thus p and q are linearly independent. Consider the matrix M with row vectors p, q, r. This matrix cannot have full rank since the product M l is the zero vector. Thus r must lie in the span of p and q. Since r itself is not· the zero vector we have a representation of the form r = λ p + µ q with λ or µ being non-zero. · · %& The last proof is simply an algebraic version of the geometric fact that we consider a line as the linear span of two distinct points on it. In the form r = λ p+µ q we can simultaneously multiply both parameters λ and µ by the same factor· ·α and still obtain the same point [r]. If one of the two parameters is non-zero we can normalize this parameter to 1. Using this fact we can express almost all points on the line through [p] and [q] by the expression λ p + q; λ R. The only point we miss is [p] itself. Similarly, we obtain all· points except∈ of [q] by the expression p + µ q. Let us interpret these relations within the framework of concrete coordinates· of points in the standard embedding of the Euclidean plane. For this we set o = (0, 0, 1) (the corresponding point [o] represents the origin of the coordinate system of R2 embedded on the z = 1 plane) and x∞ = (1, 0, 0) (the corresponding point [x∞] represents the infinite point in direction of the x-axis). The points represented by vectors λ x∞ + o =(λ, 0, 1) are the finite points on the line joining [o] and [x∞] (this is· the embedded x-axis). Each such point (λ, 0, 1)is bijectively associated to a real parameter λ R. It is important∈ to notice that this way of assigning real numbers to points in the projective plane is heavily dependent on the choice of the reference points. It will be our next aim to reconstruct this relation of real parameters in a purely projective setup. 4.2 The real projective line 75 4.2 The real projective line y ( 1, 1) (1, 1) − (2, 1) x (1, 0) Fig. 4.1. Homogeneous coordinates on the real projective line. The last section focused on viewing a single line in R from the projective viewpoint. In the expression λ p + µ q the parametersP (λ, µ) themselves can be considered as homogeneous· coordinates· on the 1-dimensional projective line spanned by p and q. In this section we want to step back from our consid- erations of the projective plane and study the situation of a (self-contained) projective line. We will do this in analogy to the homogeneous setup for the projective plane. For the moment, we again restrict ourselves to the real case. A real (Euclidean) line is a 1-dimensional object that could be isomor- phically associated to the real numbers R. Each point on the line uniquely corresponds to exactly one real number. Increasing this real number further and further we will move the corresponding point further and further out. Decreasing the parameter will move the point further and further out in the opposite direction. In a projective setup we will compactify this situation by adding one point at infinity on this line. If we increase or decrease the real parameter we will in the limit process reach this unique infinite point. Algebraically we can model this process again by introducing homogeneous coordinates. A finite point with parameter λ on the line will be represented by a two-dimensional vector (λ, 1) (or any non-zero multiple of this vector). The unique infinite point corresponds to the vector (1, 0) (or any non-zero multiple R2−{(0,0)} of this vector). Formally we can describe this space as R−{0} . The picture above gives an impression of the situation. The original line is now embedded on the y = 1 line. Each two dimensional vector represents a one dimensional subspace of R2. For finite points the intersection of this subspace with the line gives the corresponding point on the line. The infinite point is represented by any vector on the x-axis. (The reader should notice that this setup is com- pletely analogous to the setup for the real projective plane that we described in Section 3.1). Topologically a projective line has the shape of a circle — a one dimensional road on which we come back to the start-point if we travel long enough in one direction. We call this space RP1. In the projective plane RP2 we can consider any line as an isomorphic copy of RP1. 76 4 Lines and cross ratios In analogy to the real projective plane we define a projective transfor- mation by the multiplication of the homogeneous coordinate vector with a matrix. This time it must be a 2 2 matrix × x ab x y '→ cd · y " # " # " # If we consider our points (λ, 1) represented by a real parameter λ, then matrix multiplication induces the following action on the parameter λ: a λ + b λ · . '→ c λ + d · A point gets mapped to infinity if the denominator of the above ratio vanishes. An argument completely analogous to the proof of Theorem 3.4 shows: Theorem 4.1. Let [a], [b], [c] RP1 be three points of which no two are co- incident and let [a#], [b#], [c#] ∈RP1 be another three points of which no two are coincident, then there exists∈ a 2 2 matrix M such that [M a] = [a#], [M b] = [b#] and [M c] = [c#]. × · · · In other words the image of three points uniquely determines a projective transformation. The projective transformations arise in a natural way if we represent points on the line with respect to two different sets of reference vectors, as the following lemma shows. Lemma 4.2. Let # be the line spanned by two points [p] and [q] in RP2. Let [a] and [b] be two other distinct points on #. Consider the vector λp + µq (that represents a point on #). This vector can also be written as αa + βb for certain α,β. The parameters (α,β) can be expressed in (λ, µ) by a linear transformation, that only depends on a, b, p and q. Proof. Theorem 4.1 ensures that the point represented by λp + µq can also be expressed in the form αa + βb. Since p is in the span of a and b it can be written as p = αpa + βpb. Similarly, q can be written as q = αqa + βqb.
Recommended publications
  • 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.
    [Show full text]
  • Algebraic Geometry Michael Stoll
    Introductory Geometry Course No. 100 351 Fall 2005 Second Part: Algebraic Geometry Michael Stoll Contents 1. What Is Algebraic Geometry? 2 2. Affine Spaces and Algebraic Sets 3 3. Projective Spaces and Algebraic Sets 6 4. Projective Closure and Affine Patches 9 5. Morphisms and Rational Maps 11 6. Curves — Local Properties 14 7. B´ezout’sTheorem 18 2 1. What Is Algebraic Geometry? Linear Algebra can be seen (in parts at least) as the study of systems of linear equations. In geometric terms, this can be interpreted as the study of linear (or affine) subspaces of Cn (say). Algebraic Geometry generalizes this in a natural way be looking at systems of polynomial equations. Their geometric realizations (their solution sets in Cn, say) are called algebraic varieties. Many questions one can study in various parts of mathematics lead in a natural way to (systems of) polynomial equations, to which the methods of Algebraic Geometry can be applied. Algebraic Geometry provides a translation between algebra (solutions of equations) and geometry (points on algebraic varieties). The methods are mostly algebraic, but the geometry provides the intuition. Compared to Differential Geometry, in Algebraic Geometry we consider a rather restricted class of “manifolds” — those given by polynomial equations (we can allow “singularities”, however). For example, y = cos x defines a perfectly nice differentiable curve in the plane, but not an algebraic curve. In return, we can get stronger results, for example a criterion for the existence of solutions (in the complex numbers), or statements on the number of solutions (for example when intersecting two curves), or classification results.
    [Show full text]
  • Arxiv:1910.11630V1 [Math.AG] 25 Oct 2019 3 Geometric Invariant Theory 10 3.1 Quotients and the Notion of Stability
    Geometric Invariant Theory, holomorphic vector bundles and the Harder–Narasimhan filtration Alfonso Zamora Departamento de Matem´aticaAplicada y Estad´ıstica Universidad CEU San Pablo Juli´anRomea 23, 28003 Madrid, Spain e-mail: [email protected] Ronald A. Z´u˜niga-Rojas Centro de Investigaciones Matem´aticasy Metamatem´aticas CIMM Escuela de Matem´atica,Universidad de Costa Rica UCR San Jos´e11501, Costa Rica e-mail: [email protected] Abstract. This survey intends to present the basic notions of Geometric Invariant Theory (GIT) through its paradigmatic application in the construction of the moduli space of holomorphic vector bundles. Special attention is paid to the notion of stability from different points of view and to the concept of maximal unstability, represented by the Harder-Narasimhan filtration and, from which, correspondences with the GIT picture and results derived from stratifications on the moduli space are discussed. Keywords: Geometric Invariant Theory, Harder-Narasimhan filtration, moduli spaces, vector bundles, Higgs bundles, GIT stability, symplectic stability, stratifications. MSC class: 14D07, 14D20, 14H10, 14H60, 53D30 Contents 1 Introduction 2 2 Preliminaries 4 2.1 Lie groups . .4 2.2 Lie algebras . .6 2.3 Algebraic varieties . .7 2.4 Vector bundles . .8 arXiv:1910.11630v1 [math.AG] 25 Oct 2019 3 Geometric Invariant Theory 10 3.1 Quotients and the notion of stability . 10 3.2 Hilbert-Mumford criterion . 14 3.3 Symplectic stability . 18 3.4 Examples . 21 3.5 Maximal unstability . 24 2 git, hvb & hnf 4 Moduli Space of vector bundles 28 4.1 GIT construction of the moduli space . 28 4.2 Harder-Narasimhan filtration .
    [Show full text]
  • 2D and 3D Transformations, Homogeneous Coordinates Lecture 03
    2D and 3D Transformations, Homogeneous Coordinates Lecture 03 Patrick Karlsson [email protected] Centre for Image Analysis Uppsala University Computer Graphics November 6 2006 Patrick Karlsson (Uppsala University) Transformations and Homogeneous Coords. Computer Graphics 1 / 23 Reading Instructions Chapters 4.1–4.9. Edward Angel. “Interactive Computer Graphics: A Top-down Approach with OpenGL”, Fourth Edition, Addison-Wesley, 2004. Patrick Karlsson (Uppsala University) Transformations and Homogeneous Coords. Computer Graphics 2 / 23 Todays lecture ... in the pipeline Patrick Karlsson (Uppsala University) Transformations and Homogeneous Coords. Computer Graphics 3 / 23 Scalars, points, and vectors Scalars α, β Real (or complex) numbers. Points P, Q Locations in space (but no size or shape). Vectors u, v Directions in space (magnitude but no position). Patrick Karlsson (Uppsala University) Transformations and Homogeneous Coords. Computer Graphics 4 / 23 Mathematical spaces Scalar field A set of scalars obeying certain properties. New scalars can be formed through addition and multiplication. (Linear) Vector space Made up of scalars and vectors. New vectors can be created through scalar-vector multiplication, and vector-vector addition. Affine space An extended vector space that include points. This gives us additional operators, such as vector-point addition, and point-point subtraction. Patrick Karlsson (Uppsala University) Transformations and Homogeneous Coords. Computer Graphics 5 / 23 Data types Polygon based objects Objects are described using polygons. A polygon is defined by its vertices (i.e., points). Transformations manipulate the vertices, thus manipulates the objects. Some examples in 2D Scalar α 1 float. Point P(x, y) 2 floats. Vector v(x, y) 2 floats. Matrix M 4 floats.
    [Show full text]
  • Vector Bundles and Projective Modules
    VECTOR BUNDLES AND PROJECTIVE MODULES BY RICHARD G. SWAN(i) Serre [9, §50] has shown that there is a one-to-one correspondence between algebraic vector bundles over an affine variety and finitely generated projective mo- dules over its coordinate ring. For some time, it has been assumed that a similar correspondence exists between topological vector bundles over a compact Haus- dorff space X and finitely generated projective modules over the ring of con- tinuous real-valued functions on X. A number of examples of projective modules have been given using this correspondence. However, no rigorous treatment of the correspondence seems to have been given. I will give such a treatment here and then give some of the examples which may be constructed in this way. 1. Preliminaries. Let K denote either the real numbers, complex numbers or quaternions. A X-vector bundle £ over a topological space X consists of a space F(£) (the total space), a continuous map p : E(Ç) -+ X (the projection) which is onto, and, on each fiber Fx(¡z)= p-1(x), the structure of a finite di- mensional vector space over K. These objects are required to satisfy the follow- ing condition: for each xeX, there is a neighborhood U of x, an integer n, and a homeomorphism <p:p-1(U)-> U x K" such that on each fiber <b is a X-homo- morphism. The fibers u x Kn of U x K" are X-vector spaces in the obvious way. Note that I do not require n to be a constant.
    [Show full text]
  • 2-D Drawing Geometry Homogeneous Coordinates
    2-D Drawing Geometry Homogeneous Coordinates The rotation of a point, straight line or an entire image on the screen, about a point other than origin, is achieved by first moving the image until the point of rotation occupies the origin, then performing rotation, then finally moving the image to its original position. The moving of an image from one place to another in a straight line is called a translation. A translation may be done by adding or subtracting to each point, the amount, by which picture is required to be shifted. Translation of point by the change of coordinate cannot be combined with other transformation by using simple matrix application. Such a combination is essential if we wish to rotate an image about a point other than origin by translation, rotation again translation. To combine these three transformations into a single transformation, homogeneous coordinates are used. In homogeneous coordinate system, two-dimensional coordinate positions (x, y) are represented by triple- coordinates. Homogeneous coordinates are generally used in design and construction applications. Here we perform translations, rotations, scaling to fit the picture into proper position 2D Transformation in Computer Graphics- In Computer graphics, Transformation is a process of modifying and re- positioning the existing graphics. • 2D Transformations take place in a two dimensional plane. • Transformations are helpful in changing the position, size, orientation, shape etc of the object. Transformation Techniques- In computer graphics, various transformation techniques are- 1. Translation 2. Rotation 3. Scaling 4. Reflection 2D Translation in Computer Graphics- In Computer graphics, 2D Translation is a process of moving an object from one position to another in a two dimensional plane.
    [Show full text]
  • Classical Algebraic Geometry
    CLASSICAL ALGEBRAIC GEOMETRY Daniel Plaumann Universität Konstanz Summer A brief inaccurate history of algebraic geometry - Projective geometry. Emergence of ’analytic’geometry with cartesian coordinates, as opposed to ’synthetic’(axiomatic) geometry in the style of Euclid. (Celebrities: Plücker, Hesse, Cayley) - Complex analytic geometry. Powerful new tools for the study of geo- metric problems over C.(Celebrities: Abel, Jacobi, Riemann) - Classical school. Perfected the use of existing tools without any ’dog- matic’approach. (Celebrities: Castelnuovo, Segre, Severi, M. Noether) - Algebraization. Development of modern algebraic foundations (’com- mutative ring theory’) for algebraic geometry. (Celebrities: Hilbert, E. Noether, Zariski) from Modern algebraic geometry. All-encompassing abstract frameworks (schemes, stacks), greatly widening the scope of algebraic geometry. (Celebrities: Weil, Serre, Grothendieck, Deligne, Mumford) from Computational algebraic geometry Symbolic computation and dis- crete methods, many new applications. (Celebrities: Buchberger) Literature Primary source [Ha] J. Harris, Algebraic Geometry: A first course. Springer GTM () Classical algebraic geometry [BCGB] M. C. Beltrametti, E. Carletti, D. Gallarati, G. Monti Bragadin. Lectures on Curves, Sur- faces and Projective Varieties. A classical view of algebraic geometry. EMS Textbooks (translated from Italian) () [Do] I. Dolgachev. Classical Algebraic Geometry. A modern view. Cambridge UP () Algorithmic algebraic geometry [CLO] D. Cox, J. Little, D.
    [Show full text]
  • Projective Geometry Lecture Notes
    Projective Geometry Lecture Notes Thomas Baird March 26, 2014 Contents 1 Introduction 2 2 Vector Spaces and Projective Spaces 4 2.1 Vector spaces and their duals . 4 2.1.1 Fields . 4 2.1.2 Vector spaces and subspaces . 5 2.1.3 Matrices . 7 2.1.4 Dual vector spaces . 7 2.2 Projective spaces and homogeneous coordinates . 8 2.2.1 Visualizing projective space . 8 2.2.2 Homogeneous coordinates . 13 2.3 Linear subspaces . 13 2.3.1 Two points determine a line . 14 2.3.2 Two planar lines intersect at a point . 14 2.4 Projective transformations and the Erlangen Program . 15 2.4.1 Erlangen Program . 16 2.4.2 Projective versus linear . 17 2.4.3 Examples of projective transformations . 18 2.4.4 Direct sums . 19 2.4.5 General position . 20 2.4.6 The Cross-Ratio . 22 2.5 Classical Theorems . 23 2.5.1 Desargues' Theorem . 23 2.5.2 Pappus' Theorem . 24 2.6 Duality . 26 3 Quadrics and Conics 28 3.1 Affine algebraic sets . 28 3.2 Projective algebraic sets . 30 3.3 Bilinear and quadratic forms . 31 3.3.1 Quadratic forms . 33 3.3.2 Change of basis . 33 1 3.3.3 Digression on the Hessian . 36 3.4 Quadrics and conics . 37 3.5 Parametrization of the conic . 40 3.5.1 Rational parametrization of the circle . 42 3.6 Polars . 44 3.7 Linear subspaces of quadrics and ruled surfaces . 46 3.8 Pencils of quadrics and degeneration . 47 4 Exterior Algebras 52 4.1 Multilinear algebra .
    [Show full text]
  • Chapter 12 the Cross Ratio
    Chapter 12 The cross ratio Math 4520, Fall 2017 We have studied the collineations of a projective plane, the automorphisms of the underlying field, the linear functions of Affine geometry, etc. We have been led to these ideas by various problems at hand, but let us step back and take a look at one important point of view of the big picture. 12.1 Klein's Erlanger program In 1872, Felix Klein, one of the leading mathematicians and geometers of his day, in the city of Erlanger, took the following point of view as to what the role of geometry was in mathematics. This is from his \Recent trends in geometric research." Let there be given a manifold and in it a group of transforma- tions; it is our task to investigate those properties of a figure belonging to the manifold that are not changed by the transfor- mation of the group. So our purpose is clear. Choose the group of transformations that you are interested in, and then hunt for the \invariants" that are relevant. This search for invariants has proved very fruitful and useful since the time of Klein for many areas of mathematics, not just classical geometry. In some case the invariants have turned out to be simple polynomials or rational functions, such as the case with the cross ratio. In other cases the invariants were groups themselves, such as homology groups in the case of algebraic topology. 12.2 The projective line In Chapter 11 we saw that the collineations of a projective plane come in two \species," projectivities and field automorphisms.
    [Show full text]
  • Homogeneous Representations of Points, Lines and Planes
    Chapter 5 Homogeneous Representations of Points, Lines and Planes 5.1 Homogeneous Vectors and Matrices ................................. 195 5.2 Homogeneous Representations of Points and Lines in 2D ............... 205 n 5.3 Homogeneous Representations in IP ................................ 209 5.4 Homogeneous Representations of 3D Lines ........................... 216 5.5 On Plücker Coordinates for Points, Lines and Planes .................. 221 5.6 The Principle of Duality ........................................... 229 5.7 Conics and Quadrics .............................................. 236 5.8 Normalizations of Homogeneous Vectors ............................. 241 5.9 Canonical Elements of Coordinate Systems ........................... 242 5.10 Exercises ........................................................ 245 This chapter motivates and introduces homogeneous coordinates for representing geo- metric entities. Their name is derived from the homogeneity of the equations they induce. Homogeneous coordinates represent geometric elements in a projective space, as inhomoge- neous coordinates represent geometric entities in Euclidean space. Throughout this book, we will use Cartesian coordinates: inhomogeneous in Euclidean spaces and homogeneous in projective spaces. A short course in the plane demonstrates the usefulness of homogeneous coordinates for constructions, transformations, estimation, and variance propagation. A characteristic feature of projective geometry is the symmetry of relationships between points and lines, called
    [Show full text]
  • Single View Geometry Camera Model & Orientation + Position Estimation
    Single View Geometry Camera model & Orientation + Position estimation What am I? Vanishing point Mapping from 3D to 2D Point & Line Goal: Homogeneous coordinates Point – represent coordinates in 2 dimensions with a 3-vector &x# &x# homogeneous coords $y! $ ! ''''''→$ ! %y" %$1"! The projective plane • Why do we need homogeneous coordinates? – represent points at infinity, homographies, perspective projection, multi-view relationships • What is the geometric intuition? – a point in the image is a ray in projective space y (sx,sy,s) (x,y,1) (0,0,0) z x image plane • Each point (x,y) on the plane is represented by a ray (sx,sy,s) – all points on the ray are equivalent: (x, y, 1) ≅ (sx, sy, s) Projective Lines Projective lines • What does a line in the image correspond to in projective space? • A line is a plane of rays through origin – all rays (x,y,z) satisfying: ax + by + cz = 0 ⎡x⎤ in vector notation : 0 a b c ⎢y⎥ = [ ]⎢ ⎥ ⎣⎢z⎦⎥ l p • A line is also represented as a homogeneous 3-vector l Line Representation • a line is • is the distance from the origin to the line • is the norm direction of the line • It can also be written as Example of Line Example of Line (2) 0.42*pi Homogeneous representation Line in Is represented by a point in : But correspondence of line to point is not unique We define set of equivalence class of vectors in R^3 - (0,0,0) As projective space Projective lines from two points Line passing through two points Two points: x Define a line l is the line passing two points Proof: Line passing through two points • More
    [Show full text]
  • 6. the Riemann Sphere It Is Sometimes Convenient to Add a Point at Infinity ∞ to the Usual Complex Plane to Get the Extended Complex Plane
    6. The Riemann sphere It is sometimes convenient to add a point at infinity 1 to the usual complex plane to get the extended complex plane. Definition 6.1. The extended complex plane, denoted P1, is simply the union of C and the point at infinity. It is somewhat curious that when we add points at infinity to the reals we add two points ±∞ but only only one point for the complex numbers. It is rare in geometry that things get easier as you increase the dimension. One very good way to understand the extended complex plane is to realise that P1 is naturally in bijection with the unit sphere: Definition 6.2. The Riemann sphere is the unit sphere in R3: 2 3 2 2 2 S = f (x; y; z) 2 R j x + y + z = 1 g: To make a correspondence with the sphere and the plane is simply to make a map (a real map not a function). We define a function 2 3 F : S − f(0; 0; 1)g −! C ⊂ R as follows. Pick a point q = (x; y; z) 2 S2, a point of the unit sphere, other than the north pole p = N = (0; 0; 1). Connect the point p to the point q by a line. This line will meet the plane z = 0, 2 f (x; y; 0) j (x; y) 2 R g in a unique point r. We then identify r with a point F (q) = x + iy 2 C in the usual way. F is called stereographic projection.
    [Show full text]