Chapter 12 the Cross Ratio
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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. -
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. -
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 . -
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. -
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. -
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. -
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. -
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 . -
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 -
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 -
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. -
Example Sheet 1
Part III 2015-16: Differential geometry [email protected] Example Sheet 1 3 1 1. (i) Do the charts '1(x) = x and '2(x) = x (x 2 R) belong to the same C differentiable structure on R? (ii) Let Rj, j = 1; 2, be the manifold defined by using the chart 'j on the topo- logical space R. Are R1 and R2 diffeomorphic? 2. Let X be the metric space defined as follows: Let P1;:::;PN be distinct points in the Euclidean plane with its standard metric d, and define a distance function (not a ∗ 2 ∗ metric for N > 1) ρ on R by ρ (P; Q) = minfd(P; Q); mini;j(d(P; Pi) + d(Pj;Q))g: 2 Let X denote the quotient of R obtained by identifying the N points Pi to a single point P¯ on X. Show that ρ∗ induces a metric on X (the London Underground metric). Show that, for N > 1, the space X cannot be given the structure of a topological manifold. 3. (i) Prove that the product of smooth manifolds has the structure of a smooth manifold. (ii) Prove that n-dimensional real projective space RP n = Sn={±1g has the struc- ture of a smooth manifold of dimension n. (iii) Prove that complex projective space CP n := (Cn+1 nf0g)=C∗ has the structure of a smooth manifold of dimension 2n. 4. (i) Prove that the complex projective line CP 1 is diffeomorphic to the sphere S2. (ii) Show that the natural map (C2 n f0g) ! CP 1 induces a smooth map of manifolds S3 ! S2, the Poincar´emap.