1 Affine and Projective Coordinate Notation 2 Transformations
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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. -
Efficient Learning of Simplices
Efficient Learning of Simplices Joseph Anderson Navin Goyal Computer Science and Engineering Microsoft Research India Ohio State University [email protected] [email protected] Luis Rademacher Computer Science and Engineering Ohio State University [email protected] Abstract We show an efficient algorithm for the following problem: Given uniformly random points from an arbitrary n-dimensional simplex, estimate the simplex. The size of the sample and the number of arithmetic operations of our algorithm are polynomial in n. This answers a question of Frieze, Jerrum and Kannan [FJK96]. Our result can also be interpreted as efficiently learning the intersection of n + 1 half-spaces in Rn in the model where the intersection is bounded and we are given polynomially many uniform samples from it. Our proof uses the local search technique from Independent Component Analysis (ICA), also used by [FJK96]. Unlike these previous algorithms, which were based on analyzing the fourth moment, ours is based on the third moment. We also show a direct connection between the problem of learning a simplex and ICA: a simple randomized reduction to ICA from the problem of learning a simplex. The connection is based on a known representation of the uniform measure on a sim- plex. Similar representations lead to a reduction from the problem of learning an affine arXiv:1211.2227v3 [cs.LG] 6 Jun 2013 transformation of an n-dimensional ℓp ball to ICA. 1 Introduction We are given uniformly random samples from an unknown convex body in Rn, how many samples are needed to approximately reconstruct the body? It seems intuitively clear, at least for n = 2, 3, that if we are given sufficiently many such samples then we can reconstruct (or learn) the body with very little error. -
Paraperspective ≡ Affine
International Journal of Computer Vision, 19(2): 169–180, 1996. Paraperspective ´ Affine Ronen Basri Dept. of Applied Math The Weizmann Institute of Science Rehovot 76100, Israel [email protected] Abstract It is shown that the set of all paraperspective images with arbitrary reference point and the set of all affine images of a 3-D object are identical. Consequently, all uncali- brated paraperspective images of an object can be constructed from a 3-D model of the object by applying an affine transformation to the model, and every affine image of the object represents some uncalibrated paraperspective image of the object. It follows that the paraperspective images of an object can be expressed as linear combinations of any two non-degenerate images of the object. When the image position of the reference point is given the parameters of the affine transformation (and, likewise, the coefficients of the linear combinations) satisfy two quadratic constraints. Conversely, when the values of parameters are given the image position of the reference point is determined by solving a bi-quadratic equation. Key words: affine transformations, calibration, linear combinations, paraperspective projec- tion, 3-D object recognition. 1 Introduction It is shown below that given an object O ½ R3, the set of all images of O obtained by applying a rigid transformation followed by a paraperspective projection with arbitrary reference point and the set of all images of O obtained by applying a 3-D affine transformation followed by an orthographic projection are identical. Consequently, all paraperspective images of an object can be constructed from a 3-D model of the object by applying an affine transformation to the model, and every affine image of the object represents some paraperspective image of the object. -
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. -
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. -
Lecture 16: Planar Homographies Robert Collins CSE486, Penn State Motivation: Points on Planar Surface
Robert Collins CSE486, Penn State Lecture 16: Planar Homographies Robert Collins CSE486, Penn State Motivation: Points on Planar Surface y x Robert Collins CSE486, Penn State Review : Forward Projection World Camera Film Pixel Coords Coords Coords Coords U X x u M M V ext Y proj y Maff v W Z U X Mint u V Y v W Z U M u V m11 m12 m13 m14 v W m21 m22 m23 m24 m31 m31 m33 m34 Robert Collins CSE486, PennWorld State to Camera Transformation PC PW W Y X U R Z C V Rotate to Translate by - C align axes (align origins) PC = R ( PW - C ) = R PW + T Robert Collins CSE486, Penn State Perspective Matrix Equation X (Camera Coordinates) x = f Z Y X y = f x' f 0 0 0 Z Y y' = 0 f 0 0 Z z' 0 0 1 0 1 p = M int ⋅ PC Robert Collins CSE486, Penn State Film to Pixel Coords 2D affine transformation from film coords (x,y) to pixel coordinates (u,v): X u’ a11 a12xa'13 f 0 0 0 Y v’ a21 a22 ya'23 = 0 f 0 0 w’ Z 0 0z1' 0 0 1 0 1 Maff Mproj u = Mint PC = Maff Mproj PC Robert Collins CSE486, Penn StateProjection of Points on Planar Surface Perspective projection y Film coordinates x Point on plane Rotation + Translation Robert Collins CSE486, Penn State Projection of Planar Points Robert Collins CSE486, Penn StateProjection of Planar Points (cont) Homography H (planar projective transformation) Robert Collins CSE486, Penn StateProjection of Planar Points (cont) Homography H (planar projective transformation) Punchline: For planar surfaces, 3D to 2D perspective projection reduces to a 2D to 2D transformation. -
Euclidean Versus Projective Geometry
Projective Geometry Projective Geometry Euclidean versus Projective Geometry n Euclidean geometry describes shapes “as they are” – Properties of objects that are unchanged by rigid motions » Lengths » Angles » Parallelism n Projective geometry describes objects “as they appear” – Lengths, angles, parallelism become “distorted” when we look at objects – Mathematical model for how images of the 3D world are formed. Projective Geometry Overview n Tools of algebraic geometry n Informal description of projective geometry in a plane n Descriptions of lines and points n Points at infinity and line at infinity n Projective transformations, projectivity matrix n Example of application n Special projectivities: affine transforms, similarities, Euclidean transforms n Cross-ratio invariance for points, lines, planes Projective Geometry Tools of Algebraic Geometry 1 n Plane passing through origin and perpendicular to vector n = (a,b,c) is locus of points x = ( x 1 , x 2 , x 3 ) such that n · x = 0 => a x1 + b x2 + c x3 = 0 n Plane through origin is completely defined by (a,b,c) x3 x = (x1, x2 , x3 ) x2 O x1 n = (a,b,c) Projective Geometry Tools of Algebraic Geometry 2 n A vector parallel to intersection of 2 planes ( a , b , c ) and (a',b',c') is obtained by cross-product (a'',b'',c'') = (a,b,c)´(a',b',c') (a'',b'',c'') O (a,b,c) (a',b',c') Projective Geometry Tools of Algebraic Geometry 3 n Plane passing through two points x and x’ is defined by (a,b,c) = x´ x' x = (x1, x2 , x3 ) x'= (x1 ', x2 ', x3 ') O (a,b,c) Projective Geometry Projective Geometry -
Affine Transformations and Rotations
CMSC 425 Dave Mount & Roger Eastman CMSC 425: Lecture 6 Affine Transformations and Rotations Affine Transformations: So far we have been stepping through the basic elements of geometric programming. We have discussed points, vectors, and their operations, and coordinate frames and how to change the representation of points and vectors from one frame to another. Our next topic involves how to map points from one place to another. Suppose you want to draw an animation of a spinning ball. How would you define the function that maps each point on the ball to its position rotated through some given angle? We will consider a limited, but interesting class of transformations, called affine transfor- mations. These include (among others) the following transformations of space: translations, rotations, uniform and nonuniform scalings (stretching the axes by some constant scale fac- tor), reflections (flipping objects about a line) and shearings (which deform squares into parallelograms). They are illustrated in Fig. 1. rotation translation uniform nonuniform reflection shearing scaling scaling Fig. 1: Examples of affine transformations. These transformations all have a number of things in common. For example, they all map lines to lines. Note that some (translation, rotation, reflection) preserve the lengths of line segments and the angles between segments. These are called rigid transformations. Others (like uniform scaling) preserve angles but not lengths. Still others (like nonuniform scaling and shearing) do not preserve angles or lengths. Formal Definition: Formally, an affine transformation is a mapping from one affine space to another (which may be, and in fact usually is, the same space) that preserves affine combi- nations. -
Determinants and Transformations 1. (A.) 2
Lecture 3 answers to exercises: Determinants and transformations 1. (a:) 2 · 4 − 3 · (−1) = 11 (b:) − 5 · 2 − 1 · 0 = −10 (c:) Of this matrix we cannot compute the determinant because it is not square. (d:) −5·7·1+1·(−2)·3+(−1)·1·0−(−1)·7·3−1·1·1−(−5)·(−2)·0 = −35−6+21−1 = −21 2. 2 5 −2 4 2 −2 6 4 −1 3 6 −1 1 2 3 −83 3 −1 1 3 42 1 x = = = 20 y = = = −10 4 5 −2 −4 4 4 5 −2 −4 2 3 4 −1 3 4 −1 −1 2 3 −1 2 3 4 5 2 3 4 6 −1 2 1 −57 1 z = = = 14 4 5 −2 −4 4 3 4 −1 −1 2 3 3. Yes. Just think of a matrix and apply it to the zero vector. The outcome of all components are zeros. 4. We find the desired matrix by multiplying the two matrices for the two parts. The 0 −1 matrix for reflection in x + y = 0 is , and the matrix for rotation of 45◦ about −1 0 p p 1 2 − 1 2 2 p 2p the origin is 1 1 . So we compute (note the order!): 2 2 2 2 p p p p 1 2 − 1 2 0 −1 1 2 − 1 2 2 p 2p 2 p 2 p 1 1 = 1 1 2 2 2 2 −1 0 − 2 2 − 2 2 5. A must be the zero matrix. This is true because the vectors 2 3 2, 1 0 2, and 0 2 4 are linearly independent. -
Arxiv:1807.03503V1 [Cs.CV] 10 Jul 2018 Dences from the Affine Features
Recovering affine features from orientation- and scale-invariant ones Daniel Barath12 1 Centre for Machine Perception, Czech Technical University, Prague, Czech Republic 2 Machine Perception Research Laboratory, MTA SZTAKI, Budapest, Hungary Abstract. An approach is proposed for recovering affine correspondences (ACs) from orientation- and scale-invariant, e.g. SIFT, features. The method calculates the affine parameters consistent with a pre-estimated epipolar geometry from the point coordinates and the scales and rotations which the feature detector obtains. The closed-form solution is given as the roots of a quadratic polynomial equation, thus having two possible real candidates and fast procedure, i.e. < 1 millisecond. It is shown, as a possible application, that using the proposed algorithm allows us to estimate a homography for every single correspondence independently. It is validated both in our synthetic environment and on publicly available real world datasets, that the proposed technique leads to accurate ACs. Also, the estimated homographies have similar accuracy to what the state-of-the-art methods obtain, but due to requiring only a single correspondence, the robust estimation, e.g. by locally optimized RANSAC, is an order of magnitude faster. 1 Introduction This paper addresses the problem of recovering fully affine-covariant features [1] from orientation- and scale-invariant ones obtained by, for instance, SIFT [2] or SURF [3] detectors. This objective is achieved by considering the epipolar geometry to be known between two images and exploiting the geometric constraints which it implies.1 The proposed algorithm requires the epipolar geometry, i.e. characterized by either a fun- damental F or an essential E matrix, and an orientation and scale-invariant feature as input and returns the affine correspondence consistent with the epipolar geometry. -
Dirichlet Problem at Infinity for P-Harmonic Functions on Negatively
CORE Metadata, citation and similar papers at core.ac.uk Provided by Helsingin yliopiston digitaalinen arkisto Dirichlet problem at infinity for p-harmonic functions on negatively curved spaces Aleksi Vähäkangas Department of Mathematics and Statistics Faculty of Science University of Helsinki 2008 Academic dissertation To be presented for public examination with the permission of the Faculty of Science of the University of Helsinki in Auditorium XIII of the University Main Building on 13 December 2008 at 10 a.m. ISBN 978-952-92-4874-2 (pbk) Helsinki University Print ISBN 978-952-10-5170-8 (PDF) http://ethesis.helsinki.fi/ Helsinki 2008 Acknowledgements I am in greatly indebted to my advisor Ilkka Holopainen for his continual support and encouragement. His input into all aspects of writing this thesis has been invaluable to me. I would also like to thank the staff and students at Kumpula for many interesting discussions in mathematics. The financial support of the Academy of Finland and the Graduate School in Mathe- matical Analysis and Its Applications is gratefully acknowledged. Finally, I would like to thank my brother Antti for advice and help with this work and my parents Simo and Anna-Kaisa for support. Helsinki, September 2008 Aleksi Vähäkangas List of included articles This thesis consists of an introductory part and the following three articles: [A] A. Vähäkangas: Dirichlet problem at infinity for A-harmonic functions. Potential Anal. 27 (2007), 27–44. [B] I. Holopainen and A. Vähäkangas: Asymptotic Dirichlet problem on negatively curved spaces. Proceedings of the International Conference on Geometric Function Theory, Special Functions and Applications (ICGFT) (R.