Fractal Limit Sets and Schottky Groups
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Globally Optimal Affine and Metric Upgrades in Stratified Autocalibration
Globally Optimal Affine and Metric Upgrades in Stratified Autocalibration Manmohan Chandrakery Sameer Agarwalz David Kriegmany Serge Belongiey [email protected] [email protected] [email protected] [email protected] y University of California, San Diego z University of Washington, Seattle Abstract parameters of the cameras, which is commonly approached by estimating the dual image of the absolute conic (DIAC). We present a practical, stratified autocalibration algo- A variety of linear methods exist towards this end, how- rithm with theoretical guarantees of global optimality. Given ever, they are known to perform poorly in the presence of a projective reconstruction, the first stage of the algorithm noise [10]. Perhaps more significantly, most methods a pos- upgrades it to affine by estimating the position of the plane teriori impose the positive semidefiniteness of the DIAC, at infinity. The plane at infinity is computed by globally which might lead to a spurious calibration. Thus, it is im- minimizing a least squares formulation of the modulus con- portant to impose the positive semidefiniteness of the DIAC straints. In the second stage, the algorithm upgrades this within the optimization, not as a post-processing step. affine reconstruction to a metric one by globally minimizing This paper proposes global minimization algorithms for the infinite homography relation to compute the dual image both stages of stratified autocalibration that furnish theoreti- of the absolute conic (DIAC). The positive semidefiniteness cal certificates of optimality. That is, they return a solution at of the DIAC is explicitly enforced as part of the optimization most away from the global minimum, for arbitrarily small . -
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 -
WHAT IS a CHAOTIC ATTRACTOR? 1. Introduction J. Yorke Coined the Word 'Chaos' As Applied to Deterministic Systems. R. Devane
WHAT IS A CHAOTIC ATTRACTOR? CLARK ROBINSON Abstract. Devaney gave a mathematical definition of the term chaos, which had earlier been introduced by Yorke. We discuss issues involved in choosing the properties that characterize chaos. We also discuss how this term can be combined with the definition of an attractor. 1. Introduction J. Yorke coined the word `chaos' as applied to deterministic systems. R. Devaney gave the first mathematical definition for a map to be chaotic on the whole space where a map is defined. Since that time, there have been several different definitions of chaos which emphasize different aspects of the map. Some of these are more computable and others are more mathematical. See [9] a comparison of many of these definitions. There is probably no one best or correct definition of chaos. In this paper, we discuss what we feel is one of better mathematical definition. (It may not be as computable as some of the other definitions, e.g., the one by Alligood, Sauer, and Yorke.) Our definition is very similar to the one given by Martelli in [8] and [9]. We also combine the concepts of chaos and attractors and discuss chaotic attractors. 2. Basic definitions We start by giving the basic definitions needed to define a chaotic attractor. We give the definitions for a diffeomorphism (or map), but those for a system of differential equations are similar. The orbit of a point x∗ by F is the set O(x∗; F) = f Fi(x∗) : i 2 Z g. An invariant set for a diffeomorphism F is an set A in the domain such that F(A) = A. -
Feature Matching and Heat Flow in Centro-Affine Geometry
Symmetry, Integrability and Geometry: Methods and Applications SIGMA 16 (2020), 093, 22 pages Feature Matching and Heat Flow in Centro-Affine Geometry Peter J. OLVER y, Changzheng QU z and Yun YANG x y School of Mathematics, University of Minnesota, Minneapolis, MN 55455, USA E-mail: [email protected] URL: http://www.math.umn.edu/~olver/ z School of Mathematics and Statistics, Ningbo University, Ningbo 315211, P.R. China E-mail: [email protected] x Department of Mathematics, Northeastern University, Shenyang, 110819, P.R. China E-mail: [email protected] Received April 02, 2020, in final form September 14, 2020; Published online September 29, 2020 https://doi.org/10.3842/SIGMA.2020.093 Abstract. In this paper, we study the differential invariants and the invariant heat flow in centro-affine geometry, proving that the latter is equivalent to the inviscid Burgers' equa- tion. Furthermore, we apply the centro-affine invariants to develop an invariant algorithm to match features of objects appearing in images. We show that the resulting algorithm com- pares favorably with the widely applied scale-invariant feature transform (SIFT), speeded up robust features (SURF), and affine-SIFT (ASIFT) methods. Key words: centro-affine geometry; equivariant moving frames; heat flow; inviscid Burgers' equation; differential invariant; edge matching 2020 Mathematics Subject Classification: 53A15; 53A55 1 Introduction The main objective in this paper is to study differential invariants and invariant curve flows { in particular the heat flow { in centro-affine geometry. In addition, we will present some basic applications to feature matching in camera images of three-dimensional objects, comparing our method with other popular algorithms. -
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. -
• Rotations • Camera Calibration • Homography • Ransac
Agenda • Rotations • Camera calibration • Homography • Ransac Geometric Transformations y 164 Computer Vision: Algorithms andx Applications (September 3, 2010 draft) Transformation Matrix # DoF Preserves Icon translation I t 2 orientation 2 3 h i ⇥ ⇢⇢SS rigid (Euclidean) R t 3 lengths S ⇢ 2 3 S⇢ ⇥ h i ⇢ similarity sR t 4 angles S 2 3 S⇢ h i ⇥ ⇥ ⇥ affine A 6 parallelism ⇥ ⇥ 2 3 h i ⇥ projective H˜ 8 straight lines ` 3 3 ` h i ⇥ Table 3.5 Hierarchy of 2D coordinate transformations. Each transformation also preserves Let’s definethe properties families listed of in thetransformations rows below it, i.e., similarity by the preserves properties not only anglesthat butthey also preserve parallelism and straight lines. The 2 3 matrices are extended with a third [0T 1] row to form ⇥ a full 3 3 matrix for homogeneous coordinate transformations. ⇥ amples of such transformations, which are based on the 2D geometric transformations shown in Figure 2.4. The formulas for these transformations were originally given in Table 2.1 and are reproduced here in Table 3.5 for ease of reference. In general, given a transformation specified by a formula x0 = h(x) and a source image f(x), how do we compute the values of the pixels in the new image g(x), as given in (3.88)? Think about this for a minute before proceeding and see if you can figure it out. If you are like most people, you will come up with an algorithm that looks something like Algorithm 3.1. This process is called forward warping or forward mapping and is shown in Figure 3.46a. -
Attractors of Dynamical Systems in Locally Compact Spaces
Open Math. 2019; 17:465–471 Open Mathematics Research Article Gang Li* and Yuxia Gao Attractors of dynamical systems in locally compact spaces https://doi.org/10.1515/math-2019-0037 Received February 7, 2019; accepted March 4, 2019 Abstract: In this article the properties of attractors of dynamical systems in locally compact metric space are discussed. Existing conditions of attractors and related results are obtained by the near isolating block which we present. Keywords: Flow, attraction neighborhood, near isolating block MSC: 34C35 54H20 1 Introduction The dynamical system theory studies the rules of changes in the state which depends on time. In the investigation of dynamical systems, one of very interesting topics is the study of attractors (see [1 − 4] and the references therein). In [1 − 3], the authors gave some denitions of attractors and also made further investigations about the properties of them. In [5], the limit set of a neighborhood was used in the denition of an attractor, and in [6] Hale and Waterman also emphasized the importance of the limit set of a set in the analysis of the limiting behavior of a dynamical system. In [7 − 9], the authors dened intertwining attractor of dynamical systems in metric space and obtained some existing conditions of intertwining attractor. In [10], the author studied the properties of limit sets of subsets and attractors in a compact metric space. In [11], the author studied a positively bounded dynamical system in the plane, and obtained the conditions of compactness of the set of all bounded solutions. In [12], the uniform attractor was dened as the minimal compact uniformly pullback attracting random set, several existence criteria for uniform attractors were given and the relationship between uniform and co-cycle attractors was carefully studied. -
Control of Chaos: Methods and Applications
Automation and Remote Control, Vol. 64, No. 5, 2003, pp. 673{713. Translated from Avtomatika i Telemekhanika, No. 5, 2003, pp. 3{45. Original Russian Text Copyright c 2003 by Andrievskii, Fradkov. REVIEWS Control of Chaos: Methods and Applications. I. Methods1 B. R. Andrievskii and A. L. Fradkov Institute of Mechanical Engineering Problems, Russian Academy of Sciences, St. Petersburg, Russia Received October 15, 2002 Abstract|The problems and methods of control of chaos, which in the last decade was the subject of intensive studies, were reviewed. The three historically earliest and most actively developing directions of research such as the open-loop control based on periodic system ex- citation, the method of Poincar´e map linearization (OGY method), and the method of time- delayed feedback (Pyragas method) were discussed in detail. The basic results obtained within the framework of the traditional linear, nonlinear, and adaptive control, as well as the neural network systems and fuzzy systems were presented. The open problems concerned mostly with support of the methods were formulated. The second part of the review will be devoted to the most interesting applications. 1. INTRODUCTION The term control of chaos is used mostly to denote the area of studies lying at the interfaces between the control theory and the theory of dynamic systems studying the methods of control of deterministic systems with nonregular, chaotic behavior. In the ancient mythology and philosophy, the word \χαωσ" (chaos) meant the disordered state of unformed matter supposed to have existed before the ordered universe. The combination \control of chaos" assumes a paradoxical sense arousing additional interest in the subject. -
ONE HUNDRED YEARS of COMPLEX DYNAMICS the Subject of Complex Dynamics, That Is, the Behaviour of Orbits of Holomorphic Functions
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by University of Liverpool Repository ONE HUNDRED YEARS OF COMPLEX DYNAMICS MARY REES The subject of Complex Dynamics, that is, the behaviour of orbits of holomorphic functions, emerged in the papers produced, independently, by Fatou and Julia, almost 100 years ago. Although the subject of Dynami- cal Systems did not then have a name, the dynamical properties found for holomorphic systems, even in these early researches, were so striking, so unusually comprehensive, and yet so varied, that these systems still attract widespread fascination, 100 years later. The first distinctive feature of iter- ation of a single holomorphic map f is the partition of either the complex plane or the Riemann sphere into two sets which are totally invariant under f: the Julia set | closed, nonempty, perfect, with dynamics which might loosely be called chaotic | and its complement | open, possibly empty, but, if non-empty, then with dynamics which were completely classified by the two pioneering researchers, modulo a few simply stated open questions. Before the subject re-emerged into prominence in the 1980's, the Julia set was alternately called the Fatou set, but Paul Blanchard introduced the idea of calling its complement the Fatou set, and this was immediately universally accepted. Probably the main reason for the remarkable rise in interest in complex dynamics, about thirty-five years ago, was the parallel with the subject of Kleinian groups, and hence with the whole subject of hyperbolic geome- try. A Kleinian group acting on the Riemann sphere is a dynamical system, with the sphere splitting into two disjoint invariant subsets, with the limit set and its complement, the domain of discontinuity, having exactly similar properties to the Julia and Fatou sets. -
Automorphisms of Classical Geometries in the Sense of Klein
Automorphisms of classical geometries in the sense of Klein Navarro, A.∗ Navarro, J.† November 11, 2018 Abstract In this note, we compute the group of automorphisms of Projective, Affine and Euclidean Geometries in the sense of Klein. As an application, we give a simple construction of the outer automorphism of S6. 1 Introduction Let Pn be the set of 1-dimensional subspaces of a n + 1-dimensional vector space E over a (commutative) field k. This standard definition does not capture the ”struc- ture” of the projective space although it does point out its automorphisms: they are projectivizations of semilinear automorphisms of E, also known as Staudt projectivi- ties. A different approach (v. gr. [1]), defines the projective space as a lattice (the lattice of all linear subvarieties) satisfying certain axioms. Then, the so named Fun- damental Theorem of Projective Geometry ([1], Thm 2.26) states that, when n> 1, collineations of Pn (i.e., bijections preserving alignment, which are the automorphisms of this lattice structure) are precisely Staudt projectivities. In this note we are concerned with geometries in the sense of Klein: a geometry is a pair (X, G) where X is a set and G is a subgroup of the group Biy(X) of all bijections of X. In Klein’s view, Projective Geometry is the pair (Pn, PGln), where PGln is the group of projectivizations of k-linear automorphisms of the vector space E (see Example 2.2). The main result of this note is a computation, analogous to the arXiv:0901.3928v2 [math.GR] 4 Jul 2013 aforementioned theorem for collineations, but in the realm of Klein geometries: Theorem 3.4 The group of automorphisms of the Projective Geometry (Pn, PGln) is the group of Staudt projectivities, for any n ≥ 1. -
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Acta Math. Univ. Comenianae 911 Vol. LXXXVIII, 3 (2019), pp. 911{916 COSET GEOMETRIES WITH TRIALITIES AND THEIR REDUCED INCIDENCE GRAPHS D. LEEMANS and K. STOKES Abstract. In this article we explore combinatorial trialities of incidence geome- tries. We give a construction that uses coset geometries to construct examples of incidence geometries with trialities and prescribed automorphism group. We define the reduced incidence graph of the geometry to be the oriented graph obtained as the quotient of the geometry under the triality. Our chosen examples exhibit interesting features relating the automorphism group of the geometry and the automorphism group of the reduced incidence graphs. 1. Introduction The projective space of dimension n over a field F is an incidence geometry PG(n; F ). It has n types of elements; the projective subspaces: the points, the lines, the planes, and so on. The elements are related by incidence, defined by inclusion. A collinearity of PG(n; F ) is an automorphism preserving incidence and type. According to the Fundamental theorem of projective geometry, every collinearity is composed by a homography and a field automorphism. A duality of PG(n; F ) is an automorphism preserving incidence that maps elements of type k to elements of type n − k − 1. Dualities are also called correlations or reciprocities. Geometric dualities, that is, dualities in projective spaces, correspond to sesquilinear forms. Therefore the classification of the sesquilinear forms also give a classification of the geometric dualities. A polarity is a duality δ that is an involution, that is, δ2 = Id. A duality can always be expressed as a composition of a polarity and a collinearity.