A Resolution of the Sylvester-Gallai Problem of J.-P. Serre LM Kelly
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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 -
COMBINATORICS, Volume
http://dx.doi.org/10.1090/pspum/019 PROCEEDINGS OF SYMPOSIA IN PURE MATHEMATICS Volume XIX COMBINATORICS AMERICAN MATHEMATICAL SOCIETY Providence, Rhode Island 1971 Proceedings of the Symposium in Pure Mathematics of the American Mathematical Society Held at the University of California Los Angeles, California March 21-22, 1968 Prepared by the American Mathematical Society under National Science Foundation Grant GP-8436 Edited by Theodore S. Motzkin AMS 1970 Subject Classifications Primary 05Axx, 05Bxx, 05Cxx, 10-XX, 15-XX, 50-XX Secondary 04A20, 05A05, 05A17, 05A20, 05B05, 05B15, 05B20, 05B25, 05B30, 05C15, 05C99, 06A05, 10A45, 10C05, 14-XX, 20Bxx, 20Fxx, 50A20, 55C05, 55J05, 94A20 International Standard Book Number 0-8218-1419-2 Library of Congress Catalog Number 74-153879 Copyright © 1971 by the American Mathematical Society Printed in the United States of America All rights reserved except those granted to the United States Government May not be produced in any form without permission of the publishers Leo Moser (1921-1970) was active and productive in various aspects of combin• atorics and of its applications to number theory. He was in close contact with those with whom he had common interests: we will remember his sparkling wit, the universality of his anecdotes, and his stimulating presence. This volume, much of whose content he had enjoyed and appreciated, and which contains the re• construction of a contribution by him, is dedicated to his memory. CONTENTS Preface vii Modular Forms on Noncongruence Subgroups BY A. O. L. ATKIN AND H. P. F. SWINNERTON-DYER 1 Selfconjugate Tetrahedra with Respect to the Hermitian Variety xl+xl + *l + ;cg = 0 in PG(3, 22) and a Representation of PG(3, 3) BY R. -
Collineations in Perspective
Collineations in Perspective Now that we have a decent grasp of one-dimensional projectivities, we move on to their two di- mensional analogs. Although they are more complicated, in a sense, they may be easier to grasp because of the many applications to perspective drawing. Speaking of, let's return to the triangle on the window and its shadow in its full form instead of only looking at one line. Perspective Collineation In one dimension, a perspectivity is a bijective mapping from a line to a line through a point. In two dimensions, a perspective collineation is a bijective mapping from a plane to a plane through a point. To illustrate, consider the triangle on the window plane and its shadow on the ground plane as in Figure 1. We can see that every point on the triangle on the window maps to exactly one point on the shadow, but the collineation is from the entire window plane to the entire ground plane. We understand the window plane to extend infinitely in all directions (even going through the ground), the ground also extends infinitely in all directions (we will assume that the earth is flat here), and we map every point on the window to a point on the ground. Looking at Figure 2, we see that the lamp analogy breaks down when we consider all lines through O. Although it makes sense for the base of the triangle on the window mapped to its shadow on 1 the ground (A to A0 and B to B0), what do we make of the mapping C to C0, or D to D0? C is on the window plane, underground, while C0 is on the ground. -
Performance Evaluation of Genetic Algorithm and Simulated Annealing in Solving Kirkman Schoolgirl Problem
FUOYE Journal of Engineering and Technology (FUOYEJET), Vol. 5, Issue 2, September 2020 ISSN: 2579-0625 (Online), 2579-0617 (Paper) Performance Evaluation of Genetic Algorithm and Simulated Annealing in solving Kirkman Schoolgirl Problem *1Christopher A. Oyeleye, 2Victoria O. Dayo-Ajayi, 3Emmanuel Abiodun and 4Alabi O. Bello 1Department of Information Systems, Ladoke Akintola University of Technology, Ogbomoso, Nigeria 2Department of Computer Science, Ladoke Akintola University of Technology, Ogbomoso, Nigeria 3Department of Computer Science, Kwara State Polytechnic, Ilorin, Nigeria 4Department of Mathematical and Physical Sciences, Afe Babalola University, Ado-Ekiti, Nigeria [email protected]|[email protected]|[email protected]|[email protected] Received: 15-FEB-2020; Reviewed: 12-MAR-2020; Accepted: 28-MAY-2020 http://dx.doi.org/10.46792/fuoyejet.v5i2.477 Abstract- This paper provides performance evaluation of Genetic Algorithm and Simulated Annealing in view of their software complexity and simulation runtime. Kirkman Schoolgirl is about arranging fifteen schoolgirls into five triplets in a week with a distinct constraint of no two schoolgirl must walk together in a week. The developed model was simulated using MATLAB version R2015a. The performance evaluation of both Genetic Algorithm (GA) and Simulated Annealing (SA) was carried out in terms of program size, program volume, program effort and the intelligent content of the program. The results obtained show that the runtime for GA and SA are 11.23sec and 6.20sec respectively. The program size for GA and SA are 2.01kb and 2.21kb, respectively. The lines of code for GA and SA are 324 and 404, respectively. The program volume for GA and SA are 1121.58 and 3127.92, respectively. -
Blocking Set Free Configurations and Their Relations to Digraphs and Hypergraphs
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector DISCRETE MATHEMATICS ELSIZI’IER Discrete Mathematics 1651166 (1997) 359.-370 Blocking set free configurations and their relations to digraphs and hypergraphs Harald Gropp* Mihlingstrasse 19. D-69121 Heidelberg, German> Abstract The current state of knowledge concerning the existence of blocking set free configurations is given together with a short history of this problem which has also been dealt with in terms of digraphs without even dicycles or 3-chromatic hypergraphs. The question is extended to the case of nonsymmetric configurations (u,, b3). It is proved that for each value of I > 3 there are only finitely many values of u for which the existence of a blocking set free configuration is still unknown. 1. Introduction In the language of configurations the existence of blocking sets was investigated in [3, 93 for the first time. Further papers [4, 201 yielded the result that the existence problem for blocking set free configurations v3 has been nearly solved. There are only 8 values of v for which it is unsettled whether there is a blocking set free configuration ~7~:u = 15,16,17,18,20,23,24,26. The existence of blocking set free configurations is equivalent to the existence of certain hypergraphs and digraphs. In these two languages results have been obtained much earlier. In Section 2 the relations between configurations, hypergraphs, and digraphs are exhibited. Furthermore, a nearly forgotten result of Steinitz is described In his dissertation of 1894 Steinitz proved the existence of a l-factor in a regular bipartite graph 20 years earlier then Kiinig. -
Finite Projective Geometry 2Nd Year Group Project
Finite Projective Geometry 2nd year group project. B. Doyle, B. Voce, W.C Lim, C.H Lo Mathematics Department - Imperial College London Supervisor: Ambrus Pal´ June 7, 2015 Abstract The Fano plane has a strong claim on being the simplest symmetrical object with inbuilt mathematical structure in the universe. This is due to the fact that it is the smallest possible projective plane; a set of points with a subsets of lines satisfying just three axioms. We will begin by developing some theory direct from the axioms and uncovering some of the hidden (and not so hidden) symmetries of the Fano plane. Alternatively, some projective planes can be derived from vector space theory and we shall also explore this and the associated linear maps on these spaces. Finally, with the help of some theory of quadratic forms we will give a proof of the surprising Bruck-Ryser theorem, which shows that if a projective plane has order n congruent to 1 or 2 mod 4, then n is the sum of two squares. Thus we will have demonstrated fascinating links between pure mathematical disciplines by incorporating the use of linear algebra, group the- ory and number theory to explain the geometric world of projective planes. 1 Contents 1 Introduction 3 2 Basic Defintions and results 4 3 The Fano Plane 7 3.1 Isomorphism and Automorphism . 8 3.2 Ovals . 10 4 Projective Geometry with fields 12 4.1 Constructing Projective Planes from fields . 12 4.2 Order of Projective Planes over fields . 14 5 Bruck-Ryser 17 A Appendix - Rings and Fields 22 2 1 Introduction Projective planes are geometrical objects that consist of a set of elements called points and sub- sets of these elements called lines constructed following three basic axioms which give the re- sulting object a remarkable level of symmetry. -
Cramer Benjamin PMET
Just-in-Time-Teaching and other gadgets Richard Cramer-Benjamin Niagara University http://faculty.niagara.edu/richcb The Class MAT 443 – Euclidean Geometry 26 Students 12 Secondary Ed (9-12 or 5-12 Certification) 14 Elementary Ed (1-6, B-6, or 1-9 Certification) The Class Venema, G., Foundations of Geometry , Preliminaries/Discrete Geometry 2 weeks Axioms of Plane Geometry 3 weeks Neutral Geometry 3 weeks Euclidean Geometry 3 weeks Circles 1 week Transformational Geometry 2 weeks Other Sources Requiring Student Questions on the Text Bonnie Gold How I (Finally) Got My Calculus I Students to Read the Text Tommy Ratliff MAA Inovative Teaching Exchange JiTT Just-in-Time-Teaching Warm-Ups Physlets Puzzles On-line Homework Interactive Lessons JiTTDLWiki JiTTDLWiki Goals Teach Students to read a textbook Math classes have taught students not to read the text. Get students thinking about the material Identify potential difficulties Spend less time lecturing Example Questions For February 1 Subject line WarmUp 3 LastName Due 8:00 pm, Tuesday, January 31. Read Sections 5.1-5.4 Be sure to understand The different axiomatic systems (Hilbert's, Birkhoff's, SMSG, and UCSMP), undefined terms, Existence Postulate, plane, Incidence Postulate, lie on, parallel, the ruler postulate, between, segment, ray, length, congruent, Theorem 5.4.6*, Corrollary 5.4.7*, Euclidean Metric, Taxicab Metric, Coordinate functions on Euclidean and taxicab metrics, the rational plane. Questions Compare Hilbert's axioms with the UCSMP axioms in the appendix. What are some observations you can make? What is a coordinate function? What does it have to do with the ruler placement postulate? What does the rational plane model demonstrate? List 3 statements about the reading. -
Self-Dual Configurations and Regular Graphs
SELF-DUAL CONFIGURATIONS AND REGULAR GRAPHS H. S. M. COXETER 1. Introduction. A configuration (mci ni) is a set of m points and n lines in a plane, with d of the points on each line and c of the lines through each point; thus cm = dn. Those permutations which pre serve incidences form a group, "the group of the configuration." If m — n, and consequently c = d, the group may include not only sym metries which permute the points among themselves but also reci procities which interchange points and lines in accordance with the principle of duality. The configuration is then "self-dual," and its symbol («<*, n<j) is conveniently abbreviated to na. We shall use the same symbol for the analogous concept of a configuration in three dimensions, consisting of n points lying by d's in n planes, d through each point. With any configuration we can associate a diagram called the Menger graph [13, p. 28],x in which the points are represented by dots or "nodes," two of which are joined by an arc or "branch" when ever the corresponding two points are on a line of the configuration. Unfortunately, however, it often happens that two different con figurations have the same Menger graph. The present address is concerned with another kind of diagram, which represents the con figuration uniquely. In this Levi graph [32, p. 5], we represent the points and lines (or planes) of the configuration by dots of two colors, say "red nodes" and "blue nodes," with the rule that two nodes differently colored are joined whenever the corresponding elements of the configuration are incident. -
Geometry and Dynamics of Configuration Spaces Mickaël Kourganoff
Geometry and dynamics of configuration spaces Mickaël Kourganoff To cite this version: Mickaël Kourganoff. Geometry and dynamics of configuration spaces. Differential Geometry [math.DG]. Ecole normale supérieure de lyon - ENS LYON, 2015. English. NNT : 2015ENSL1049. tel-01250817 HAL Id: tel-01250817 https://tel.archives-ouvertes.fr/tel-01250817 Submitted on 5 Jan 2016 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. These` en vue de l’obtention du grade de Docteur de l’Universit´ede Lyon, d´elivr´epar l’Ecole´ Normale Sup´erieurede Lyon Discipline : Math´ematiques pr´esent´eeet soutenue publiquement le 4 d´ecembre 2015 par Micka¨el Kourganoff G´eom´etrieet dynamique des espaces de configuration Directeur de th`ese: Abdelghani Zeghib Devant la commission d’examen form´eede : Viviane Baladi (Universit´ePierre et Marie Curie, Paris), examinatrice Thierry Barbot (Universit´ed’Avignon), rapporteur G´erard Besson (Universit´ede Grenoble), examinateur Etienne´ Ghys( Ecole´ Normale Sup´erieure de Lyon), examinateur Boris Hasselblatt (Tufts University), rapporteur Mark Pollicott (University of Warwick), rapporteur BrunoS evennec´ ( Ecole´ Normale Sup´erieure de Lyon), examinateur Abdelghani Zeghib( Ecole´ Normale Sup´erieure de Lyon), directeur Unit´ede Math´ematiques Pures et Appliqu´ees– Ecole´ doctorale InfoMaths 2 Remerciements Mes premiers remerciements vont `aGhani, qui m’a fait d´ecouvrir un sujet passionnant, riche et original, et m’a laiss´eune grande libert´e,tout en sachant s’impliquer dans le d´etaild`esque n´ecessaire. -
Intel® MAX® 10 FPGA Configuration User Guide
Intel® MAX® 10 FPGA Configuration User Guide Subscribe UG-M10CONFIG | 2021.07.02 Send Feedback Latest document on the web: PDF | HTML Contents Contents 1. Intel® MAX® 10 FPGA Configuration Overview................................................................ 4 2. Intel MAX 10 FPGA Configuration Schemes and Features...............................................5 2.1. Configuration Schemes..........................................................................................5 2.1.1. JTAG Configuration.................................................................................... 5 2.1.2. Internal Configuration................................................................................ 6 2.2. Configuration Features.........................................................................................13 2.2.1. Remote System Upgrade...........................................................................13 2.2.2. Configuration Design Security....................................................................20 2.2.3. SEU Mitigation and Configuration Error Detection........................................ 24 2.2.4. Configuration Data Compression................................................................ 27 2.3. Configuration Details............................................................................................ 28 2.3.1. Configuration Sequence............................................................................ 28 2.3.2. Intel MAX 10 Configuration Pins................................................................ -
Matroid Enumeration for Incidence Geometry
Discrete Comput Geom (2012) 47:17–43 DOI 10.1007/s00454-011-9388-y Matroid Enumeration for Incidence Geometry Yoshitake Matsumoto · Sonoko Moriyama · Hiroshi Imai · David Bremner Received: 30 August 2009 / Revised: 25 October 2011 / Accepted: 4 November 2011 / Published online: 30 November 2011 © Springer Science+Business Media, LLC 2011 Abstract Matroids are combinatorial abstractions for point configurations and hy- perplane arrangements, which are fundamental objects in discrete geometry. Matroids merely encode incidence information of geometric configurations such as collinear- ity or coplanarity, but they are still enough to describe many problems in discrete geometry, which are called incidence problems. We investigate two kinds of inci- dence problem, the points–lines–planes conjecture and the so-called Sylvester–Gallai type problems derived from the Sylvester–Gallai theorem, by developing a new algo- rithm for the enumeration of non-isomorphic matroids. We confirm the conjectures of Welsh–Seymour on ≤11 points in R3 and that of Motzkin on ≤12 lines in R2, extend- ing previous results. With respect to matroids, this algorithm succeeds to enumerate a complete list of the isomorph-free rank 4 matroids on 10 elements. When geometric configurations corresponding to specific matroids are of interest in some incidence problems, they should be analyzed on oriented matroids. Using an encoding of ori- ented matroid axioms as a boolean satisfiability (SAT) problem, we also enumerate oriented matroids from the matroids of rank 3 on n ≤ 12 elements and rank 4 on n ≤ 9 elements. We further list several new minimal non-orientable matroids. Y. Matsumoto · H. Imai Graduate School of Information Science and Technology, University of Tokyo, Tokyo, Japan Y. -
Projective Planes
Projective Planes A projective plane is an incidence system of points and lines satisfying the following axioms: (P1) Any two distinct points are joined by exactly one line. (P2) Any two distinct lines meet in exactly one point. (P3) There exists a quadrangle: four points of which no three are collinear. In class we will exhibit a projective plane with 57 points and 57 lines: the game of The Fano Plane (of order 2): SpotIt®. (Actually the game is sold with 7 points, 7 lines only 55 lines; for the purposes of this 3 points on each line class I have added the two missing lines.) 3 lines through each point The smallest projective plane, often called the Fano plane, has seven points and seven lines, as shown on the right. The Projective Plane of order 3: 13 points, 13 lines The second smallest 4 points on each line projective plane, 4 lines through each point having thirteen points 0, 1, 2, …, 12 and thirteen lines A, B, C, …, M is shown on the left. These two planes are coordinatized by the fields of order 2 and 3 respectively; this accounts for the term ‘order’ which we shall define shortly. Given any field 퐹, the classical projective plane over 퐹 is constructed from a 3-dimensional vector space 퐹3 = {(푥, 푦, 푧) ∶ 푥, 푦, 푧 ∈ 퐹} as follows: ‘Points’ are one-dimensional subspaces 〈(푥, 푦, 푧)〉. Here (푥, 푦, 푧) ∈ 퐹3 is any nonzero vector; it spans a one-dimensional subspace 〈(푥, 푦, 푧)〉 = {휆(푥, 푦, 푧) ∶ 휆 ∈ 퐹}.