Projective Geometry: a Short Introduction
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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 . -
13 Circles and Cross Ratio
13 Circles and Cross Ratio Following up Klein’s Erlangen Program, after defining the group of linear transformations, we study the invariant properites of subsets in the Riemann Sphere under this group. Appolonius’ Circle Recall that a line or a circle on the complex plane, their corresondence in the Riemann Sphere are all circles. We would like to prove that any linear transformation f maps a circle or a line into another circle or line. To prove this, we may separate 4 cases: f maps a line to a line; f maps a line to a circle; f maps a circle to a line; and f maps a circle to a circle. Also, we observe that f is a composition of several simple linear transformations of three type: 1 f1(z)= az, f2(z)= a + b and f3(z)= z because az + b g(z) f(z)= = = g(z)φ(h(z)) (56) cz + d h(z) 1 where g(z) = az + b, h(z) = cz + d and φ(z) = z . Then it suffices to separate 8 cases: fj maps a line to a line; fj maps a line to a circle; fj maps a circle to a line; and fj maps a circle to a circle for j =1, 2, 3. To simplify this proof, we want to write a line or a circle by a single equation, although usually equations for a line and for a circle are are quite different. Let us consider the following single equation: z − z1 = k, (57) z − z2 for some z1, z2 ∈ C and 0 <k< ∞. -
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 -
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. -
Projective Coordinates and Compactification in Elliptic, Parabolic and Hyperbolic 2-D Geometry
PROJECTIVE COORDINATES AND COMPACTIFICATION IN ELLIPTIC, PARABOLIC AND HYPERBOLIC 2-D GEOMETRY Debapriya Biswas Department of Mathematics, Indian Institute of Technology-Kharagpur, Kharagpur-721302, India. e-mail: d [email protected] Abstract. A result that the upper half plane is not preserved in the hyperbolic case, has implications in physics, geometry and analysis. We discuss in details the introduction of projective coordinates for the EPH cases. We also introduce appropriate compactification for all the three EPH cases, which results in a sphere in the elliptic case, a cylinder in the parabolic case and a crosscap in the hyperbolic case. Key words. EPH Cases, Projective Coordinates, Compactification, M¨obius transformation, Clifford algebra, Lie group. 2010(AMS) Mathematics Subject Classification: Primary 30G35, Secondary 22E46. 1. Introduction Geometry is an essential branch of Mathematics. It deals with the proper- ties of figures in a plane or in space [7]. Perhaps, the most influencial book of all time, is ‘Euclids Elements’, written in around 3000 BC [14]. Since Euclid, geometry usually meant the geometry of Euclidean space of two-dimensions (2-D, Plane geometry) and three-dimensions (3-D, Solid geometry). A close scrutiny of the basis for the traditional Euclidean geometry, had revealed the independence of the parallel axiom from the others and consequently, non- Euclidean geometry was born and in projective geometry, new “points” (i.e., points at infinity and points with complex number coordinates) were intro- duced [1, 3, 9, 26]. In the eighteenth century, under the influence of Steiner, Von Staudt, Chasles and others, projective geometry became one of the chief subjects of mathematical research [7]. -
Arxiv:1910.10745V1 [Cond-Mat.Str-El] 23 Oct 2019 2.2 Symmetry-Protected Time Crystals
A Brief History of Time Crystals Vedika Khemania,b,∗, Roderich Moessnerc, S. L. Sondhid aDepartment of Physics, Harvard University, Cambridge, Massachusetts 02138, USA bDepartment of Physics, Stanford University, Stanford, California 94305, USA cMax-Planck-Institut f¨urPhysik komplexer Systeme, 01187 Dresden, Germany dDepartment of Physics, Princeton University, Princeton, New Jersey 08544, USA Abstract The idea of breaking time-translation symmetry has fascinated humanity at least since ancient proposals of the per- petuum mobile. Unlike the breaking of other symmetries, such as spatial translation in a crystal or spin rotation in a magnet, time translation symmetry breaking (TTSB) has been tantalisingly elusive. We review this history up to recent developments which have shown that discrete TTSB does takes place in periodically driven (Floquet) systems in the presence of many-body localization (MBL). Such Floquet time-crystals represent a new paradigm in quantum statistical mechanics — that of an intrinsically out-of-equilibrium many-body phase of matter with no equilibrium counterpart. We include a compendium of the necessary background on the statistical mechanics of phase structure in many- body systems, before specializing to a detailed discussion of the nature, and diagnostics, of TTSB. In particular, we provide precise definitions that formalize the notion of a time-crystal as a stable, macroscopic, conservative clock — explaining both the need for a many-body system in the infinite volume limit, and for a lack of net energy absorption or dissipation. Our discussion emphasizes that TTSB in a time-crystal is accompanied by the breaking of a spatial symmetry — so that time-crystals exhibit a novel form of spatiotemporal order. -
The Fractal Dimension of Product Sets
THE FRACTAL DIMENSION OF PRODUCT SETS A PREPRINT Clayton Moore Williams Brigham Young University [email protected] Machiel van Frankenhuijsen Utah Valley University [email protected] February 26, 2021 ABSTRACT Using methods from nonstandard analysis, we define a nonstandard Minkowski dimension which exists for all bounded sets and which has the property that dim(A × B) = dim(A) + dim(B). That is, our new dimension is “product-summable”. To illustrate our theorem we generalize an example of Falconer’s1 to show that the standard upper Minkowski dimension, as well as the Hausdorff di- mension, are not product-summable. We also include a method for creating sets of arbitrary rational dimension. Introduction There are several notions of dimension used in fractal geometry, which coincide for many sets but have important, distinct properties. Indeed, determining the most proper notion of dimension has been a major problem in geometric measure theory from its inception, and debate over what it means for a set to be fractal has often reduced to debate over the propernotion of dimension. A classical example is the “Devil’s Staircase” set, which is intuitively fractal but which has integer Hausdorff dimension2. As Falconer notes in The Geometry of Fractal Sets3, the Hausdorff dimension is “undoubtedly, the most widely investigated and most widely used” notion of dimension. It can, however, be difficult to compute or even bound (from below) for many sets. For this reason one might want to work with the Minkowski dimension, for which one can often obtain explicit formulas. Moreover, the Minkowski dimension is computed using finite covers and hence has a series of useful identities which can be used in analysis. -
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. -
Definition Concurrent Lines Are Lines That Intersect in a Single Point. 1. Theorem 128: the Perpendicular Bisectors of the Sides
14.3 Notes Thursday, April 23, 2009 12:49 PM Definition 1. Concurrent lines are lines that intersect in a single point. j k m Theorem 128: The perpendicular bisectors of the sides of a triangle are concurrent at a point that is equidistant from the vertices of the triangle. This point is called the circumcenter of the triangle. D E F Theorem 129: The bisectors of the angles of a triangle are concurrent at a point that is equidistant from the sides of the triangle. This point is called the incenter of the triangle. A B Notes Page 1 C A B C Theorem 130: The lines containing the altitudes of a triangle are concurrent. This point is called the orthocenter of the triangle. A B C Theorem 131: The medians of a triangle are concurrent at a point that is 2/3 of the way from any vertex of the triangle to the midpoint of the opposite side. This point is called the centroid of the of the triangle. Example 1: Construct the incenter of ABC A B C Notes Page 2 14.4 Notes Friday, April 24, 2009 1:10 PM Examples 1-3 on page 670 1. Construct an angle whose measure is equal to 2A - B. A B 2. Construct the tangent to circle P at point A. P A 3. Construct a tangent to circle O from point P. Notes Page 3 3. Construct a tangent to circle O from point P. O P Notes Page 4 14.5 notes Tuesday, April 28, 2009 8:26 AM Constructions 9, 10, 11 Geometric mean Notes Page 5 14.6 Notes Tuesday, April 28, 2009 9:54 AM Construct: ABC, given {a, ha, B} a Ha B A b c B C a Notes Page 6 14.1 Notes Tuesday, April 28, 2009 10:01 AM Definition: A locus is a set consisting of all points, and only the points, that satisfy specific conditions. -
4 Jul 2018 Is Spacetime As Physical As Is Space?
Is spacetime as physical as is space? Mayeul Arminjon Univ. Grenoble Alpes, CNRS, Grenoble INP, 3SR, F-38000 Grenoble, France Abstract Two questions are investigated by looking successively at classical me- chanics, special relativity, and relativistic gravity: first, how is space re- lated with spacetime? The proposed answer is that each given reference fluid, that is a congruence of reference trajectories, defines a physical space. The points of that space are formally defined to be the world lines of the congruence. That space can be endowed with a natural structure of 3-D differentiable manifold, thus giving rise to a simple notion of spatial tensor — namely, a tensor on the space manifold. The second question is: does the geometric structure of the spacetime determine the physics, in particular, does it determine its relativistic or preferred- frame character? We find that it does not, for different physics (either relativistic or not) may be defined on the same spacetime structure — and also, the same physics can be implemented on different spacetime structures. MSC: 70A05 [Mechanics of particles and systems: Axiomatics, founda- tions] 70B05 [Mechanics of particles and systems: Kinematics of a particle] 83A05 [Relativity and gravitational theory: Special relativity] 83D05 [Relativity and gravitational theory: Relativistic gravitational theories other than Einstein’s] Keywords: Affine space; classical mechanics; special relativity; relativis- arXiv:1807.01997v1 [gr-qc] 4 Jul 2018 tic gravity; reference fluid. 1 Introduction and Summary What is more “physical”: space or spacetime? Today, many physicists would vote for the second one. Whereas, still in 1905, spacetime was not a known concept. It has been since realized that, even in classical mechanics, space and time are coupled: already in the minimum sense that space does not exist 1 without time and vice-versa, and also through the Galileo transformation. -
Lecture 3: Geometry
E-320: Teaching Math with a Historical Perspective Oliver Knill, 2010-2015 Lecture 3: Geometry Geometry is the science of shape, size and symmetry. While arithmetic dealt with numerical structures, geometry deals with metric structures. Geometry is one of the oldest mathemati- cal disciplines and early geometry has relations with arithmetics: we have seen that that the implementation of a commutative multiplication on the natural numbers is rooted from an inter- pretation of n × m as an area of a shape that is invariant under rotational symmetry. Number systems built upon the natural numbers inherit this. Identities like the Pythagorean triples 32 +42 = 52 were interpreted geometrically. The right angle is the most "symmetric" angle apart from 0. Symmetry manifests itself in quantities which are invariant. Invariants are one the most central aspects of geometry. Felix Klein's Erlanger program uses symmetry to classify geome- tries depending on how large the symmetries of the shapes are. In this lecture, we look at a few results which can all be stated in terms of invariants. In the presentation as well as the worksheet part of this lecture, we will work us through smaller miracles like special points in triangles as well as a couple of gems: Pythagoras, Thales,Hippocrates, Feuerbach, Pappus, Morley, Butterfly which illustrate the importance of symmetry. Much of geometry is based on our ability to measure length, the distance between two points. A modern way to measure distance is to determine how long light needs to get from one point to the other. This geodesic distance generalizes to curved spaces like the sphere and is also a practical way to measure distances, for example with lasers. -
Notes 20: Afine Geometry
Notes 20: Afine Geometry Example 1. Let C be the curve in R2 defined by x2 + 4xy + y2 + y2 − 1 = 0 = (x + 2y)2 + y2 − 1: If we set u = x + 2y; v = y; we obtain u2 + v2 − 1 = 0 which is a circle in the u; v plane. Let 2 2 F : R ! R ; (x; y) 7! (u = 2x + y; v = y): We look at the geometry of F: Angles: This transformation does not preserve angles. For example the line 2x + y = 0 is mapped to the line u = 0; and the line y = 0 is mapped to v = 0: The two lines in the x − y plane are not orthogonal, while the lines in the u − v plane are orthogonal. Distances: This transformation does not preserve distance. The point A = (−1=2; 1) is mapped to a = (0; 1) and the point B = (0; 0) is mapped to b = (0; 0): the distance AB is not equal to the distance ab: Circles: The curve C is an ellipse with axis along the lines 2x + y = 0 and x = 0: It is mapped to the unit circle in the u − v plane. Lines: The map is a one-to-one onto map that maps lines to lines. What is a Geometry? We can think of a geometry as having three parts: A set of points, a special set of subsets called lines, and a group of transformations of the set of points that maps lines to lines. In the case of Euclidean geometry, the set of points is the familiar plane R2: The special subsets are what we ordinarily call lines.