Homography (Or Planar Perspective Map)
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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. -
• 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. -
Math 535 - General Topology Additional Notes
Math 535 - General Topology Additional notes Martin Frankland September 5, 2012 1 Subspaces Definition 1.1. Let X be a topological space and A ⊆ X any subset. The subspace topology sub on A is the smallest topology TA making the inclusion map i: A,! X continuous. sub In other words, TA is generated by subsets V ⊆ A of the form V = i−1(U) = U \ A for any open U ⊆ X. Proposition 1.2. The subspace topology on A is sub TA = fV ⊆ A j V = U \ A for some open U ⊆ Xg: In other words, the collection of subsets of the form U \ A already forms a topology on A. 2 Products Before discussing the product of spaces, let us review the notion of product of sets. 2.1 Product of sets Let X and Y be sets. The Cartesian product of X and Y is the set of pairs X × Y = f(x; y) j x 2 X; y 2 Y g: It comes equipped with the two projection maps pX : X × Y ! X and pY : X × Y ! Y onto each factor, defined by pX (x; y) = x pY (x; y) = y: This explicit description of X × Y is made more meaningful by the following proposition. 1 Proposition 2.1. The Cartesian product of sets satisfies the following universal property. For any set Z along with maps fX : Z ! X and fY : Z ! Y , there is a unique map f : Z ! X × Y satisfying pX ◦ f = fX and pY ◦ f = fY , in other words making the diagram Z fX 9!f fY X × Y pX pY { " XY commute. -
Math 395: Category Theory Northwestern University, Lecture Notes
Math 395: Category Theory Northwestern University, Lecture Notes Written by Santiago Can˜ez These are lecture notes for an undergraduate seminar covering Category Theory, taught by the author at Northwestern University. The book we roughly follow is “Category Theory in Context” by Emily Riehl. These notes outline the specific approach we’re taking in terms the order in which topics are presented and what from the book we actually emphasize. We also include things we look at in class which aren’t in the book, but otherwise various standard definitions and examples are left to the book. Watch out for typos! Comments and suggestions are welcome. Contents Introduction to Categories 1 Special Morphisms, Products 3 Coproducts, Opposite Categories 7 Functors, Fullness and Faithfulness 9 Coproduct Examples, Concreteness 12 Natural Isomorphisms, Representability 14 More Representable Examples 17 Equivalences between Categories 19 Yoneda Lemma, Functors as Objects 21 Equalizers and Coequalizers 25 Some Functor Properties, An Equivalence Example 28 Segal’s Category, Coequalizer Examples 29 Limits and Colimits 29 More on Limits/Colimits 29 More Limit/Colimit Examples 30 Continuous Functors, Adjoints 30 Limits as Equalizers, Sheaves 30 Fun with Squares, Pullback Examples 30 More Adjoint Examples 30 Stone-Cech 30 Group and Monoid Objects 30 Monads 30 Algebras 30 Ultrafilters 30 Introduction to Categories Category theory provides a framework through which we can relate a construction/fact in one area of mathematics to a construction/fact in another. The goal is an ultimate form of abstraction, where we can truly single out what about a given problem is specific to that problem, and what is a reflection of a more general phenomenom which appears elsewhere. -
9.2.2 Projection Formula Definition 2.8
9.2. Intersection and morphisms 397 Proof Let Γ ⊆ X×S Z be the graph of ϕ. This is an S-scheme and the projection p :Γ → X is projective birational. Let us show that Γ admits a desingularization. This is Theorem 8.3.44 if dim S = 0. Let us therefore suppose that dim S = 1. Let K = K(S). Then pK :ΓK → XK is proper birational with XK normal, and hence pK is an isomorphism. We can therefore apply Theorem 8.3.50. Let g : Xe → Γ be a desingularization morphism, and f : Xe → X the composition p ◦ g. It now suffices to apply Theorem 2.2 to the morphism f. 9.2.2 Projection formula Definition 2.8. Let X, Y be Noetherian schemes, and let f : X → Y be a proper morphism. For any prime cycle Z on X (Section 7.2), we set W = f(Z) and ½ [K(Z): K(W )]W if K(Z) is finite over K(W ) f Z = ∗ 0 otherwise. By linearity, we define a homomorphism f∗ from the group of cycles on X to the group of cycles on Y . This generalizes Definition 7.2.17. It is clear that the construction of f∗ is compatible with the composition of morphisms. Remark 2.9. We can interpret the intersection of two divisors in terms of direct images of cycles. Let C, D be two Cartier divisors on a regular fibered surface X → S, of which at least one is vertical. Let us suppose that C is effective and has no common component with Supp D. -
Random Projections for Manifold Learning
Random Projections for Manifold Learning Chinmay Hegde Michael B. Wakin ECE Department EECS Department Rice University University of Michigan [email protected] [email protected] Richard G. Baraniuk ECE Department Rice University [email protected] Abstract We propose a novel method for linear dimensionality reduction of manifold mod- eled data. First, we show that with a small number M of random projections of sample points in RN belonging to an unknown K-dimensional Euclidean mani- fold, the intrinsic dimension (ID) of the sample set can be estimated to high accu- racy. Second, we rigorously prove that using only this set of random projections, we can estimate the structure of the underlying manifold. In both cases, the num- ber of random projections required is linear in K and logarithmic in N, meaning that K < M ≪ N. To handle practical situations, we develop a greedy algorithm to estimate the smallest size of the projection space required to perform manifold learning. Our method is particularly relevant in distributed sensing systems and leads to significant potential savings in data acquisition, storage and transmission costs. 1 Introduction Recently, we have witnessed a tremendous increase in the sizes of data sets generated and processed by acquisition and computing systems. As the volume of the data increases, memory and processing requirements need to correspondingly increase at the same rapid pace, and this is often prohibitively expensive. Consequently, there has been considerable interest in the task of effective modeling of high-dimensional observed data and information; such models must capture the structure of the information content in a concise manner. -
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. -
3D to 2D Bijection for Spherical Objects Under Equidistant Fisheye Projection
Computer Vision and Image Understanding 125 (2014) 172–183 Contents lists available at ScienceDirect Computer Vision and Image Understanding journal homepage: www.elsevier.com/locate/cviu 3D to 2D bijection for spherical objects under equidistant fisheye projection q ⇑ Aamir Ahmad , João Xavier, José Santos-Victor, Pedro Lima Institute for Systems and Robotics, Instituto Superior Técnico, Lisboa, Portugal article info abstract Article history: The core problem addressed in this article is the 3D position detection of a spherical object of known- Received 6 November 2013 radius in a single image frame, obtained by a dioptric vision system consisting of only one fisheye lens Accepted 5 April 2014 camera that follows equidistant projection model. The central contribution is a bijection principle Available online 16 April 2014 between a known-radius spherical object’s 3D world position and its 2D projected image curve, that we prove, thus establishing that for every possible 3D world position of the spherical object, there exists Keywords: a unique curve on the image plane if the object is projected through a fisheye lens that follows equidis- Equidistant projection tant projection model. Additionally, we present a setup for the experimental verification of the principle’s Fisheye lens correctness. In previously published works we have applied this principle to detect and subsequently Spherical object detection Omnidirectional vision track a known-radius spherical object. 3D detection Ó 2014 Elsevier Inc. All rights reserved. 1. Introduction 1.2. Related work 1.1. Motivation Spherical object position detection is a functionality required by innumerable applications in the areas ranging from mobile robot- In recent years, omni-directional vision involving cata-dioptric ics [1], biomedical imaging [3,4], machine vision [5] to space- [1] and dioptric [2] vision systems has become one of the most related robotic applications [6]. -
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. -
Projection Complexes and Quasimedian Maps
PROJECTION COMPLEXES AND QUASIMEDIAN MAPS MARK HAGEN AND HARRY PETYT Abstract. We use the projection complex machinery of Bestvina–Bromberg–Fujiwara to study hierarchically hyperbolic groups. In particular, we show that if the associated hyperbolic spaces are quasiisometric to trees then the group is quasiisometric to a fi- nite dimensional CAT(0) cube complex. We use this to deduce a number of properties, including the Helly property for hierarchically quasiconvex subsets, and the fact that such a group has finitely many conjugacy classes of finite subgroups. Future work will examine the extent to which the quasitree hypothesis can be replaced by more straightforward algebraic assumptions, in the presence of a BBF colouring. 1. Introduction The original motivation for defining hierarchically hyperbolic groups was to build a bridge between the worlds of mapping class groups and cubical groups, providing a frame- work for studying both. The idea is that these groups can be studied via a “hierarchy” of associated hyperbolic spaces: a question about the group is broken into questions about the hyperbolic spaces and the relations between them, and the answers are assembled into an answer to the original question, often using core structure theorems established in [BHS19]. This is a common generalisation of the Masur–Minsky machinery, [MM00], and of [BHS17b], and is somewhat related to the work of Kim–Koberda on right-angled Artin groups [KK14], and also to the work of Bestvina–Bromberg–Fujiwara [BBF15] (which features prominently in the present paper). When a cubical group admits such a structure (which, in fact, all known cubical groups do [HS16]), the associated hyperbolic spaces can always be taken to be quasiisometric to trees (i.e.