Dual Quaternions and Their Applications in Robot Kinematics
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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. -
Lecture 12: Camera Projection
Robert Collins CSE486, Penn State Lecture 12: Camera Projection Reading: T&V Section 2.4 Robert Collins CSE486, Penn State Imaging Geometry W Object of Interest in World Coordinate System (U,V,W) V U Robert Collins CSE486, Penn State Imaging Geometry Camera Coordinate Y System (X,Y,Z). Z X • Z is optic axis f • Image plane located f units out along optic axis • f is called focal length Robert Collins CSE486, Penn State Imaging Geometry W Y y X Z x V U Forward Projection onto image plane. 3D (X,Y,Z) projected to 2D (x,y) Robert Collins CSE486, Penn State Imaging Geometry W Y y X Z x V u U v Our image gets digitized into pixel coordinates (u,v) Robert Collins CSE486, Penn State Imaging Geometry Camera Image (film) World Coordinates Coordinates CoordinatesW Y y X Z x V u U v Pixel Coordinates Robert Collins CSE486, Penn State Forward Projection World Camera Film Pixel Coords Coords Coords Coords U X x u V Y y v W Z We want a mathematical model to describe how 3D World points get projected into 2D Pixel coordinates. Our goal: describe this sequence of transformations by a big matrix equation! Robert Collins CSE486, Penn State Backward Projection World Camera Film Pixel Coords Coords Coords Coords U X x u V Y y v W Z Note, much of vision concerns trying to derive backward projection equations to recover 3D scene structure from images (via stereo or motion) But first, we have to understand forward projection… Robert Collins CSE486, Penn State Forward Projection World Camera Film Pixel Coords Coords Coords Coords U X x u V Y y v W Z 3D-to-2D Projection • perspective projection We will start here in the middle, since we’ve already talked about this when discussing stereo. -
Chapter 1 the Field of Reals and Beyond
Chapter 1 The Field of Reals and Beyond Our goal with this section is to develop (review) the basic structure that character- izes the set of real numbers. Much of the material in the ¿rst section is a review of properties that were studied in MAT108 however, there are a few slight differ- ences in the de¿nitions for some of the terms. Rather than prove that we can get from the presentation given by the author of our MAT127A textbook to the previous set of properties, with one exception, we will base our discussion and derivations on the new set. As a general rule the de¿nitions offered in this set of Compan- ion Notes will be stated in symbolic form this is done to reinforce the language of mathematics and to give the statements in a form that clari¿es how one might prove satisfaction or lack of satisfaction of the properties. YOUR GLOSSARIES ALWAYS SHOULD CONTAIN THE (IN SYMBOLIC FORM) DEFINITION AS GIVEN IN OUR NOTES because that is the form that will be required for suc- cessful completion of literacy quizzes and exams where such statements may be requested. 1.1 Fields Recall the following DEFINITIONS: The Cartesian product of two sets A and B, denoted by A B,is a b : a + A F b + B . 1 2 CHAPTER 1. THE FIELD OF REALS AND BEYOND A function h from A into B is a subset of A B such that (i) 1a [a + A " 2bb + B F a b + h] i.e., dom h A,and (ii) 1a1b1c [a b + h F a c + h " b c] i.e., h is single-valued. -
Real Numbers and Their Properties
Real Numbers and their Properties Types of Numbers + • Z Natural numbers - counting numbers - 1, 2, 3,... The textbook uses the notation N. • Z Integers - 0, ±1, ±2, ±3,... The textbook uses the notation J. • Q Rationals - quotients (ratios) of integers. • R Reals - may be visualized as correspond- ing to all points on a number line. The reals which are not rational are called ir- rational. + Z ⊂ Z ⊂ Q ⊂ R. R ⊂ C, the field of complex numbers, but in this course we will only consider real numbers. Properties of Real Numbers There are four binary operations which take a pair of real numbers and result in another real number: Addition (+), Subtraction (−), Multiplication (× or ·), Division (÷ or /). These operations satisfy a number of rules. In the following, we assume a, b, c ∈ R. (In other words, a, b and c are all real numbers.) • Closure: a + b ∈ R, a · b ∈ R. This means we can add and multiply real num- bers. We can also subtract real numbers and we can divide as long as the denominator is not 0. • Commutative Law: a + b = b + a, a · b = b · a. This means when we add or multiply real num- bers, the order doesn’t matter. • Associative Law: (a + b) + c = a + (b + c), (a · b) · c = a · (b · c). We can thus write a + b + c or a · b · c without having to worry that different people will get different results. • Distributive Law: a · (b + c) = a · b + a · c, (a + b) · c = a · c + b · c. The distributive law is the one law which in- volves both addition and multiplication. -
Chapter 4 Complex Numbers Course Number
Chapter 4 Complex Numbers Course Number Section 4.1 Complex Numbers Instructor Objective: In this lesson you learned how to perform operations with Date complex numbers. Important Vocabulary Define each term or concept. Complex numbers The set of numbers obtained by adding real number to real multiples of the imaginary unit i. Complex conjugates A pair of complex numbers of the form a + bi and a – bi. I. The Imaginary Unit i (Page 328) What you should learn How to use the imaginary Mathematicians created an expanded system of numbers using unit i to write complex the imaginary unit i, defined as i = Ö - 1 , because . numbers there is no real number x that can be squared to produce - 1. By definition, i2 = - 1 . For the complex number a + bi, if b = 0, the number a + bi = a is a(n) real number . If b ¹ 0, the number a + bi is a(n) imaginary number .If a = 0, the number a + bi = bi is a(n) pure imaginary number . The set of complex numbers consists of the set of real numbers and the set of imaginary numbers . Two complex numbers a + bi and c + di, written in standard form, are equal to each other if . and only if a = c and b = d. II. Operations with Complex Numbers (Pages 329-330) What you should learn How to add, subtract, and To add two complex numbers, . add the real parts and the multiply complex imaginary parts of the numbers separately. numbers Larson/Hostetler Trigonometry, Sixth Edition Student Success Organizer IAE Copyright © Houghton Mifflin Company. -
Application of Dual Quaternions on Selected Problems
APPLICATION OF DUAL QUATERNIONS ON SELECTED PROBLEMS Jitka Prošková Dissertation thesis Supervisor: Doc. RNDr. Miroslav Láviˇcka, Ph.D. Plze ˇn, 2017 APLIKACE DUÁLNÍCH KVATERNIONU˚ NA VYBRANÉ PROBLÉMY Jitka Prošková Dizertaˇcní práce Školitel: Doc. RNDr. Miroslav Láviˇcka, Ph.D. Plze ˇn, 2017 Acknowledgement I would like to thank all the people who have supported me during my studies. Especially many thanks belong to my family for their moral and material support and my advisor doc. RNDr. Miroslav Láviˇcka, Ph.D. for his guidance. I hereby declare that this Ph.D. thesis is completely my own work and that I used only the cited sources. Plzeˇn, July 20, 2017, ........................... i Annotation In recent years, the study of quaternions has become an active research area of applied geometry, mainly due to an elegant and efficient possibility to represent using them ro- tations in three dimensional space. Thanks to their distinguished properties, quaternions are often used in computer graphics, inverse kinematics robotics or physics. Furthermore, dual quaternions are ordered pairs of quaternions. They are especially suitable for de- scribing rigid transformations, i.e., compositions of rotations and translations. It means that this structure can be considered as a very efficient tool for solving mathematical problems originated for instance in kinematics, bioinformatics or geodesy, i.e., whenever the motion of a rigid body defined as a continuous set of displacements is investigated. The main goal of this thesis is to provide a theoretical analysis and practical applications of the dual quaternions on the selected problems originated in geometric modelling and other sciences or various branches of technical practise. -
Point Sets up to Rigid Transformations Are Determined by the Distribution of Their Pairwise Distances
Point Sets Up to Rigid Transformations are Determined by the Distribution of their Pairwise Distances Daniel Chen December 6, 2008 Abstract This report is a summary of [BK06], which gives a simpler, albeit less general proof for the result of [BK04]. 1 Introduction [BK04] and [BK06] study the circumstances when sets of n-points in Euclidean space are determined up to rigid transformations by the distribution of unlabeled pairwise distances. In fact, they show the rather surprising result that any set of n-points in general position are determined up to rigid transformations by the distribution of unlabeled distances. 1.1 Practical Motivation The results of [BK04] and [BK06] are partly motivated by experimental results in [OFCD]. [OFCD] is an experimental study evaluating the use of shape distri- butions to classify 3D objects represented by point clouds. A shape distribution is a the distribution of a function on a randomly selected set of points from the point cloud. [OFCD] tested various such functions, including the angle be- tween three random points on the surface, the distance between the centroid and a random point, the distance between two random points, the square root of the area of the triangle between three random points, and the cube root of the volume of the tetrahedron between four random points. Then, the distri- butions are compared using dissimilarity measures, including χ2, Battacharyya, and Minkowski LN norms for both the pdf and cdf for N = 1; 2; 1. The experimental results showed that the shape distribution function that worked best for classification was the distance between two random points and the best dissimilarity measure was the pdf L1 norm. -
Draft Framework for a Teaching Unit: Transformations
Co-funded by the European Union PRIMARY TEACHER EDUCATION (PrimTEd) PROJECT GEOMETRY AND MEASUREMENT WORKING GROUP DRAFT FRAMEWORK FOR A TEACHING UNIT Preamble The general aim of this teaching unit is to empower pre-service students by exposing them to geometry and measurement, and the relevant pe dagogical content that would allow them to become skilful and competent mathematics teachers. The depth and scope of the content often go beyond what is required by prescribed school curricula for the Intermediate Phase learners, but should allow pre- service teachers to be well equipped, and approach the teaching of Geometry and Measurement with confidence. Pre-service teachers should essentially be prepared for Intermediate Phase teaching according to the requirements set out in MRTEQ (Minimum Requirements for Teacher Education Qualifications, 2019). “MRTEQ provides a basis for the construction of core curricula Initial Teacher Education (ITE) as well as for Continuing Professional Development (CPD) Programmes that accredited institutions must use in order to develop programmes leading to teacher education qualifications.” [p6]. Competent learning… a mixture of Theoretical & Pure & Extrinsic & Potential & Competent learning represents the acquisition, integration & application of different types of knowledge. Each type implies the mastering of specific related skills Disciplinary Subject matter knowledge & specific specialized subject Pedagogical Knowledge of learners, learning, curriculum & general instructional & assessment strategies & specialized Learning in & from practice – the study of practice using Practical learning case studies, videos & lesson observations to theorize practice & form basis for learning in practice – authentic & simulated classroom environments, i.e. Work-integrated Fundamental Learning to converse in a second official language (LOTL), ability to use ICT & acquisition of academic literacies Knowledge of varied learning situations, contexts & Situational environments of education – classrooms, schools, communities, etc. -
Half-Turns and Line Symmetric Motions
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by LSBU Research Open Half-turns and Line Symmetric Motions J.M. Selig Faculty of Business, Computing and Info. Management, London South Bank University, London SE1 0AA, U.K. (e-mail: [email protected]) and M. Husty Institute of Basic Sciences in Engineering Unit Geometry and CAD, Leopold-Franzens-Universit¨at Innsbruck, Austria. (e-mail: [email protected]) Abstract A line symmetric motion is the motion obtained by reflecting a rigid body in the successive generator lines of a ruled surface. In this work we review the dual quaternion approach to rigid body displacements, in particular the representation of the group SE(3) by the Study quadric. Then some classical work on reflections in lines or half-turns is reviewed. Next two new characterisations of line symmetric motions are presented. These are used to study a number of examples one of which is a novel line symmetric motion given by a rational degree five curve in the Study quadric. The rest of the paper investigates the connection between sets of half-turns and linear subspaces of the Study quadric. Line symmetric motions produced by some degenerate ruled surfaces are shown to be restricted to certain 2-planes in the Study quadric. Reflections in the lines of a linear line complex lie in the intersection of a the Study-quadric with a 4-plane. 1 Introduction In this work we revisit the classical idea of half-turns using modern mathematical techniques. In particular we use the dual quaternion representation of rigid-body motions. -
Ring (Mathematics) 1 Ring (Mathematics)
Ring (mathematics) 1 Ring (mathematics) In mathematics, a ring is an algebraic structure consisting of a set together with two binary operations usually called addition and multiplication, where the set is an abelian group under addition (called the additive group of the ring) and a monoid under multiplication such that multiplication distributes over addition.a[›] In other words the ring axioms require that addition is commutative, addition and multiplication are associative, multiplication distributes over addition, each element in the set has an additive inverse, and there exists an additive identity. One of the most common examples of a ring is the set of integers endowed with its natural operations of addition and multiplication. Certain variations of the definition of a ring are sometimes employed, and these are outlined later in the article. Polynomials, represented here by curves, form a ring under addition The branch of mathematics that studies rings is known and multiplication. as ring theory. Ring theorists study properties common to both familiar mathematical structures such as integers and polynomials, and to the many less well-known mathematical structures that also satisfy the axioms of ring theory. The ubiquity of rings makes them a central organizing principle of contemporary mathematics.[1] Ring theory may be used to understand fundamental physical laws, such as those underlying special relativity and symmetry phenomena in molecular chemistry. The concept of a ring first arose from attempts to prove Fermat's last theorem, starting with Richard Dedekind in the 1880s. After contributions from other fields, mainly number theory, the ring notion was generalized and firmly established during the 1920s by Emmy Noether and Wolfgang Krull.[2] Modern ring theory—a very active mathematical discipline—studies rings in their own right. -
The 22Nd International Conference on Finite Or Infinite Dimensional
The 22nd International Conference on Finite or Infinite Dimensional Complex Analysis and Applications August 8{August 11, 2014 Dongguk University Gyeongju, Republic of Korea The 22nd International Conference on Finite or Infinite Dimensional Complex Analysis and Applications http://22.icfidcaa.org August 8{11, 2014 Dongguk University Gyeongju, Republic of Korea Organized by • Dongguk University (Gyeongju Campus) • Youngnam Mathematical Society • BK21PLUS Center for Math Research and Education at PNU • Gyeongsang National University Supported by • KOFST (The Korean Federation of Science and Technology Societies) August 2, 2014 Contents Preface, Welcome and Acknowledgements ..................................1 Committee ......................................................................3 Topics of Conference ...........................................................4 History and Publications ......................................................5 Welcome Address ..............................................................8 Outline of Activities ...........................................................9 Details of Activities ..........................................................12 Abstracts of Talks .............................................................30 1 Preface, Welcome and Acknowledgements The 22nd International Conference on Finite or Infinite Dimensional Complex Analy- sis and Applications is being held at Dongguk University (Gyeongju), continuing to The 16th International Conference on Finite or Infinite Dimensional Complex -
1 Sets and Set Notation. Definition 1 (Naive Definition of a Set)
LINEAR ALGEBRA MATH 2700.006 SPRING 2013 (COHEN) LECTURE NOTES 1 Sets and Set Notation. Definition 1 (Naive Definition of a Set). A set is any collection of objects, called the elements of that set. We will most often name sets using capital letters, like A, B, X, Y , etc., while the elements of a set will usually be given lower-case letters, like x, y, z, v, etc. Two sets X and Y are called equal if X and Y consist of exactly the same elements. In this case we write X = Y . Example 1 (Examples of Sets). (1) Let X be the collection of all integers greater than or equal to 5 and strictly less than 10. Then X is a set, and we may write: X = f5; 6; 7; 8; 9g The above notation is an example of a set being described explicitly, i.e. just by listing out all of its elements. The set brackets {· · ·} indicate that we are talking about a set and not a number, sequence, or other mathematical object. (2) Let E be the set of all even natural numbers. We may write: E = f0; 2; 4; 6; 8; :::g This is an example of an explicity described set with infinitely many elements. The ellipsis (:::) in the above notation is used somewhat informally, but in this case its meaning, that we should \continue counting forever," is clear from the context. (3) Let Y be the collection of all real numbers greater than or equal to 5 and strictly less than 10. Recalling notation from previous math courses, we may write: Y = [5; 10) This is an example of using interval notation to describe a set.