1 Graphics CSCI 343, Fall 2015 Lecture 9 Geometric Objects.

Slides:



Advertisements
Similar presentations
Vectors, Points, Lines and Planes Jim Van Verth Lars M. Bishop
Advertisements

1 Seminar of computational geometry Lecture #1 Convexity.
Euclidean m-Space & Linear Equations Euclidean m-space.
Vector Calculus Mengxia Zhu Fall Objective Review vector arithmetic Distinguish points and vectors Relate geometric concepts to their algebraic.
Math Foundations of CG Math 1 Hofstra University.
CS 4731: Computer Graphics Lecture 6: Points, Scalars and Vectors Emmanuel Agu.
Introduction to Vectors March 2, What are Vectors? Vectors are pairs of a direction and a magnitude. We usually represent a vector with an arrow:
1 Angel: Interactive Computer Graphics 4E © Addison-Wesley 2005 Geometry Ed Angel Professor of Computer Science, Electrical and Computer Engineering, and.
1 Angel: Interactive Computer Graphics 4E © Addison-Wesley 2005 Representation Ed Angel Professor of Computer Science, Electrical and Computer Engineering,
Math Foundations of CG Math 1 Hofstra University.
Transformations. 2 Angel: Interactive Computer Graphics 3E © Addison-Wesley 2002 Coordinate-Free Geometry When we learned simple geometry, most of us.
Representation CS4395: Computer Graphics 1 Mohan Sridharan Based on slides created by Edward Angel.
Lecture 2: Geometry vs Linear Algebra Points-Vectors and Distance-Norm Shang-Hua Teng.
Vectors. We will start with a basic review of vectors.
Chapter 3: VECTORS 3-2 Vectors and Scalars 3-2 Vectors and Scalars
Geometric Objects and Transformations Geometric Entities Representation vs. Reference System Geometric ADT (Abstract Data Types)
The Cross Product of 2 Vectors 11.3 JMerrill, 2010.
APPLICATIONS OF TRIGONOMETRY
Multiplication with Vectors
6.4 Vectors and Dot Products The Definition of the Dot Product of Two Vectors The dot product of u = and v = is Ex.’s Find each dot product.
Chapter 9-Vectors Calculus, 2ed, by Blank & Krantz, Copyright 2011 by John Wiley & Sons, Inc, All Rights Reserved.
Copyright © Cengage Learning. All rights reserved.
Graphics CSE 581 – Interactive Computer Graphics Mathematics for Computer Graphics CSE 581 – Roger Crawfis (slides developed from Korea University slides)
Math Primer for CG Ref: Interactive Computer Graphics, Chap. 4, E. Angel.
Geometry CSC 2141 Introduction to Computer Graphics.
1 Geometry. 2 Objectives Introduce the elements of geometry ­Scalars ­Vectors ­Points Develop mathematical operations among them in a coordinate-free.
Intro to 3D Models Angel Angel: Interactive Computer Graphics5E © Addison-Wesley
1 Math Review Coordinate systems 2-D, 3-D Vectors Matrices Matrix operations.
Mathematics for Graphics. 1 Objectives Introduce the elements of geometry  Scalars  Vectors  Points Develop mathematical operations among them in a.
Section 10.2a VECTORS IN THE PLANE. Vectors in the Plane Some quantities only have magnitude, and are called scalars … Examples? Some quantities have.
©College of Computer and Information Science, Northeastern University CS 4300 Computer Graphics Prof. Harriet Fell Fall 2012 Lecture 12 – October 1, 2012.
Vectors Day 2. Scalar Multiplication A vector can be multiplied by a real number Multiplying a vector by a positive number changes its size, but not its.
C O M P U T E R G R A P H I C S Guoying Zhao 1 / 52 C O M P U T E R G R A P H I C S Guoying Zhao 1 / 52 Computer Graphics Three-Dimensional Graphics I.
Background Mathematics Aaron Bloomfield CS 445: Introduction to Graphics Fall 2006.
VECTORS (Ch. 12) Vectors in the plane Definition: A vector v in the Cartesian plane is an ordered pair of real numbers:  a,b . We write v =  a,b  and.
Vector Tools for Computer Graphics
1 Graphics CSCI 343, Fall 2015 Lecture 10 Coordinate Transformations.
Chun-Yuan Lin Mathematics for Computer Graphics 2015/12/15 1 CG.
Basic Entities Scalars - real numbers sizes/lengths/angles Vectors - typically 2D, 3D, 4D directions Points - typically 2D, 3D, 4D locations Basic Geometry.
DOT PRODUCT CROSS PRODUCT APPLICATIONS
Affine Geometry Jehee Lee Seoul National University.
Computer Graphics, KKU. Lecture 41 The Computer Programming Laws Any given program, when running, is obsolete. Any given program costs more and.
University of British Columbia CPSC 314 Computer Graphics Jan-Apr 2016 Tamara Munzner Math Basics Week 1, Fri.
1 Introduction to Computer Graphics with WebGL Ed Angel Professor Emeritus of Computer Science Founding Director, Arts, Research, Technology and Science.
CSC461: Lecture 13 Coordinates Objectives Introduce concepts such as dimension and basis Introduce concepts such as dimension and basis Introduce coordinate.
1 Representation. 2 Objectives Introduce concepts such as dimension and basis Introduce coordinate systems for representing vectors spaces and frames.
Copyright © Cengage Learning. All rights reserved. 6.3 Vectors in the Plane.
Vectors and Dot Product 6.4 JMerrill, Quick Review of Vectors: Definitions Vectors are quantities that are described by direction and magnitude.
Computer Graphics I, Fall 2010 Geometry.
1 Introduction to Computer Graphics with WebGL Ed Angel Professor Emeritus of Computer Science Founding Director, Arts, Research, Technology and Science.
Chapter 4 Vector Spaces Linear Algebra. Ch04_2 Definition 1: ……………………………………………………………………. The elements in R n called …………. 4.1 The vector Space R n Addition.
1 Introduction to Computer Graphics with WebGL Ed Angel Professor Emeritus of Computer Science Founding Director, Arts, Research, Technology and Science.
Graphics Graphics Korea University kucg.korea.ac.kr Geometric Primitives 고려대학교 컴퓨터 그래픽스 연구실.
Graphics Graphics Korea University kucg.korea.ac.kr Mathematics for Computer Graphics 고려대학교 컴퓨터 그래픽스 연구실.
Spaces.
Mathematics for Computer Graphics
Scalars and Vectors.
More Vector Basics.
CSC461: Lecture 12 Geometry Objectives
VECTORS.
Lecture 03: Linear Algebra
Scalars Some quantities, like temperature, distance, height, area, and volume, can be represented by a ________________ that indicates __________________,
Introduction to Computer Graphics with WebGL
VECTORS Dr. Shildneck.
VECTORS Dr. Shildneck.
Lecture 2: Geometry vs Linear Algebra Points-Vectors and Distance-Norm
PPT&Programs
Vectors and Dot Products
Rayat Shikshan Sanstha’s S.M.Joshi College, Hadapsar -28
Presentation transcript:

1 Graphics CSCI 343, Fall 2015 Lecture 9 Geometric Objects

2 Geometric Objects 1.A vector space contains vectors and scalars. A vector has direction and magnitude (but not position). Vectors are denoted by u, v, w (lower case). A scalar is a real number. Scalars are denoted by  2.An affine space is an extension of the vector space to include points (positions in space). Points are denoted by P, Q, R (upper case) 3. A Euclidean space extends the linear vector space to add a measure of size or distance.

3 Combining entities in affine space 1.Vector-scalar multiplication |  v| = |  | |v|, where |v| is the magnitude of v The direction of  v is the same as the direction of v v2v? 2.Point-vector addition Adding a vector to a point results in another point. Q P Q + v = P v v = P - Q -0.5v?

4 Vector addition u = (x 1, y 1 )(x 1 is horizontal component of u, y 1 is vertical component of u.) v = (x 2, y 2 ) u+v = (x 1 +x 2, y 1 +y 2 ) u v Place the tail of v at the head of u. Draw a vector from the tail of u to the head of v.

5 A parametric line P(  ) = P 0 +  v P0P0 P(  ) v A line in space: Affine sums: P(  ) = Q +  v defines a line. From this we show that: P =  1 R +  2 Q where  1 +  2 = 1 If 0 <=  <= 1, then all P lie on the line between Q and R. Q R  = 0  = 1

6 Affine sums for more points The affine sum for three points: P =  1 P 1 +  2 P 2 +  3 P 3, where  1 +  2 +  3 = 1,  i >=0 defines all points inside triangle P 1 P 2 P 3. P1P1 P2P2 P3P3 P =  1 P 1 +  2 P  n P n, where  1 +   n = 1,  i >=0 defines all points inside convex hull around P 1 P 2... P n. The affine sum for n points: A convex hull is like shrink-wrap around all n points.

7 The dot product The dot product (or inner product) of two vectors is defined as follows: where  represents the angle between the two vectors. u v  Projection, w, of u onto v: w w = ?

8 The cross product The cross product of two linearly independent (non-parallel) vectors is a third vector that is orthogonal to both of them. u v n The direction of n is defined by the right handed coordinate system.

9 A parametric plane A plane in affine space can be defined in terms of two linearly independent vectors as follows: uu vv P0P0 uu vv P

10 Defining a coordinate system Any 3D vector, w, can be defined in terms of 3 linearly independent vectors, v 1, v 2, v 3 : v1v1 v2v2 v3v3 w  1,  2 and  3 are components of w with respect to the basis (v 1, v 2, v 3 ).

11 Matrix representation of vectors We can represent w as a column matrix:

12 Adding a point of reference Because vectors have no position, we must add a reference point to specify a coordinate frame. Choose P 0 as reference. Vectors can be written as: Points can be written as: v1v1 v2v2 v3v3 P P0P0