Lecture 8 Wrap-up Part1, Matlab

Similar documents
CS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 2

Fundamentals of Linear Algebra

11-755/ Machine Learning for Signal Processing. Algebra. Class August Instructor: Bhiksha Raj

Operations with Matrices

INTRODUCTION TO LINEAR ALGEBRA

Uses of transformations. 3D transformations. Review of vectors. Vectors in 3D. Points vs. vectors. Homogeneous coordinates S S [ H [ S \ H \ S ] H ]

LINEAR ALGEBRA APPLIED

1 Linear Least Squares

The Algebra (al-jabr) of Matrices

Numerical Linear Algebra Assignment 008

Matrix Eigenvalues and Eigenvectors September 13, 2017

Chapter 5 Determinants

ES.182A Topic 32 Notes Jeremy Orloff

Chapter 2. Vectors. 2.1 Vectors Scalars and Vectors

Lecture Solution of a System of Linear Equation

Chapter 3 MATRIX. In this chapter: 3.1 MATRIX NOTATION AND TERMINOLOGY

Differential Geometry: Conformal Maps

Algebra Of Matrices & Determinants

11-755/ Machine Learning for Signal Processing. Algebra. Class Sep Instructor: Bhiksha Raj

Matrices and Determinants

Fundamentals of Linear Algebra

M344 - ADVANCED ENGINEERING MATHEMATICS

Math 520 Final Exam Topic Outline Sections 1 3 (Xiao/Dumas/Liaw) Spring 2008

Lecture Note 9: Orthogonal Reduction

MATRICES AND VECTORS SPACE

GG303 Lab 6 9/25/12. Components of cross product v2 x v1 N x N y N z. N=v2xv1. Plane trend ( ) Pole N. Plane. Pole N. plunge ( ) strike ( ) dip ( )

A Matrix Algebra Primer

THE DISCRIMINANT & ITS APPLICATIONS

MATRIX DEFINITION A matrix is any doubly subscripted array of elements arranged in rows and columns.

Things to Memorize: A Partial List. January 27, 2017

Module 6: LINEAR TRANSFORMATIONS

ROTATION IN 3D WORLD RIGID BODY MOTION

Introduction to Algebra - Part 2

DEFINITION OF ASSOCIATIVE OR DIRECT PRODUCT AND ROTATION OF VECTORS

Introduction To Matrices MCV 4UI Assignment #1

Chapter 3. Vector Spaces

September 13 Homework Solutions

Visual motion. Many slides adapted from S. Seitz, R. Szeliski, M. Pollefeys

Engineering Analysis ENG 3420 Fall Dan C. Marinescu Office: HEC 439 B Office hours: Tu-Th 11:00-12:00

A - INTRODUCTION AND OVERVIEW

Functions and transformations

Counting intersections of spirals on a torus

MATH34032: Green s Functions, Integral Equations and the Calculus of Variations 1

Lecture 4 Single View Metrology

MTH 5102 Linear Algebra Practice Exam 1 - Solutions Feb. 9, 2016

Math 32B Discussion Session Session 7 Notes August 28, 2018

Geometric Sequences. Geometric Sequence a sequence whose consecutive terms have a common ratio.

Global Motion. Estimate motion using all pixels in the image. Parametric flow gives an equation, which describes optical flow for each pixel.

Chapter 1: Logarithmic functions and indices

1 Part II: Numerical Integration

Optimization Lecture 1 Review of Differential Calculus for Functions of Single Variable.

Logarithms. Logarithm is another word for an index or power. POWER. 2 is the power to which the base 10 must be raised to give 100.

5.2 Exponent Properties Involving Quotients

1. Extend QR downwards to meet the x-axis at U(6, 0). y

ODE: Existence and Uniqueness of a Solution

Worksheet : Class XII Matrices & Determinants

SECTION A STUDENT MATERIAL. Part 1. What and Why.?

A matrix is a set of numbers or symbols arranged in a square or rectangular array of m rows and n columns as

Math 270A: Numerical Linear Algebra

Chapter 16. Molecular Symmetry

a a a a a a a a a a a a a a a a a a a a a a a a In this section, we introduce a general formula for computing determinants.

Here we study square linear systems and properties of their coefficient matrices as they relate to the solution set of the linear system.

AQA Further Pure 1. Complex Numbers. Section 1: Introduction to Complex Numbers. The number system

Dynamics: Newton s Laws of Motion

Calculus - Activity 1 Rate of change of a function at a point.

Chapter 2. Determinants

Matrices. Elementary Matrix Theory. Definition of a Matrix. Matrix Elements:

Introduction to Determinants. Remarks. Remarks. The determinant applies in the case of square matrices

Design Synthesis. specified positions called precision points zero error at precision points small error between points - optimization

set is not closed under matrix [ multiplication, ] and does not form a group.

VECTORS, TENSORS, AND MATRICES. 2 + Az. A vector A can be defined by its length A and the direction of a unit

In Section 5.3 we considered initial value problems for the linear second order equation. y.a/ C ˇy 0.a/ D k 1 (13.1.4)

along the vector 5 a) Find the plane s coordinate after 1 hour. b) Find the plane s coordinate after 2 hours. c) Find the plane s coordinate

Chapter 3 Polynomials

Point Lattices: Bravais Lattices

Multivariate problems and matrix algebra

How do you know you have SLE?

Matrix Solution to Linear Equations and Markov Chains

ECON 331 Lecture Notes: Ch 4 and Ch 5

STRAND J: TRANSFORMATIONS, VECTORS and MATRICES

How do we solve these things, especially when they get complicated? How do we know when a system has a solution, and when is it unique?

Lesson 1: Quadratic Equations

Exploring parametric representation with the TI-84 Plus CE graphing calculator

Some Methods in the Calculus of Variations

2A1A Vector Algebra and Calculus I

Surface Integrals of Vector Fields

Contents. Outline. Structured Rank Matrices Lecture 2: The theorem Proofs Examples related to structured ranks References. Structure Transport

Summer Work Packet for MPH Math Classes

In-Class Problems 2 and 3: Projectile Motion Solutions. In-Class Problem 2: Throwing a Stone Down a Hill

UNIT 1 FUNCTIONS AND THEIR INVERSES Lesson 1.4: Logarithmic Functions as Inverses Instruction

Best Approximation in the 2-norm

Matrix Algebra. Matrix Addition, Scalar Multiplication and Transposition. Linear Algebra I 24

Lecture 0. MATH REVIEW for ECE : LINEAR CIRCUIT ANALYSIS II

Before we can begin Ch. 3 on Radicals, we need to be familiar with perfect squares, cubes, etc. Try and do as many as you can without a calculator!!!

III. Vector data. First, create a unit circle which presents the margin of the stereonet. tan. sin. r=1. cos

The final exam will take place on Friday May 11th from 8am 11am in Evans room 60.

Multiple Integrals. Review of Single Integrals. Planar Area. Volume of Solid of Revolution

Computer Graphics (CS 4731) Lecture 7: Linear Algebra for Graphics (Points, Scalars, Vectors)

. Double-angle formulas. Your answer should involve trig functions of θ, and not of 2θ. sin 2 (θ) =

SCHOOL OF ENGINEERING & BUILT ENVIRONMENT. Mathematics. Basic Algebra

Transcription:

Lecture 8 Wrp-up Prt1, Mtlb

Homework Polic Plese stple our homework (ou will lose 10% credit if not stpled or secured) Submit ll problems in order. This mens to plce ever item relting to problem 3 (our writeup, code, figures, etc.) before nthing relting to problem 4. Preferred order would be writeup, then figures (if seprte), nd finll code, but we won't be pick bout this. The sme philosoph pplies to multi-prt questions. Submit ll prts of 3() before n prts of 3(b). (We relize there will be situtions is which, for emple, ou write code which is used in severl problems. In this cse, plce the code in the section of the first problem in which it is used.)

Spots nd Oriented Brs (Mlik nd Peron)

Gbor Filters Gbor filters t different scles nd sptil frequencies top row shows nti-smmetric (or odd) filters, bottom row the smmetric (or even) filters. cos( k 2 + + k )ep 2σ 2 2

Gbor filters re emples of Wvelets We know two bses for imges: Piels re loclized in spce. Fourier re loclized in frequenc. Wvelets re little of both. Good for mesuring frequenc locll.

Snthesis with this Representtion (Bergen nd Heeger)

Mrkov Model Cptures locl dependencies. Ech piel depends on neighborhood. Emple, 1D first order model P(p1, p2, pn) = P(p1)*P(p2 p1)*p(p3 p2,p1)* = P(p1)*P(p2 p1)*p(p3 p2)*p(p4 p3)*

Emple 1 st Order Mrkov Model Ech piel is like neighbor to left + noise with some probbilit. Mtlb These cpture much wider rnge of phenomen.

Edge There re dependencies in Filter Outputs Filter responds t one scle, often does t other scles. Filter responds t one orienttion, often doesn t t orthogonl orienttion. Snthesis using wvelets nd Mrkov model for dependencies: DeBonet nd Viol Portill nd Simoncelli

We cn do this without filters Ech piel depends on neighbors. 1. As ou snthesize, look t neighbors. 2. Look for similr neighborhood in smple teture. 3. Cop piel from tht neighborhood. 4. Continue.

This is like coping, but not just repetition Photo Pttern Repeted

With Blocks

Mtlb tutoril nd Liner Algebr Review Tod s gols: Lern enough mtlb to get strted. Review some bsics of Liner Algebr Essentil for geometr of points nd lines. But lso, ll mth is liner lgebr. (ok slight eggertion however most computtion is) Mn slides tod dpted from Octvi Cmps, Penn Stte.

Strting Mtlb For PCs, Mtlb should be progrm. For Sun s: Numericl Anlsis nd Visuliztion Mtlb 6.1

Help help helpcommnd Eg., help plus Help on toolbr demo Tutoril: http://mth.colordo.edu/scico/tutorils/mtlb/

Mtlb interpreter Mn common functions: see help ops

Ordered set of numbers: (1,2,3,4) Emple: (,,z) coordintes of pt in spce. Vectors v = v = ( 1, n i= 1, K, 2 If v = 1, v is i 2 n ) unit vector

Indeing into vectors

Vector Addition v + w = ( 1, 2) + ( 1, 2) = ( 1 + 1, 2 + 2) v V+w w

Product of sclr nd vector v = ( 1, 2) = ( 1, 2) v v

Opertions on vectors sum m, min, men, sort, Pointwise:.^

Inner (dot) Product v α w v. w = ( + 1, 2).( 1, 2) = 1 1 2. 2 The inner product is SCALAR! v. w = 2 ( 1, 2).( 1, ) = v w cosα v. w = 0 v w

Mtrices A n m = M n 11 21 31 1 12 22 32 M n2 L L L O L 1m 2m 3m M nm Sum: C n m = An m + Bn m c = + ij ij A nd B must hve the sme dimensions b ij

Mtrices Product: C n p = An mbm p m c ij = k = 1 ik b kj A nd B must hve comptible dimensions A n nbn n Bn n An n Identit Mtri: 1 0 O 0 0 1 O 0 I = IA = AI = O O O O 0 0 O 1 A

Mtrices Trnspose: T C m n = A n m c = ij ji ( T T A + B) = A + ( T T AB ) = B A T B T If A T = A A is smmetric

Mtrices Determinnt: Determinnt: A must be squre A must be squre 32 31 22 21 13 33 31 23 21 12 33 32 23 22 11 33 32 31 23 22 21 13 12 11 det + = 12 21 22 11 22 21 12 11 22 21 12 11 det = =

Mtrices Inverse: A must be squre 1 1 An n A n n = A n n An n = I 1 11 12 1 21 22 = 11 22 21 12 22 21 11 12

Indeing into mtrices

Eucliden trnsformtions

2D Trnsltion P t P

2D Trnsltion Eqution t P t P P = (, ) t = ( t, t ) t P ' = ( + t, + t ) = P+ t

2D Trnsltion using Mtrices P t t P t ), ( ), ( t t = = t P = + + 1 1 0 0 1 ' t t t t P t P

Scling P P

Scling Eqution P s. s. P s. s. ), ( ' ), ( s s = = P P P P '= s = s s s s 0 0 P' S P S P = '

Rottion P P

Rottion Equtions Counter-clockwise rottion b n ngle θ Y P X θ P ' ' cosθ = sinθ P'= R.P sinθ cosθ

Degrees of Freedom ' ' = R is 22 cosθ sinθ sinθ cosθ 4 elements BUT! There is onl 1 degree of freedom: θ The 4 elements must stisf the following constrints: R R T = R T R = I det( R) = 1

Stretching Eqution P S.. P S.. = s s s s 0 0 P' ), ( ' ), ( s s = = P P S P S P = '

Stretching = tilting nd projecting (with wek perspective) = = s s s s s s s 1 0 0 0 0 P'

Liner Trnsformtion = = s s s s s d c b ϕ ϕ ϕ ϕ θ θ θ θ ϕ ϕ ϕ ϕ θ θ θ θ sin cos cos sin 1 0 0 sin cos cos sin sin cos cos sin 0 0 sin cos cos sin P' SVD

Affine Trnsformtion P' c b d t t 1

Files

Functions Formt: function o = test(,) Nme function nd file the sme. Onl first function in file is visible outside the file.

Imges

Debugging Add print sttements to function b leving off ; kebord debug nd brekpoint

Conclusions Quick tour of mtlb, ou should tech ourself the rest. We ll give hints in problem sets. Liner lgebr llows geometric mnipultion of points. Lern to love SVD.