Curve fitting using linear models
|
|
- Rose Edwards
- 6 years ago
- Views:
Transcription
1 Curve fitting using linear models Rasmus Waagepetersen Department of Mathematics Aalborg University Denmark September 28, / 12
2 Outline for today linear models and basis functions polynomial regression and overfitting piecewise linear functions 2 / 12
3 Basis functions representations of unknown function Suppose we are given measurements (x i, y i ) where y i are observations of Y i with EY i = f (x i ) for some unknown function f. Idea: represent f ( ) as a linear combination of specified basis functions p 1 f (x) = β i B i (x) i=0 Example (linear regression): p = 2, B 0 (x) = 1, B 1 (x) = x. Polynomial regression: B i (x) = x i, i = 0,..., p 1 Trigonometric polynomials/discrete Fourier transform: B l1 (x) = cos(2πlx), B l2 (x) = sin(2πlx) x [0, 1] (or B l (x) = exp(i2πlx) with complex coefficients). 3 / 12
4 Overfitting Suppose we are given observations (x i, y i ) i = 1,..., n. Then we can always find a nth order polynomial ˆf (x) that fits exactly these observations - i.e. y i ˆf (x i ) = 0 for all i (Note: if design matrix n n and full rank then L = R n and P = I ). However, typically such a high order polynomial fits actual data too well - it fits not only f but also the noise. This means fitted ˆf bad for prediction of new observations. Another problem: polynomials global - if just one (x i, y i ) is changed this affects the whole fitted polynomial. 4 / 12
5 Piecewise linear function A first approximation of f might be a linear regression f (x) = a + bx but this is often too crude. A next step might be a piecewise linear function f f (x) = a l + b l x, x [c l, c l+1 [ for some cut -points or knots c l, l = 1,..., p. However, we typically want f to be continuous! This is ensured if we require a l + b l c l+1 = a l+1 + b l+1 c l+1. 5 / 12
6 A continous piece-wise linear curve from c 1 to c p is obtained with the following parametrization: β 0 + β 1 x x [c 1, c 2 ] f (c 2 ) + β 2 (x c 2 ) x ]c 2, c 3 ] f (x) = f (c 3 ) + β 3 (x c 3 ) x ]c 3, c 4 ] etc. This still defines a linear model! Basis functions: B 0 (x) = 1, B 1 (x), B 2 (x) = 1(x ]c 2, c 3 ])(x c 2 ) + 1[x > c 3 ](c 3 c 2 ),... 6 / 12
7 Alternative basis functions for piecewise linear functions Cut-points c 0, c 1,..., c p 1, c p. For i = 1,..., p: x c i 1 c i c i 1 x [c i 1, c i [ B i (x) = 1 x c i c i+1 c i x [c i, c i+1 [ 0 otherwise Note: f (x) = p 1 i=0 β ib i (x) piecewise linear and continuous. Hence we obtain exactly same set of functions as with basis on previous slide! Note: new set of basis functions local - only non-zero on intervals [c i 1, c i+1 [. Thereby more sparse X T X matrix. Disadvantage: f above is not smooth at cutpoints. 7 / 12
8 B-spline basis functions Consider doubly infinite sequence of equi-distant cut points..., c 1, c 0, c 1, c 2,... with (wlog) c i+1 c i = 1. Define B i (x) = B(x c i ) where 1 6 (x + 2)3 x [ 2, 1[ 1 6 (1 + 3(x + 1) + 3(x + 1)2 3(x + 1) 3 ) x [ 1, 0[ B(x) = 1 6 (4 6x 2 + 3x 3 ) x [0, 1[ 1 6 (1 3(x 1) + 3(x 1)2 (u 1) 3 )) x [1, 2[ 0 otherwise B(x) is a cubic spline: composed of the constant function g(x) = 0 and 4 third-order polynomials such that it is everywhere continuous and twice-differentiable. 8 / 12
9 The B-spline basis function: B(x, 0, 1) x 9 / 12
10 Cubic spline f (x) = f i (x) = a i0 +a i1 (x c i )+a i2 (x c i ) 2 +a i3 (x c i ) 3 x [c i, c i+1 [ Require continuity and twice differentiability: f i (c i+1 ) = f i+1 (c i+1 ) f i (c i+1 ) = f i+1(c i+1 ) f i (c i+1 ) = f i+1(c i+1 ) Again possible to compute basis functions and fit model in R. R Function bs() can be used to generate required basis functions for linear model. Suppose we use cut-points/knots c 1,..., c q and the q 1 associated cubic polynomials. Then we have p = (q 1) 4 3 (q 2) = q + 2 free parameters. 10 / 12
11 Equivalence of bases for cubic splines Fitting a cubic spline with knots 0, 1,..., q 1 (starting at c 1 and ending at c q ) is equivalent to fitting the linear model based on the B-spline basis functions B(x i), i = 1,..., q. Note: same number of free parameters. Intuitively makes sense, since both models generate continous piecewise cubic splines with continuous first and second derivatives. 11 / 12
12 Exercises 1. Write down the design matrix for a piece-wise linear regression model with cut-points c 1 and c 2 (i.e. the curve is composed of three segments). 2. Implement in R the above piece-wise model for your wind/power data. Try both types of basis functions. 3. Write down the equations for a cubic spline with knots 0, 1, 2 starting at 0 and ending at 2. Write down the associated design matrix. Do the same using the B-spline basis functions B(x i), i = 1, 0, 1, 2, 3. Compare the two design matrices. 4. Fit a cubic spline to your wind/power data (use R-function bs()). 12 / 12
Consider functions such that then satisfies these properties: So is represented by the cubic polynomials on on and on.
1 of 9 3/1/2006 2:28 PM ne previo Next: Trigonometric Interpolation Up: Spline Interpolation Previous: Piecewise Linear Case Cubic Splines A piece-wise technique which is very popular. Recall the philosophy
More informationComputational Physics PHYS 420
Computational Physics PHYS 420 Dr Richard H. Cyburt Assistant Professor of Physics My office: 402c in the Science Building My phone: (304) 384-6006 My email: rcyburt@concord.edu My webpage: www.concord.edu/rcyburt
More informationInteractive Graphics. Lecture 9: Introduction to Spline Curves. Interactive Graphics Lecture 9: Slide 1
Interactive Graphics Lecture 9: Introduction to Spline Curves Interactive Graphics Lecture 9: Slide 1 Interactive Graphics Lecture 13: Slide 2 Splines The word spline comes from the ship building trade
More informationA MATRIX FORMULATION OF THE CUBIC BÉZIER CURVE
Geometric Modeling Notes A MATRIX FORMULATION OF THE CUBIC BÉZIER CURVE Kenneth I. Joy Institute for Data Analysis and Visualization Department of Computer Science University of California, Davis Overview
More informationFebruary 2017 (1/20) 2 Piecewise Polynomial Interpolation 2.2 (Natural) Cubic Splines. MA378/531 Numerical Analysis II ( NA2 )
f f f f f (/2).9.8.7.6.5.4.3.2. S Knots.7.6.5.4.3.2. 5 5.2.8.6.4.2 S Knots.2 5 5.9.8.7.6.5.4.3.2..9.8.7.6.5.4.3.2. S Knots 5 5 S Knots 5 5 5 5.35.3.25.2.5..5 5 5.6.5.4.3.2. 5 5 4 x 3 3.5 3 2.5 2.5.5 5
More informationNatural Quartic Spline
Natural Quartic Spline Rafael E Banchs INTRODUCTION This report describes the natural quartic spline algorithm developed for the enhanced solution of the Time Harmonic Field Electric Logging problem As
More informationMultiple-Choice Test Spline Method Interpolation COMPLETE SOLUTION SET
Multiple-Choice Test Spline Method Interpolation COMPLETE SOLUTION SET 1. The ollowing n data points, ( x ), ( x ),.. ( x, ) 1, y 1, y n y n quadratic spline interpolation the x-data needs to be (A) equally
More informationNonparametric regression using kernel and spline methods
Nonparametric regression using kernel and spline methods Jean D. Opsomer F. Jay Breidt March 3, 016 1 The statistical model When applying nonparametric regression methods, the researcher is interested
More informationRational Bezier Surface
Rational Bezier Surface The perspective projection of a 4-dimensional polynomial Bezier surface, S w n ( u, v) B i n i 0 m j 0, u ( ) B j m, v ( ) P w ij ME525x NURBS Curve and Surface Modeling Page 97
More informationVideo 11.1 Vijay Kumar. Property of University of Pennsylvania, Vijay Kumar
Video 11.1 Vijay Kumar 1 Smooth three dimensional trajectories START INT. POSITION INT. POSITION GOAL Applications Trajectory generation in robotics Planning trajectories for quad rotors 2 Motion Planning
More informationSee the course website for important information about collaboration and late policies, as well as where and when to turn in assignments.
COS Homework # Due Tuesday, February rd See the course website for important information about collaboration and late policies, as well as where and when to turn in assignments. Data files The questions
More informationInterpolation by Spline Functions
Interpolation by Spline Functions Com S 477/577 Sep 0 007 High-degree polynomials tend to have large oscillations which are not the characteristics of the original data. To yield smooth interpolating curves
More informationSung-Eui Yoon ( 윤성의 )
CS480: Computer Graphics Curves and Surfaces Sung-Eui Yoon ( 윤성의 ) Course URL: http://jupiter.kaist.ac.kr/~sungeui/cg Today s Topics Surface representations Smooth curves Subdivision 2 Smooth Curves and
More informationME 261: Numerical Analysis Lecture-12: Numerical Interpolation
1 ME 261: Numerical Analysis Lecture-12: Numerical Interpolation Md. Tanver Hossain Department of Mechanical Engineering, BUET http://tantusher.buet.ac.bd 2 Inverse Interpolation Problem : Given a table
More informationImportant Properties of B-spline Basis Functions
Important Properties of B-spline Basis Functions P2.1 N i,p (u) = 0 if u is outside the interval [u i, u i+p+1 ) (local support property). For example, note that N 1,3 is a combination of N 1,0, N 2,0,
More informationCS 450 Numerical Analysis. Chapter 7: Interpolation
Lecture slides based on the textbook Scientific Computing: An Introductory Survey by Michael T. Heath, copyright c 2018 by the Society for Industrial and Applied Mathematics. http://www.siam.org/books/cl80
More informationAn introduction to interpolation and splines
An introduction to interpolation and splines Kenneth H. Carpenter, EECE KSU November 22, 1999 revised November 20, 2001, April 24, 2002, April 14, 2004 1 Introduction Suppose one wishes to draw a curve
More informationLecture 16: High-dimensional regression, non-linear regression
Lecture 16: High-dimensional regression, non-linear regression Reading: Sections 6.4, 7.1 STATS 202: Data mining and analysis November 3, 2017 1 / 17 High-dimensional regression Most of the methods we
More informationKnowledge Discovery and Data Mining
Knowledge Discovery and Data Mining Basis Functions Tom Kelsey School of Computer Science University of St Andrews http://www.cs.st-andrews.ac.uk/~tom/ tom@cs.st-andrews.ac.uk Tom Kelsey ID5059-02-BF 2015-02-04
More informationlecture 10: B-Splines
9 lecture : -Splines -Splines: a basis for splines Throughout our discussion of standard polynomial interpolation, we viewed P n as a linear space of dimension n +, and then expressed the unique interpolating
More informationA popular method for moving beyond linearity. 2. Basis expansion and regularization 1. Examples of transformations. Piecewise-polynomials and splines
A popular method for moving beyond linearity 2. Basis expansion and regularization 1 Idea: Augment the vector inputs x with additional variables which are transformation of x use linear models in this
More information08 - Designing Approximating Curves
08 - Designing Approximating Curves Acknowledgement: Olga Sorkine-Hornung, Alexander Sorkine-Hornung, Ilya Baran Last time Interpolating curves Monomials Lagrange Hermite Different control types Polynomials
More information99 International Journal of Engineering, Science and Mathematics
Journal Homepage: Applications of cubic splines in the numerical solution of polynomials Najmuddin Ahmad 1 and Khan Farah Deeba 2 Department of Mathematics Integral University Lucknow Abstract: In this
More informationRepresenting Curves Part II. Foley & Van Dam, Chapter 11
Representing Curves Part II Foley & Van Dam, Chapter 11 Representing Curves Polynomial Splines Bezier Curves Cardinal Splines Uniform, non rational B-Splines Drawing Curves Applications of Bezier splines
More informationInterpolation and Splines
Interpolation and Splines Anna Gryboś October 23, 27 1 Problem setting Many of physical phenomenona are described by the functions that we don t know exactly. Often we can calculate or measure the values
More informationObjects 2: Curves & Splines Christian Miller CS Fall 2011
Objects 2: Curves & Splines Christian Miller CS 354 - Fall 2011 Parametric curves Curves that are defined by an equation and a parameter t Usually t [0, 1], and curve is finite Can be discretized at arbitrary
More informationInterpolation - 2D mapping Tutorial 1: triangulation
Tutorial 1: triangulation Measurements (Zk) at irregular points (xk, yk) Ex: CTD stations, mooring, etc... The known Data How to compute some values on the regular spaced grid points (+)? The unknown data
More informationMoving Beyond Linearity
Moving Beyond Linearity The truth is never linear! 1/23 Moving Beyond Linearity The truth is never linear! r almost never! 1/23 Moving Beyond Linearity The truth is never linear! r almost never! But often
More informationDoubly Cyclic Smoothing Splines and Analysis of Seasonal Daily Pattern of CO2 Concentration in Antarctica
Boston-Keio Workshop 2016. Doubly Cyclic Smoothing Splines and Analysis of Seasonal Daily Pattern of CO2 Concentration in Antarctica... Mihoko Minami Keio University, Japan August 15, 2016 Joint work with
More informationFour equations are necessary to evaluate these coefficients. Eqn
1.2 Splines 11 A spline function is a piecewise defined function with certain smoothness conditions [Cheney]. A wide variety of functions is potentially possible; polynomial functions are almost exclusively
More information1D Regression. i.i.d. with mean 0. Univariate Linear Regression: fit by least squares. Minimize: to get. The set of all possible functions is...
1D Regression i.i.d. with mean 0. Univariate Linear Regression: fit by least squares. Minimize: to get. The set of all possible functions is... 1 Non-linear problems What if the underlying function is
More informationPolynomials tend to oscillate (wiggle) a lot, even when our true function does not.
AMSC/CMSC 460 Computational Methods, Fall 2007 UNIT 2: Spline Approximations Dianne P O Leary c 2001, 2002, 2007 Piecewise polynomial interpolation Piecewise polynomial interpolation Read: Chapter 3 Skip:
More informationAssessing the Quality of the Natural Cubic Spline Approximation
Assessing the Quality of the Natural Cubic Spline Approximation AHMET SEZER ANADOLU UNIVERSITY Department of Statisticss Yunus Emre Kampusu Eskisehir TURKEY ahsst12@yahoo.com Abstract: In large samples,
More informationComputer Graphics / Animation
Computer Graphics / Animation Artificial object represented by the number of points in space and time (for moving, animated objects). Essential point: How do you interpolate these points in space and time?
More informationLecture VIII. Global Approximation Methods: I
Lecture VIII Global Approximation Methods: I Gianluca Violante New York University Quantitative Macroeconomics G. Violante, Global Methods p. 1 /29 Global function approximation Global methods: function
More informationGeometric Modeling of Curves
Curves Locus of a point moving with one degree of freedom Locus of a one-dimensional parameter family of point Mathematically defined using: Explicit equations Implicit equations Parametric equations (Hermite,
More information(Spline, Bezier, B-Spline)
(Spline, Bezier, B-Spline) Spline Drafting terminology Spline is a flexible strip that is easily flexed to pass through a series of design points (control points) to produce a smooth curve. Spline curve
More information3D Modeling Parametric Curves & Surfaces
3D Modeling Parametric Curves & Surfaces Shandong University Spring 2012 3D Object Representations Raw data Point cloud Range image Polygon soup Solids Voxels BSP tree CSG Sweep Surfaces Mesh Subdivision
More informationUntil now we have worked with flat entities such as lines and flat polygons. Fit well with graphics hardware Mathematically simple
Curves and surfaces Escaping Flatland Until now we have worked with flat entities such as lines and flat polygons Fit well with graphics hardware Mathematically simple But the world is not composed of
More informationMar. 20 Math 2335 sec 001 Spring 2014
Mar. 20 Math 2335 sec 001 Spring 2014 Chebyshev Polynomials Definition: For an integer n 0 define the function ( ) T n (x) = cos n cos 1 (x), 1 x 1. It can be shown that T n is a polynomial of degree n.
More informationCubic spline interpolation
Cubic spline interpolation In the following, we want to derive the collocation matrix for cubic spline interpolation. Let us assume that we have equidistant knots. To fulfill the Schoenberg-Whitney condition
More information3D Modeling Parametric Curves & Surfaces. Shandong University Spring 2013
3D Modeling Parametric Curves & Surfaces Shandong University Spring 2013 3D Object Representations Raw data Point cloud Range image Polygon soup Surfaces Mesh Subdivision Parametric Implicit Solids Voxels
More informationLast time... Bias-Variance decomposition. This week
Machine learning, pattern recognition and statistical data modelling Lecture 4. Going nonlinear: basis expansions and splines Last time... Coryn Bailer-Jones linear regression methods for high dimensional
More informationCurves and Surfaces 1
Curves and Surfaces 1 Representation of Curves & Surfaces Polygon Meshes Parametric Cubic Curves Parametric Bi-Cubic Surfaces Quadric Surfaces Specialized Modeling Techniques 2 The Teapot 3 Representing
More informationCurve and Surface Fitting with Splines. PAUL DIERCKX Professor, Computer Science Department, Katholieke Universiteit Leuven, Belgium
Curve and Surface Fitting with Splines PAUL DIERCKX Professor, Computer Science Department, Katholieke Universiteit Leuven, Belgium CLARENDON PRESS OXFORD 1995 - Preface List of Figures List of Tables
More informationOUTLINE. Quadratic Bezier Curves Cubic Bezier Curves
BEZIER CURVES 1 OUTLINE Introduce types of curves and surfaces Introduce the types of curves Interpolating Hermite Bezier B-spline Quadratic Bezier Curves Cubic Bezier Curves 2 ESCAPING FLATLAND Until
More informationEECS 556 Image Processing W 09. Interpolation. Interpolation techniques B splines
EECS 556 Image Processing W 09 Interpolation Interpolation techniques B splines What is image processing? Image processing is the application of 2D signal processing methods to images Image representation
More informationSplines and penalized regression
Splines and penalized regression November 23 Introduction We are discussing ways to estimate the regression function f, where E(y x) = f(x) One approach is of course to assume that f has a certain shape,
More informationHandout 4 - Interpolation Examples
Handout 4 - Interpolation Examples Middle East Technical University Example 1: Obtaining the n th Degree Newton s Interpolating Polynomial Passing through (n+1) Data Points Obtain the 4 th degree Newton
More informationRemark. Jacobs University Visualization and Computer Graphics Lab : ESM4A - Numerical Methods 331
Remark Reconsidering the motivating example, we observe that the derivatives are typically not given by the problem specification. However, they can be estimated in a pre-processing step. A good estimate
More informationFall CSCI 420: Computer Graphics. 4.2 Splines. Hao Li.
Fall 2014 CSCI 420: Computer Graphics 4.2 Splines Hao Li http://cs420.hao-li.com 1 Roller coaster Next programming assignment involves creating a 3D roller coaster animation We must model the 3D curve
More informationSpline Models. Introduction to CS and NCS. Regression splines. Smoothing splines
Spline Models Introduction to CS and NCS Regression splines Smoothing splines 3 Cubic Splines a knots: a< 1 < 2 < < m
More informationSplines. Patrick Breheny. November 20. Introduction Regression splines (parametric) Smoothing splines (nonparametric)
Splines Patrick Breheny November 20 Patrick Breheny STA 621: Nonparametric Statistics 1/46 Introduction Introduction Problems with polynomial bases We are discussing ways to estimate the regression function
More informationA Curve Tutorial for Introductory Computer Graphics
A Curve Tutorial for Introductory Computer Graphics Michael Gleicher Department of Computer Sciences University of Wisconsin, Madison October 7, 2003 Note to 559 Students: These notes were put together
More informationNumerical Methods in Physics Lecture 2 Interpolation
Numerical Methods in Physics Pat Scott Department of Physics, Imperial College November 8, 2016 Slides available from http://astro.ic.ac.uk/pscott/ course-webpage-numerical-methods-201617 Outline The problem
More informationNonparametric Approaches to Regression
Nonparametric Approaches to Regression In traditional nonparametric regression, we assume very little about the functional form of the mean response function. In particular, we assume the model where m(xi)
More informationScientific Computing: Interpolation
Scientific Computing: Interpolation Aleksandar Donev Courant Institute, NYU donev@courant.nyu.edu Course MATH-GA.243 or CSCI-GA.22, Fall 25 October 22nd, 25 A. Donev (Courant Institute) Lecture VIII /22/25
More informationLecture 8. Divided Differences,Least-Squares Approximations. Ceng375 Numerical Computations at December 9, 2010
Lecture 8, Ceng375 Numerical Computations at December 9, 2010 Computer Engineering Department Çankaya University 8.1 Contents 1 2 3 8.2 : These provide a more efficient way to construct an interpolating
More informationDesign considerations
Curves Design considerations local control of shape design each segment independently smoothness and continuity ability to evaluate derivatives stability small change in input leads to small change in
More informationNeed for Parametric Equations
Curves and Surfaces Curves and Surfaces Need for Parametric Equations Affine Combinations Bernstein Polynomials Bezier Curves and Surfaces Continuity when joining curves B Spline Curves and Surfaces Need
More informationCurves. Computer Graphics CSE 167 Lecture 11
Curves Computer Graphics CSE 167 Lecture 11 CSE 167: Computer graphics Polynomial Curves Polynomial functions Bézier Curves Drawing Bézier curves Piecewise Bézier curves Based on slides courtesy of Jurgen
More informationSplines. Parameterization of a Curve. Curve Representations. Roller coaster. What Do We Need From Curves in Computer Graphics? Modeling Complex Shapes
CSCI 420 Computer Graphics Lecture 8 Splines Jernej Barbic University of Southern California Hermite Splines Bezier Splines Catmull-Rom Splines Other Cubic Splines [Angel Ch 12.4-12.12] Roller coaster
More informationCS130 : Computer Graphics Curves. Tamar Shinar Computer Science & Engineering UC Riverside
CS130 : Computer Graphics Curves Tamar Shinar Computer Science & Engineering UC Riverside Design considerations local control of shape design each segment independently smoothness and continuity ability
More informationLecture 9: Introduction to Spline Curves
Lecture 9: Introduction to Spline Curves Splines are used in graphics to represent smooth curves and surfaces. They use a small set of control points (knots) and a function that generates a curve through
More informationComputer Graphics Curves and Surfaces. Matthias Teschner
Computer Graphics Curves and Surfaces Matthias Teschner Outline Introduction Polynomial curves Bézier curves Matrix notation Curve subdivision Differential curve properties Piecewise polynomial curves
More informationJustify all your answers and write down all important steps. Unsupported answers will be disregarded.
Numerical Analysis FMN011 2017/05/30 The exam lasts 5 hours and has 15 questions. A minimum of 35 points out of the total 70 are required to get a passing grade. These points will be added to those you
More informationMoving Beyond Linearity
Moving Beyond Linearity Basic non-linear models one input feature: polynomial regression step functions splines smoothing splines local regression. more features: generalized additive models. Polynomial
More informationCubic Splines By Dave Slomer
Cubic Splines By Dave Slomer [Note: Before starting any example or exercise below, press g on the home screen to Clear a-z.] Curve fitting, the process of finding a function that passes through (or near)
More informationTo Do. Resources. Algorithm Outline. Simplifications. Advanced Computer Graphics (Spring 2013) Surface Simplification: Goals (Garland)
Advanced omputer Graphics (Spring 213) S 283, Lecture 6: Quadric Error Metrics Ravi Ramamoorthi To Do Assignment 1, Due Feb 22. Should have made some serious progress by end of week This lecture reviews
More informationCurve Representation ME761A Instructor in Charge Prof. J. Ramkumar Department of Mechanical Engineering, IIT Kanpur
Curve Representation ME761A Instructor in Charge Prof. J. Ramkumar Department of Mechanical Engineering, IIT Kanpur Email: jrkumar@iitk.ac.in Curve representation 1. Wireframe models There are three types
More informationHomework #6 Brief Solutions 2012
Homework #6 Brief Solutions %page 95 problem 4 data=[-,;-,;,;4,] data = - - 4 xk=data(:,);yk=data(:,);s=csfit(xk,yk,-,) %Using the program to find the coefficients S =.456 -.456 -.. -.5.9 -.5484. -.58.87.
More informationDeficient Quartic Spline Interpolation
International Journal of Computational Science and Mathematics. ISSN 0974-3189 Volume 3, Number 2 (2011), pp. 227-236 International Research Publication House http://www.irphouse.com Deficient Quartic
More informationChapter 18. Geometric Operations
Chapter 18 Geometric Operations To this point, the image processing operations have computed the gray value (digital count) of the output image pixel based on the gray values of one or more input pixels;
More informationCS354 Computer Graphics Surface Representation III. Qixing Huang March 5th 2018
CS354 Computer Graphics Surface Representation III Qixing Huang March 5th 2018 Today s Topic Bspline curve operations (Brief) Knot Insertion/Deletion Subdivision (Focus) Subdivision curves Subdivision
More informationCOMPUTER AIDED ENGINEERING DESIGN (BFF2612)
COMPUTER AIDED ENGINEERING DESIGN (BFF2612) BASIC MATHEMATICAL CONCEPTS IN CAED by Dr. Mohd Nizar Mhd Razali Faculty of Manufacturing Engineering mnizar@ump.edu.my COORDINATE SYSTEM y+ y+ z+ z+ x+ RIGHT
More informationCSE 167: Introduction to Computer Graphics Lecture #11: Bezier Curves. Jürgen P. Schulze, Ph.D. University of California, San Diego Fall Quarter 2016
CSE 167: Introduction to Computer Graphics Lecture #11: Bezier Curves Jürgen P. Schulze, Ph.D. University of California, San Diego Fall Quarter 2016 Announcements Project 3 due tomorrow Midterm 2 next
More informationBASIC LOESS, PBSPLINE & SPLINE
CURVES AND SPLINES DATA INTERPOLATION SGPLOT provides various methods for fitting smooth trends to scatterplot data LOESS An extension of LOWESS (Locally Weighted Scatterplot Smoothing), uses locally weighted
More informationCubic Splines and Matlab
Cubic Splines and Matlab October 7, 2006 1 Introduction In this section, we introduce the concept of the cubic spline, and how they are implemented in Matlab. Of particular importance are the new Matlab
More informationParameterization. Michael S. Floater. November 10, 2011
Parameterization Michael S. Floater November 10, 2011 Triangular meshes are often used to represent surfaces, at least initially, one reason being that meshes are relatively easy to generate from point
More informationThe goal is the definition of points with numbers and primitives with equations or functions. The definition of points with numbers requires a
The goal is the definition of points with numbers and primitives with equations or functions. The definition of points with numbers requires a coordinate system and then the measuring of the point with
More informationParameterization of triangular meshes
Parameterization of triangular meshes Michael S. Floater November 10, 2009 Triangular meshes are often used to represent surfaces, at least initially, one reason being that meshes are relatively easy to
More informationPiecewise polynomial interpolation
Chapter 2 Piecewise polynomial interpolation In ection.6., and in Lab, we learned that it is not a good idea to interpolate unctions by a highorder polynomials at equally spaced points. However, it transpires
More informationResearch Article Data Visualization Using Rational Trigonometric Spline
Applied Mathematics Volume Article ID 97 pages http://dx.doi.org/.//97 Research Article Data Visualization Using Rational Trigonometric Spline Uzma Bashir and Jamaludin Md. Ali School of Mathematical Sciences
More informationProperties of Quadratic functions
Name Today s Learning Goals: #1 How do we determine the axis of symmetry and vertex of a quadratic function? Properties of Quadratic functions Date 5-1 Properties of a Quadratic Function A quadratic equation
More informationCOMP3421. Global Lighting Part 2: Radiosity
COMP3421 Global Lighting Part 2: Radiosity Recap: Global Lighting The lighting equation we looked at earlier only handled direct lighting from sources: We added an ambient fudge term to account for all
More informationFitting latency models using B-splines in EPICURE for DOS
Fitting latency models using B-splines in EPICURE for DOS Michael Hauptmann, Jay Lubin January 11, 2007 1 Introduction Disease latency refers to the interval between an increment of exposure and a subsequent
More informationIntroduction to ANSYS DesignXplorer
Lecture 4 14. 5 Release Introduction to ANSYS DesignXplorer 1 2013 ANSYS, Inc. September 27, 2013 s are functions of different nature where the output parameters are described in terms of the input parameters
More informationThe Free-form Surface Modelling System
1. Introduction The Free-form Surface Modelling System Smooth curves and surfaces must be generated in many computer graphics applications. Many real-world objects are inherently smooth (fig.1), and much
More informationRational Bezier Curves
Rational Bezier Curves Use of homogeneous coordinates Rational spline curve: define a curve in one higher dimension space, project it down on the homogenizing variable Mathematical formulation: n P(u)
More informationLet be a function. We say, is a plane curve given by the. Let a curve be given by function where is differentiable with continuous.
Module 8 : Applications of Integration - II Lecture 22 : Arc Length of a Plane Curve [Section 221] Objectives In this section you will learn the following : How to find the length of a plane curve 221
More informationNonparametric Mixed-Effects Models for Longitudinal Data
Nonparametric Mixed-Effects Models for Longitudinal Data Zhang Jin-Ting Dept of Stat & Appl Prob National University of Sinagpore University of Seoul, South Korea, 7 p.1/26 OUTLINE The Motivating Data
More informationFitting to a set of data. Lecture on fitting
Fitting to a set of data Lecture on fitting Linear regression Linear regression Residual is the amount difference between a real data point and a modeled data point Fitting a polynomial to data Could use
More informationConvergence of C 2 Deficient Quartic Spline Interpolation
Advances in Computational Sciences and Technology ISSN 0973-6107 Volume 10, Number 4 (2017) pp. 519-527 Research India Publications http://www.ripublication.com Convergence of C 2 Deficient Quartic Spline
More informationTO DUY ANH SHIP CALCULATION
TO DUY ANH SHIP CALCULATION Ship Calculattion (1)-Space Cuvers 3D-curves play an important role in the engineering, design and manufature in Shipbuilding. Prior of the development of mathematical and computer
More informationApplied Statistics : Practical 9
Applied Statistics : Practical 9 This practical explores nonparametric regression and shows how to fit a simple additive model. The first item introduces the necessary R commands for nonparametric regression
More informationParametric Surfaces. Michael Kazhdan ( /657) HB , FvDFH 11.2
Parametric Surfaces Michael Kazhdan (601.457/657) HB 10.6 -- 10.9, 10.1 FvDFH 11.2 Announcement OpenGL review session: When: Wednesday (10/1) @ 7:00-9:00 PM Where: Olin 05 Cubic Splines Given a collection
More informationInterpolation and Basis Fns
CS148: Introduction to Computer Graphics and Imaging Interpolation and Basis Fns Topics Today Interpolation Linear and bilinear interpolation Barycentric interpolation Basis functions Square, triangle,,
More information8 Piecewise Polynomial Interpolation
Applied Math Notes by R. J. LeVeque 8 Piecewise Polynomial Interpolation 8. Pitfalls of high order interpolation Suppose we know the value of a function at several points on an interval and we wish to
More informationMontana Instructional Alignment HPS Critical Competencies Mathematics Honors Pre-Calculus
Content Standards Content Standard 1 - Number Sense and Operations Content Standard 2 - Data Analysis A student, applying reasoning and problem solving, will use number sense and operations to represent
More informationAP Calculus. Slide 1 / 213 Slide 2 / 213. Slide 3 / 213. Slide 4 / 213. Slide 4 (Answer) / 213 Slide 5 / 213. Derivatives. Derivatives Exploration
Slide 1 / 213 Slide 2 / 213 AP Calculus Derivatives 2015-11-03 www.njctl.org Slide 3 / 213 Table of Contents Slide 4 / 213 Rate of Change Slope of a Curve (Instantaneous ROC) Derivative Rules: Power, Constant,
More information