Parametric curves. Reading. Parametric polynomial curves. Mathematical curve representation. Brian Curless CSE 457 Spring 2015

Size: px
Start display at page:

Download "Parametric curves. Reading. Parametric polynomial curves. Mathematical curve representation. Brian Curless CSE 457 Spring 2015"

Transcription

1 Readig Required: Agel , 0.5., , 0.9 Parametric curves Bria Curless CSE 457 Sprig 05 Optioal Bartels, Beatty, ad Barsy. A Itroductio to Splies for use i Computer Graphics ad Geometric Modelig, 987. Fari. Curves ad Surfaces for CAGD: A Practical Guide, 4th ed., 997. Mathematical curve represetatio Parametric polyomial curves Explicit: y =f (x ) what if the curve is t a fuctio, e.g., a circle? We ll use parametric curves, Q (u )=(x (u ), y (u )), where the fuctios are all polyomials i the parameter. Implicit: g (x, y ) = 0 Advatages: easy (ad efficiet) to compute ifiitely differetiable (all derivatives above the th derivative are zero) Parametric: Q (u ) = (x (u ), y (u )) For the circle: x (u ) = cos u y (u ) = si u We ll also assume that u varies from 0 to. Note that we ll focus o D curves, but the geeralizatio to 3D curves is completely straightforward. 3 4

2 de Casteljau s algorithm de Casteljau s algorithm, cot d We will ow build a curve geometrically, ad the show how it is a parametric polyomial curve. Recursive otatio: We start with cotrol poits {V, V, V 3, V 4 } ad coect them together to mae a cotrol polygo. We the recursively subdivide: V 0 What is the equatio for? What if u = 0? What if u =? 5 6 Fidig Q(u) Fidig Q(u) (cot d) Let s solve for Q(u): 0 (- ) 0 (- ) (- ) 3 V u V uv V u V uv V u V uv I geeral, i i Qu ( ) u ( u) Vi i 0 i where choose i is: 0 (- ) 0 (- ) V u V uv V u V uv ( ) (- ) 0 Qu uv uv! i ( i)! i! This defies a class of curves called Bézier curves. We ca also write this as: (- u)[(- u) V0 uv] u[(- u) V uv] (- u)[(- u){(- u) V0 uv} u{(- u) VuV}] (- u) V0 3 u(- u) V3 u (- u) V u V3 where the are the Berstei polyomials: 3 V0 ( 3V0 3 V) u(3v0 6V3 V) u ( V0 3V3 V V3) u 3 V0, x ( 3V0, x 3 V, x) u(3v0, x 6V, x 3 V, x) u ( V0, x 3V, x 3 V, x V3, x) u 3 V0, y ( 3V0, y 3 V, y ) u(3v0, y 6V, y 3 V, y ) u ( V0, y 3V, y 3V, y V3, y ) u 7 bi ( u) u ( u) i i i Q: If we have cotrol poits, what is the polyomial order of the curve? 8

3 Berstei polyomials Displayig Bézier curves For degree 3, the Berstei polyomials are: How could we draw oe of these thigs? Useful properties (for Berstei polyomials of ay degree) o the iterval [0,]: The sum of all four is exactly for ay u. (We say the curves form a partitio of uity ). Each polyomial has value betwee 0 ad. These together imply that the curve is geerated by covex combiatios of the cotrol poits ad therefore lies withi the covex hull of those cotrol poits. The covex hull of a poit set is the smallest covex polygo (i D) or polyhedro (i 3D) eclosig the poits. I D, thi of a strig looped aroud the outside of the poit set ad the pulled tightly aroud the set. 9 0 Curve desiderata Local cotrol Bézier curves offer a fairly simple way to model parametric curves. But, let s cosider some geeral properties we would lie curves to have Oe problem with Béziers is that every cotrol poit affects every poit o the curve (except the edpoits). Movig a sigle cotrol poit affects the whole curve! We d lie to have local cotrol, that is, have each cotrol poit affect some well-defied eighborhood aroud that poit.

4 Iterpolatio Cotiuity Bézier curves are approximatig. The curve does ot (ecessarily) pass through all the cotrol poits. Each poit pulls the curve toward it, but other poits are pullig as well. We wat our curve to have cotiuity: there should t be ay abrupt chages as we move alog the curve. 0 th order cotiuity would mea that curve does t jump from oe place to aother. We d lie to have a curve that is iterpolatig, that is, that always passes through every cotrol poit. We ca also loo at derivatives of the curve to get higher order cotiuity. 3 4 st ad d Derivative Cotiuity C (Parametric) Cotiuity First order cotiuity implies cotiuous first derivative: dq( u) Q'( u) du Let s thi of u as time ad Q(u) as the path of a particle through space. What is the meaig of the first derivative, ad which way does it poit? I geeral, we defie C cotiuity as follows: Qu ( ) is C cotiuous i ( i ) dqu ( ) Q ( u) i is cotiuous for 0 i du Note: these are ested degrees of cotiuity: iff C - : C 0 : Secod order cotiuity meas cotiuous secod derivative: dqu ( ) Q''( u) du What is the ituitive meaig of this derivative? C, C : C 3, C 4, : 5 6

5 Bézier curves splies Esurig C 0 cotiuity Bézier curves have C-ifiity cotiuity o their iteriors, but we saw that they do ot exhibit local cotrol or iterpolate their cotrol poits. It is possible to defie poits that we wat to iterpolate, ad the solve for the Bézier cotrol poits that will do the job. But, you will eed as may cotrol poits as iterpolated poits -> high order polyomials -> wiggly curves. (Ad you still wo t have local cotrol.) Suppose we have a cubic Bézier defied by (V 0,V,V,V 3 ), ad we wat to attach aother curve (W 0,W,W,W 3 ) to it, so that there is C 0 cotiuity at the joit. What costrait(s) does this place o (W 0,W,W,W 3 )? Istead, we ll splice together a curve from idividual Béziers segmets, i particular, cubic Béziers. We call these curves splies. The primary cocer whe splicig cuves together is gettig good cotiuity at the edpoits where they meet 7 8 The C 0 Bezier splie st derivatives at the edpoits How the could we costruct a curve passig through a set of poits P P? For degree 3 (cubic) curves, we have already show that we get: We ca expad the terms i u ad rearrage to get: What the is the first derivative whe evaluated at each edpoit, u = 0 ad u =? We call this curve a splie. The edpoits of the Bezier segmets are called joits. All other Bezier poits (i.e., ot edpoits) are called ier Bezier poits; these poits are geerally ot iterpolated. I the aimator project, you will costruct such a curve by specifyig all the Bezier cotrol poits directly. 9 0

6 Esurig C cotiuity The C Bezier splie Suppose we have a cubic Bézier defied by (V 0,V,V,V 3 ), ad we wat to attach aother curve (W 0,W,W,W 3 ) to it, so that there is C cotiuity at the joit. How the could we costruct a curve passig through a set of poits P 0 P? What costrait(s) does this place o (W 0,W,W,W 3 )? We ca specify the Bezier cotrol poits directly, or we ca devise a scheme for placig them automatically Catmull-Rom splies Catmull-Rom to Beziers If we set each derivative to be oe half of the vector betwee the previous ad ext cotrols, we get a Catmull-Rom splie. We ca write the Catmull-Rom to Bezier trasformatio as: This leads to: 3 4

7 Edpoits of Catmull-Rom splies Tesio cotrol We ca see that Catmull-Rom splies do t iterpolate the first ad last cotrol poits. We ca give more cotrol by exposig the derivative scale factor as a parameter: By repeatig those cotrol poits, we ca force iterpolatio. The parameter cotrols slacess. Catmull-Rom uses = /. Here s a example with =3/. 5 6 d derivatives at the edpoits Esurig C cotiuity Fially, we ll wat to develop C splies. To do this, we ll eed secod derivatives of Bezier curves. Taig the secod derivative of Q (u ) yields: Suppose we have a cubic Bézier defied by (V 0,V,V,V 3 ), ad we wat to attach aother curve (W 0,W,W,W 3 ) to it, so that there is C cotiuity at the joit. What costrait(s) does this place o (W 0,W,W,W 3 )? 7 8

8 Buildig a complex splie B-splies Istead of specifyig the Bézier cotrol poits themselves, let s specify the corers of the A-frames i order to build a C cotiuous splie. Here is the completed B-splie. What are the Bézier cotrol poits, i terms of the de Boor poits? These are called B-splies. The startig set of poits are called de Boor poits B-splies to Beziers Edpoits of B-splies We ca write the B-splie to Bezier trasformatio as: As with Catmull-Rom splies, the first ad last cotrol poits of B-splies are geerally ot iterpolated. Agai, we ca force iterpolatio by repeatig the edpoits twice. 3 3

9 Curves i the aimator project I the aimator project, you will draw a curve o the scree: Q( u) x( u), y( u) You will actually treat this curve as: ( u) y( u) tu ( ) xu ( ) Where is a variable you wat to aimate. We ca thi of the result as a fuctio: ( t) I geeral, you have to apply some costraits to mae sure that (t ) actually is a fuctio Closig the loop What if we wat a closed curve, i.e., a loop? With Catmull-Rom ad B-splie curves, this is easy: Drawig Bézier curves, revisited Let s retur to the questio of how to draw Bezier curves, the buildig bloc for splies. Cosider a set of Bézier cotrol poits are arraged as follows: 35 How may lie segmets do you really eed to draw? It would be ice if we had a adaptive algorithm, that would tae ito accout flatess. DisplayBezier( V0, V, V, V3 ) begi if ( FlatEough( V0, V, V, V3 ) ) Lie( V0, V3 ); else somethig; ed; 36

10 Subdivide ad coquer Testig for flatess DisplayBezier( V0, V, V, V3 ) begi if ( FlatEough( V0, V, V, V3 ) ) Lie( V0, V3 ); else Subdivide(V[ ]) L[ ], R[ ] DisplayBezier( L0, L, L, L3 ); DisplayBezier( R0, R, R, R3 ); ed; 37 Compare total legth of cotrol polygo to legth of lie coectig edpoits: 38 Reparameterizatio Arc legth parameterizatio We have so far bee cosiderig parametric cotiuity, derivatives w.r.t. the parameter u. This form of cotiuity maes sese particularly if we really are describig a particle movig over time ad wat its motio (e.g., velocity ad acceleratio) to be smooth. We ca reparameterize a curve so that equal steps i parameter space (we ll call this ew parameter s ) map to equal distaces alog the curve: But, what if we re thiig oly i terms of the shape of the curve? Is the parameterizatio actually itrisic to the shape, i.e., is it the case that a shape has oly oe parameterizatio? We call this a arc legth parameterizatio. We ca re-write the equal step requiremet as: Looig at very small steps, we fid: 39 40

11 G (Geometric) Cotiuity Now, we defie geometric G cotiuity as follows: Qs ( ) is G cotiuous iff i () i dqs ( ) Q ( s) is cotiuous for 0 i i ds Where Q (s ) is parameterized by arc legth. The first derivative still poits alog the taget, but its legth is always. G cotiuity is usually a weaer costrait tha C cotiuity (e.g., speed alog the curve does ot matter). G Cotiuity (cot d) The secod derivative ow has a specific geometric iterpretatio. First, the osculatig circle at a poit o a curve ca be defied based o the limit behavior of three poits movig toward each other: c r The secod derivative Q (s ) the has these properties: Q( s) ( s) Q( s) c( s) Q( s) rs ( ) where r (s ) ad c(s ) are the radius ad ceter of O (s ), respectively, ad (s ) is the curvature of the curve at s. 4 4 Ratioal polyomial curves Remarably, parametric polyomial curves caot represet somethig as simple as a circle! BUT, ratios of polyomials ca. We ca write these i terms of homogeeous coordiates, which we the ormalize: Ratioal polyomial curves (cot d) What do we get for the followig curve? u Q D( u) u u xu ( ) au 0 yu 0 Q D( u) ( ) b u wu ( ) cu 0 Normalize cu 0 Q D ( u ) au cu 0 0 bu cu 0 0 The equatios above describe a ratioal Bézier curve. It ca be represeted i terms of cotrol poits, but ow we add the homogeous dimesio. So for a D curve, we have cotrol poits with three compoets (lofted up ito 3D), where the homogeous compoet ca be somethig other tha. Q: How does Illustrator represet a circle? 43 44

12 NURBS I geeral, we ca splie together ratioal Bézier curves, to get thigs lie ratioal B-splies. Aother thig we ca do is vary the rage of u so that it is ot always [0..] i each Bézier segmet of a splie. E.g, it could be [0..] i oe segmet ad the [0..] i the ext. Summary What to tae home from this lecture: Geometric ad algebraic defiitios of Bézier curves. Basic properties of Bézier curves. How to display Bézier curves with lie segmets. Meaigs of C cotiuities. Geometric coditios for cotiuity of cubic splies. Properties of B-splies ad Catmull-Rom splies. Geometric costructio of B-splies ad Catmull- Rom splies. How to costruct closed loop splies. The u-rage affects placemet of cotrol poits. The result is a o-uiform splie. A very commo type of splie is a No-Uiform Ratioal B-Splie or NURBS. (The B i B-splie techically stads for Basis. ) 45 46

Reading. Parametric curves. Mathematical curve representation. Curves before computers. Required: Angel , , , 11.9.

Reading. Parametric curves. Mathematical curve representation. Curves before computers. Required: Angel , , , 11.9. Readig Required: Agel.-.3,.5.,.6-.7,.9. Optioal Parametric curves Bartels, Beatty, ad Barsky. A Itroductio to Splies for use i Computer Graphics ad Geometric Modelig, 987. Fari. Curves ad Surfaces for

More information

Parametric curves. Brian Curless CSE 457 Spring 2016

Parametric curves. Brian Curless CSE 457 Spring 2016 Parametric curves Brian Curless CSE 457 Spring 2016 1 Reading Required: Angel 10.1-10.3, 10.5.2, 10.6-10.7, 10.9 Optional Bartels, Beatty, and Barsky. An Introduction to Splines for use in Computer Graphics

More information

Parametric curves. Reading. Curves before computers. Mathematical curve representation. CSE 457 Winter Required:

Parametric curves. Reading. Curves before computers. Mathematical curve representation. CSE 457 Winter Required: Reading Required: Angel 10.1-10.3, 10.5.2, 10.6-10.7, 10.9 Parametric curves CSE 457 Winter 2014 Optional Bartels, Beatty, and Barsky. An Introduction to Splines for use in Computer Graphics and Geometric

More information

Convex hull ( 凸殻 ) property

Convex hull ( 凸殻 ) property Covex hull ( 凸殻 ) property The covex hull of a set of poits S i dimesios is the itersectio of all covex sets cotaiig S. For N poits P,..., P N, the covex hull C is the give by the expressio The covex hull

More information

Cubic Polynomial Curves with a Shape Parameter

Cubic Polynomial Curves with a Shape Parameter roceedigs of the th WSEAS Iteratioal Coferece o Robotics Cotrol ad Maufacturig Techology Hagzhou Chia April -8 00 (pp5-70) Cubic olyomial Curves with a Shape arameter MO GUOLIANG ZHAO YANAN Iformatio ad

More information

Bezier curves. Figure 2 shows cubic Bezier curves for various control points. In a Bezier curve, only

Bezier curves. Figure 2 shows cubic Bezier curves for various control points. In a Bezier curve, only Edited: Yeh-Liag Hsu (998--; recommeded: Yeh-Liag Hsu (--9; last updated: Yeh-Liag Hsu (9--7. Note: This is the course material for ME55 Geometric modelig ad computer graphics, Yua Ze Uiversity. art of

More information

EVALUATION OF TRIGONOMETRIC FUNCTIONS

EVALUATION OF TRIGONOMETRIC FUNCTIONS EVALUATION OF TRIGONOMETRIC FUNCTIONS Whe first exposed to trigoometric fuctios i high school studets are expected to memorize the values of the trigoometric fuctios of sie cosie taget for the special

More information

Reading. Subdivision curves and surfaces. Subdivision curves. Chaikin s algorithm. Recommended:

Reading. Subdivision curves and surfaces. Subdivision curves. Chaikin s algorithm. Recommended: Readig Recommeded: Stollitz, DeRose, ad Salesi. Wavelets for Computer Graphics: Theory ad Applicatios, 996, sectio 6.-6.3, 0., A.5. Subdivisio curves ad surfaces Note: there is a error i Stollitz, et al.,

More information

Computational Geometry

Computational Geometry Computatioal Geometry Chapter 4 Liear programmig Duality Smallest eclosig disk O the Ageda Liear Programmig Slides courtesy of Craig Gotsma 4. 4. Liear Programmig - Example Defie: (amout amout cosumed

More information

. Written in factored form it is easy to see that the roots are 2, 2, i,

. Written in factored form it is easy to see that the roots are 2, 2, i, CMPS A Itroductio to Programmig Programmig Assigmet 4 I this assigmet you will write a java program that determies the real roots of a polyomial that lie withi a specified rage. Recall that the roots (or

More information

The isoperimetric problem on the hypercube

The isoperimetric problem on the hypercube The isoperimetric problem o the hypercube Prepared by: Steve Butler November 2, 2005 1 The isoperimetric problem We will cosider the -dimesioal hypercube Q Recall that the hypercube Q is a graph whose

More information

CHAPTER IV: GRAPH THEORY. Section 1: Introduction to Graphs

CHAPTER IV: GRAPH THEORY. Section 1: Introduction to Graphs CHAPTER IV: GRAPH THEORY Sectio : Itroductio to Graphs Sice this class is called Number-Theoretic ad Discrete Structures, it would be a crime to oly focus o umber theory regardless how woderful those topics

More information

Numerical Methods Lecture 6 - Curve Fitting Techniques

Numerical Methods Lecture 6 - Curve Fitting Techniques Numerical Methods Lecture 6 - Curve Fittig Techiques Topics motivatio iterpolatio liear regressio higher order polyomial form expoetial form Curve fittig - motivatio For root fidig, we used a give fuctio

More information

n n B. How many subsets of C are there of cardinality n. We are selecting elements for such a

n n B. How many subsets of C are there of cardinality n. We are selecting elements for such a 4. [10] Usig a combiatorial argumet, prove that for 1: = 0 = Let A ad B be disjoit sets of cardiality each ad C = A B. How may subsets of C are there of cardiality. We are selectig elemets for such a subset

More information

Alpha Individual Solutions MAΘ National Convention 2013

Alpha Individual Solutions MAΘ National Convention 2013 Alpha Idividual Solutios MAΘ Natioal Covetio 0 Aswers:. D. A. C 4. D 5. C 6. B 7. A 8. C 9. D 0. B. B. A. D 4. C 5. A 6. C 7. B 8. A 9. A 0. C. E. B. D 4. C 5. A 6. D 7. B 8. C 9. D 0. B TB. 570 TB. 5

More information

Creating Exact Bezier Representations of CST Shapes. David D. Marshall. California Polytechnic State University, San Luis Obispo, CA , USA

Creating Exact Bezier Representations of CST Shapes. David D. Marshall. California Polytechnic State University, San Luis Obispo, CA , USA Creatig Exact Bezier Represetatios of CST Shapes David D. Marshall Califoria Polytechic State Uiversity, Sa Luis Obispo, CA 93407-035, USA The paper presets a method of expressig CST shapes pioeered by

More information

Parabolic Path to a Best Best-Fit Line:

Parabolic Path to a Best Best-Fit Line: Studet Activity : Fidig the Least Squares Regressio Lie By Explorig the Relatioship betwee Slope ad Residuals Objective: How does oe determie a best best-fit lie for a set of data? Eyeballig it may be

More information

Texture Mapping. Jian Huang. This set of slides references the ones used at Ohio State for instruction.

Texture Mapping. Jian Huang. This set of slides references the ones used at Ohio State for instruction. Texture Mappig Jia Huag This set of slides refereces the oes used at Ohio State for istructio. Ca you do this What Dreams May Come Texture Mappig Of course, oe ca model the exact micro-geometry + material

More information

Smooth Spline Surfaces over Irregular Meshes

Smooth Spline Surfaces over Irregular Meshes Smooth Splie Surfaces over Irregular Meshes Charles Loop Apple Computer, Ic. Abstract A algorithm for creatig smooth splie surfaces over irregular meshes is preseted. The algorithm is a geeralizatio of

More information

CSC165H1 Worksheet: Tutorial 8 Algorithm analysis (SOLUTIONS)

CSC165H1 Worksheet: Tutorial 8 Algorithm analysis (SOLUTIONS) CSC165H1, Witer 018 Learig Objectives By the ed of this worksheet, you will: Aalyse the ruig time of fuctios cotaiig ested loops. 1. Nested loop variatios. Each of the followig fuctios takes as iput a

More information

Lecture 24: Bezier Curves and Surfaces. thou shalt be near unto me Genesis 45:10

Lecture 24: Bezier Curves and Surfaces. thou shalt be near unto me Genesis 45:10 Lecture 24: Bezier Curves ad Surfaces thou shalt be ear uto me Geesis 45:0. Iterpolatio ad Approximatio Freeform curves ad surfaces are smooth shapes ofte describig ma-made objects. The hood of a car,

More information

Assignment 5; Due Friday, February 10

Assignment 5; Due Friday, February 10 Assigmet 5; Due Friday, February 10 17.9b The set X is just two circles joied at a poit, ad the set X is a grid i the plae, without the iteriors of the small squares. The picture below shows that the iteriors

More information

Learning to Shoot a Goal Lecture 8: Learning Models and Skills

Learning to Shoot a Goal Lecture 8: Learning Models and Skills Learig to Shoot a Goal Lecture 8: Learig Models ad Skills How do we acquire skill at shootig goals? CS 344R/393R: Robotics Bejami Kuipers Learig to Shoot a Goal The robot eeds to shoot the ball i the goal.

More information

Math Section 2.2 Polynomial Functions

Math Section 2.2 Polynomial Functions Math 1330 - Sectio. Polyomial Fuctios Our objectives i workig with polyomial fuctios will be, first, to gather iformatio about the graph of the fuctio ad, secod, to use that iformatio to geerate a reasoably

More information

Recursive Procedures. How can you model the relationship between consecutive terms of a sequence?

Recursive Procedures. How can you model the relationship between consecutive terms of a sequence? 6. Recursive Procedures I Sectio 6.1, you used fuctio otatio to write a explicit formula to determie the value of ay term i a Sometimes it is easier to calculate oe term i a sequece usig the previous terms.

More information

Ones Assignment Method for Solving Traveling Salesman Problem

Ones Assignment Method for Solving Traveling Salesman Problem Joural of mathematics ad computer sciece 0 (0), 58-65 Oes Assigmet Method for Solvig Travelig Salesma Problem Hadi Basirzadeh Departmet of Mathematics, Shahid Chamra Uiversity, Ahvaz, Ira Article history:

More information

Examples and Applications of Binary Search

Examples and Applications of Binary Search Toy Gog ITEE Uiersity of Queeslad I the secod lecture last week we studied the biary search algorithm that soles the problem of determiig if a particular alue appears i a sorted list of iteger or ot. We

More information

CSC 220: Computer Organization Unit 11 Basic Computer Organization and Design

CSC 220: Computer Organization Unit 11 Basic Computer Organization and Design College of Computer ad Iformatio Scieces Departmet of Computer Sciece CSC 220: Computer Orgaizatio Uit 11 Basic Computer Orgaizatio ad Desig 1 For the rest of the semester, we ll focus o computer architecture:

More information

Module 8-7: Pascal s Triangle and the Binomial Theorem

Module 8-7: Pascal s Triangle and the Binomial Theorem Module 8-7: Pascal s Triagle ad the Biomial Theorem Gregory V. Bard April 5, 017 A Note about Notatio Just to recall, all of the followig mea the same thig: ( 7 7C 4 C4 7 7C4 5 4 ad they are (all proouced

More information

Consider the following population data for the state of California. Year Population

Consider the following population data for the state of California. Year Population Assigmets for Bradie Fall 2016 for Chapter 5 Assigmet sheet for Sectios 5.1, 5.3, 5.5, 5.6, 5.7, 5.8 Read Pages 341-349 Exercises for Sectio 5.1 Lagrage Iterpolatio #1, #4, #7, #13, #14 For #1 use MATLAB

More information

1 Graph Sparsfication

1 Graph Sparsfication CME 305: Discrete Mathematics ad Algorithms 1 Graph Sparsficatio I this sectio we discuss the approximatio of a graph G(V, E) by a sparse graph H(V, F ) o the same vertex set. I particular, we cosider

More information

Mathematics and Art Activity - Basic Plane Tessellation with GeoGebra

Mathematics and Art Activity - Basic Plane Tessellation with GeoGebra 1 Mathematics ad Art Activity - Basic Plae Tessellatio with GeoGebra Worksheet: Explorig Regular Edge-Edge Tessellatios of the Cartesia Plae ad the Mathematics behid it. Goal: To eable Maths educators

More information

9 x and g(x) = 4. x. Find (x) 3.6. I. Combining Functions. A. From Equations. Example: Let f(x) = and its domain. Example: Let f(x) = and g(x) = x x 4

9 x and g(x) = 4. x. Find (x) 3.6. I. Combining Functions. A. From Equations. Example: Let f(x) = and its domain. Example: Let f(x) = and g(x) = x x 4 1 3.6 I. Combiig Fuctios A. From Equatios Example: Let f(x) = 9 x ad g(x) = 4 f x. Fid (x) g ad its domai. 4 Example: Let f(x) = ad g(x) = x x 4. Fid (f-g)(x) B. From Graphs: Graphical Additio. Example:

More information

Pattern Recognition Systems Lab 1 Least Mean Squares

Pattern Recognition Systems Lab 1 Least Mean Squares Patter Recogitio Systems Lab 1 Least Mea Squares 1. Objectives This laboratory work itroduces the OpeCV-based framework used throughout the course. I this assigmet a lie is fitted to a set of poits usig

More information

Counting Regions in the Plane and More 1

Counting Regions in the Plane and More 1 Coutig Regios i the Plae ad More 1 by Zvezdelia Stakova Berkeley Math Circle Itermediate I Group September 016 1. Overarchig Problem Problem 1 Regios i a Circle. The vertices of a polygos are arraged o

More information

Lecture 1: Introduction and Strassen s Algorithm

Lecture 1: Introduction and Strassen s Algorithm 5-750: Graduate Algorithms Jauary 7, 08 Lecture : Itroductio ad Strasse s Algorithm Lecturer: Gary Miller Scribe: Robert Parker Itroductio Machie models I this class, we will primarily use the Radom Access

More information

How do we evaluate algorithms?

How do we evaluate algorithms? F2 Readig referece: chapter 2 + slides Algorithm complexity Big O ad big Ω To calculate ruig time Aalysis of recursive Algorithms Next time: Litterature: slides mostly The first Algorithm desig methods:

More information

Area As A Limit & Sigma Notation

Area As A Limit & Sigma Notation Area As A Limit & Sigma Notatio SUGGESTED REFERENCE MATERIAL: As you work through the problems listed below, you should referece Chapter 5.4 of the recommeded textbook (or the equivalet chapter i your

More information

Improved triangular subdivision schemes 1

Improved triangular subdivision schemes 1 Improved triagular subdivisio schemes Hartmut Prautzsch 2 Georg Umlauf 3 Faultät für Iformati, Uiversität Karlsruhe, D-762 Karlsruhe, Germay E-mail: 2 prau@ira.ua.de 3 umlauf@ira.ua.de Abstract I this

More information

Our Learning Problem, Again

Our Learning Problem, Again Noparametric Desity Estimatio Matthew Stoe CS 520, Sprig 2000 Lecture 6 Our Learig Problem, Agai Use traiig data to estimate ukow probabilities ad probability desity fuctios So far, we have depeded o describig

More information

CS Polygon Scan Conversion. Slide 1

CS Polygon Scan Conversion. Slide 1 CS 112 - Polygo Sca Coversio Slide 1 Polygo Classificatio Covex All iterior agles are less tha 180 degrees Cocave Iterior agles ca be greater tha 180 degrees Degeerate polygos If all vertices are colliear

More information

Thompson s Group F (p + 1) is not Minimally Almost Convex

Thompson s Group F (p + 1) is not Minimally Almost Convex Thompso s Group F (p + ) is ot Miimally Almost Covex Claire Wladis Thompso s Group F (p + ). A Descriptio of F (p + ) Thompso s group F (p + ) ca be defied as the group of piecewiseliear orietatio-preservig

More information

Normals. In OpenGL the normal vector is part of the state Set by glnormal*()

Normals. In OpenGL the normal vector is part of the state Set by glnormal*() Ray Tracig 1 Normals OpeG the ormal vector is part of the state Set by glnormal*() -glnormal3f(x, y, z); -glnormal3fv(p); Usually we wat to set the ormal to have uit legth so cosie calculatios are correct

More information

The number n of subintervals times the length h of subintervals gives length of interval (b-a).

The number n of subintervals times the length h of subintervals gives length of interval (b-a). Simulator with MadMath Kit: Riema Sums (Teacher s pages) I your kit: 1. GeoGebra file: Ready-to-use projector sized simulator: RiemaSumMM.ggb 2. RiemaSumMM.pdf (this file) ad RiemaSumMMEd.pdf (educator's

More information

. Perform a geometric (ray-optics) construction (i.e., draw in the rays on the diagram) to show where the final image is formed.

. Perform a geometric (ray-optics) construction (i.e., draw in the rays on the diagram) to show where the final image is formed. MASSACHUSETTS INSTITUTE of TECHNOLOGY Departmet of Electrical Egieerig ad Computer Sciece 6.161 Moder Optics Project Laboratory 6.637 Optical Sigals, Devices & Systems Problem Set No. 1 Geometric optics

More information

Extended Space Mapping with Bézier Patches and Volumes

Extended Space Mapping with Bézier Patches and Volumes Eteded Space Mappig with Béier Patches ad Volumes Bejami Schmitt 1, 2 Aleader Pasko 1 Vladimir Savcheko 1 Abstract We eplore a geeral eteded space mappig as a framework for trasformig a hypersurface i

More information

Lecture 18. Optimization in n dimensions

Lecture 18. Optimization in n dimensions Lecture 8 Optimizatio i dimesios Itroductio We ow cosider the problem of miimizig a sigle scalar fuctio of variables, f x, where x=[ x, x,, x ]T. The D case ca be visualized as fidig the lowest poit of

More information

15-859E: Advanced Algorithms CMU, Spring 2015 Lecture #2: Randomized MST and MST Verification January 14, 2015

15-859E: Advanced Algorithms CMU, Spring 2015 Lecture #2: Randomized MST and MST Verification January 14, 2015 15-859E: Advaced Algorithms CMU, Sprig 2015 Lecture #2: Radomized MST ad MST Verificatio Jauary 14, 2015 Lecturer: Aupam Gupta Scribe: Yu Zhao 1 Prelimiaries I this lecture we are talkig about two cotets:

More information

Python Programming: An Introduction to Computer Science

Python Programming: An Introduction to Computer Science Pytho Programmig: A Itroductio to Computer Sciece Chapter 6 Defiig Fuctios Pytho Programmig, 2/e 1 Objectives To uderstad why programmers divide programs up ito sets of cooperatig fuctios. To be able to

More information

Introduction to Sigma Notation

Introduction to Sigma Notation Itroductio to Siga Notatio Steph de Silva //207 What is siga otatio? is the capital Greek letter for the soud s I this case, it s just shorthad for su Siga otatio is what we use whe we have a series of

More information

Image Segmentation EEE 508

Image Segmentation EEE 508 Image Segmetatio Objective: to determie (etract) object boudaries. It is a process of partitioig a image ito distict regios by groupig together eighborig piels based o some predefied similarity criterio.

More information

IMP: Superposer Integrated Morphometrics Package Superposition Tool

IMP: Superposer Integrated Morphometrics Package Superposition Tool IMP: Superposer Itegrated Morphometrics Package Superpositio Tool Programmig by: David Lieber ( 03) Caisius College 200 Mai St. Buffalo, NY 4208 Cocept by: H. David Sheets, Dept. of Physics, Caisius College

More information

A Taste of Maya. Character Setup

A Taste of Maya. Character Setup This tutorial goes through the steps to add aimatio cotrols to a previously modeled character. The character i the scee below is wearig clothes made with Cloth ad the sceery has bee created with Pait Effects.

More information

On (K t e)-saturated Graphs

On (K t e)-saturated Graphs Noame mauscript No. (will be iserted by the editor O (K t e-saturated Graphs Jessica Fuller Roald J. Gould the date of receipt ad acceptace should be iserted later Abstract Give a graph H, we say a graph

More information

1.2 Binomial Coefficients and Subsets

1.2 Binomial Coefficients and Subsets 1.2. BINOMIAL COEFFICIENTS AND SUBSETS 13 1.2 Biomial Coefficiets ad Subsets 1.2-1 The loop below is part of a program to determie the umber of triagles formed by poits i the plae. for i =1 to for j =

More information

AP B mirrors and lenses websheet 23.2

AP B mirrors and lenses websheet 23.2 Name: Class: _ Date: _ ID: A AP B mirrors ad leses websheet 232 Multiple Choice Idetify the choice that best completes the statemet or aswers the questio 1 The of light ca chage whe light is refracted

More information

Test 4 Review. dy du 9 5. sin5 zdz. dt. 5 Ê. x 2 È 1, 3. 2cos( x) dx is less than using Simpson's. ,1 t 5 t 2. ft () t2 4.

Test 4 Review. dy du 9 5. sin5 zdz. dt. 5 Ê. x 2 È 1, 3. 2cos( x) dx is less than using Simpson's. ,1 t 5 t 2. ft () t2 4. Name: Class: Date: ID: A Test Review Short Aswer. Fid the geeral solutio of the differetial equatio below ad check the result by differetiatio. dy du 9 u. Use the error formula to estimate the error i

More information

2D Isogeometric Shape Optimization considering both control point positions and weights as design variables

2D Isogeometric Shape Optimization considering both control point positions and weights as design variables 1 th World Cogress o tructural ad Multidiscipliary Optimizatio May 19-24, 213, Orlado, Florida, UA 2D Isogeometric hape Optimizatio cosiderig both cotrol poit positios ad weights as desig variables Yeo-Ul

More information

SD vs. SD + One of the most important uses of sample statistics is to estimate the corresponding population parameters.

SD vs. SD + One of the most important uses of sample statistics is to estimate the corresponding population parameters. SD vs. SD + Oe of the most importat uses of sample statistics is to estimate the correspodig populatio parameters. The mea of a represetative sample is a good estimate of the mea of the populatio that

More information

An Efficient Algorithm for Graph Bisection of Triangularizations

An Efficient Algorithm for Graph Bisection of Triangularizations A Efficiet Algorithm for Graph Bisectio of Triagularizatios Gerold Jäger Departmet of Computer Sciece Washigto Uiversity Campus Box 1045 Oe Brookigs Drive St. Louis, Missouri 63130-4899, USA jaegerg@cse.wustl.edu

More information

Administrative UNSUPERVISED LEARNING. Unsupervised learning. Supervised learning 11/25/13. Final project. No office hours today

Administrative UNSUPERVISED LEARNING. Unsupervised learning. Supervised learning 11/25/13. Final project. No office hours today Admiistrative Fial project No office hours today UNSUPERVISED LEARNING David Kauchak CS 451 Fall 2013 Supervised learig Usupervised learig label label 1 label 3 model/ predictor label 4 label 5 Supervised

More information

Section 7.2: Direction Fields and Euler s Methods

Section 7.2: Direction Fields and Euler s Methods Sectio 7.: Directio ields ad Euler s Methods Practice HW from Stewart Tetbook ot to had i p. 5 # -3 9-3 odd or a give differetial equatio we wat to look at was to fid its solutio. I this chapter we will

More information

Fundamentals of Media Processing. Shin'ichi Satoh Kazuya Kodama Hiroshi Mo Duy-Dinh Le

Fundamentals of Media Processing. Shin'ichi Satoh Kazuya Kodama Hiroshi Mo Duy-Dinh Le Fudametals of Media Processig Shi'ichi Satoh Kazuya Kodama Hiroshi Mo Duy-Dih Le Today's topics Noparametric Methods Parze Widow k-nearest Neighbor Estimatio Clusterig Techiques k-meas Agglomerative Hierarchical

More information

COMP 558 lecture 6 Sept. 27, 2010

COMP 558 lecture 6 Sept. 27, 2010 Radiometry We have discussed how light travels i straight lies through space. We would like to be able to talk about how bright differet light rays are. Imagie a thi cylidrical tube ad cosider the amout

More information

The Closest Line to a Data Set in the Plane. David Gurney Southeastern Louisiana University Hammond, Louisiana

The Closest Line to a Data Set in the Plane. David Gurney Southeastern Louisiana University Hammond, Louisiana The Closest Lie to a Data Set i the Plae David Gurey Southeaster Louisiaa Uiversity Hammod, Louisiaa ABSTRACT This paper looks at three differet measures of distace betwee a lie ad a data set i the plae:

More information

G 1 Interpolation of arbitrary meshes with Bézier patches

G 1 Interpolation of arbitrary meshes with Bézier patches Master s Degree i Applied Mathematics Supervised by Stefaie Hahma March 5th - July 5th 007 G Iterpolatio of arbitrary meshes with Bézier patches Jea Kutzma Lab. B.P. 53 3804 Greoble Cedex 9 Cotets Ackowledgmets

More information

CS 11 C track: lecture 1

CS 11 C track: lecture 1 CS 11 C track: lecture 1 Prelimiaries Need a CMS cluster accout http://acctreq.cms.caltech.edu/cgi-bi/request.cgi Need to kow UNIX IMSS tutorial liked from track home page Track home page: http://courses.cms.caltech.edu/courses/cs11/material

More information

Intro to Scientific Computing: Solutions

Intro to Scientific Computing: Solutions Itro to Scietific Computig: Solutios Dr. David M. Goulet. How may steps does it take to separate 3 objects ito groups of 4? We start with 5 objects ad apply 3 steps of the algorithm to reduce the pile

More information

Lecturers: Sanjam Garg and Prasad Raghavendra Feb 21, Midterm 1 Solutions

Lecturers: Sanjam Garg and Prasad Raghavendra Feb 21, Midterm 1 Solutions U.C. Berkeley CS170 : Algorithms Midterm 1 Solutios Lecturers: Sajam Garg ad Prasad Raghavedra Feb 1, 017 Midterm 1 Solutios 1. (4 poits) For the directed graph below, fid all the strogly coected compoets

More information

The Platonic solids The five regular polyhedra

The Platonic solids The five regular polyhedra The Platoic solids The five regular polyhedra Ole Witt-Hase jauary 7 www.olewitthase.dk Cotets. Polygos.... Topologically cosideratios.... Euler s polyhedro theorem.... Regular ets o a sphere.... The dihedral

More information

condition w i B i S maximum u i

condition w i B i S maximum u i ecture 10 Dyamic Programmig 10.1 Kapsack Problem November 1, 2004 ecturer: Kamal Jai Notes: Tobias Holgers We are give a set of items U = {a 1, a 2,..., a }. Each item has a weight w i Z + ad a utility

More information

Overview Chapter 12 A display model

Overview Chapter 12 A display model Overview Chapter 12 A display model Why graphics? A graphics model Examples Bjare Stroustrup www.stroustrup.com/programmig 3 Why bother with graphics ad GUI? Why bother with graphics ad GUI? It s very

More information

Lecture 5. Counting Sort / Radix Sort

Lecture 5. Counting Sort / Radix Sort Lecture 5. Coutig Sort / Radix Sort T. H. Corme, C. E. Leiserso ad R. L. Rivest Itroductio to Algorithms, 3rd Editio, MIT Press, 2009 Sugkyukwa Uiversity Hyuseug Choo choo@skku.edu Copyright 2000-2018

More information

An Efficient Algorithm for Graph Bisection of Triangularizations

An Efficient Algorithm for Graph Bisection of Triangularizations Applied Mathematical Scieces, Vol. 1, 2007, o. 25, 1203-1215 A Efficiet Algorithm for Graph Bisectio of Triagularizatios Gerold Jäger Departmet of Computer Sciece Washigto Uiversity Campus Box 1045, Oe

More information

EE123 Digital Signal Processing

EE123 Digital Signal Processing Last Time EE Digital Sigal Processig Lecture 7 Block Covolutio, Overlap ad Add, FFT Discrete Fourier Trasform Properties of the Liear covolutio through circular Today Liear covolutio with Overlap ad add

More information

CMPT 125 Assignment 2 Solutions

CMPT 125 Assignment 2 Solutions CMPT 25 Assigmet 2 Solutios Questio (20 marks total) a) Let s cosider a iteger array of size 0. (0 marks, each part is 2 marks) it a[0]; I. How would you assig a poiter, called pa, to store the address

More information

CONTINUI TY. JEE-Mathematics. Illustration 1 : Solution : Illustration 2 : 1. CONTINUOUS FUNCTIONS :

CONTINUI TY. JEE-Mathematics. Illustration 1 : Solution : Illustration 2 : 1. CONTINUOUS FUNCTIONS : J-Mathematics. CONTINUOUS FUNCTIONS : CONTINUI TY A fuctio for which a small chage i the idepedet variable causes oly a small chage ad ot a sudde jump i the depedet variable are called cotiuous fuctios.

More information

Designing a learning system

Designing a learning system CS 75 Machie Learig Lecture Desigig a learig system Milos Hauskrecht milos@cs.pitt.edu 539 Seott Square, x-5 people.cs.pitt.edu/~milos/courses/cs75/ Admiistrivia No homework assigmet this week Please try

More information

CS 111: Program Design I Lecture 21: Network Analysis. Robert H. Sloan & Richard Warner University of Illinois at Chicago April 10, 2018

CS 111: Program Design I Lecture 21: Network Analysis. Robert H. Sloan & Richard Warner University of Illinois at Chicago April 10, 2018 CS 111: Program Desig I Lecture 21: Network Aalysis Robert H. Sloa & Richard Warer Uiversity of Illiois at Chicago April 10, 2018 NETWORK ANALYSIS Which displays a graph i the sese of graph/etwork aalysis?

More information

Lecture Notes 6 Introduction to algorithm analysis CSS 501 Data Structures and Object-Oriented Programming

Lecture Notes 6 Introduction to algorithm analysis CSS 501 Data Structures and Object-Oriented Programming Lecture Notes 6 Itroductio to algorithm aalysis CSS 501 Data Structures ad Object-Orieted Programmig Readig for this lecture: Carrao, Chapter 10 To be covered i this lecture: Itroductio to algorithm aalysis

More information

( n+1 2 ) , position=(7+1)/2 =4,(median is observation #4) Median=10lb

( n+1 2 ) , position=(7+1)/2 =4,(median is observation #4) Median=10lb Chapter 3 Descriptive Measures Measures of Ceter (Cetral Tedecy) These measures will tell us where is the ceter of our data or where most typical value of a data set lies Mode the value that occurs most

More information

OCR Statistics 1. Working with data. Section 3: Measures of spread

OCR Statistics 1. Working with data. Section 3: Measures of spread Notes ad Eamples OCR Statistics 1 Workig with data Sectio 3: Measures of spread Just as there are several differet measures of cetral tedec (averages), there are a variet of statistical measures of spread.

More information

A New Morphological 3D Shape Decomposition: Grayscale Interframe Interpolation Method

A New Morphological 3D Shape Decomposition: Grayscale Interframe Interpolation Method A ew Morphological 3D Shape Decompositio: Grayscale Iterframe Iterpolatio Method D.. Vizireau Politehica Uiversity Bucharest, Romaia ae@comm.pub.ro R. M. Udrea Politehica Uiversity Bucharest, Romaia mihea@comm.pub.ro

More information

Math 10C Long Range Plans

Math 10C Long Range Plans Math 10C Log Rage Plas Uits: Evaluatio: Homework, projects ad assigmets 10% Uit Tests. 70% Fial Examiatio.. 20% Ay Uit Test may be rewritte for a higher mark. If the retest mark is higher, that mark will

More information

Message Authentication Codes. Reading: Chapter 4 of Katz & Lindell

Message Authentication Codes. Reading: Chapter 4 of Katz & Lindell Message Autheticatio Codes Readig: Chapter 4 of Katz & Lidell 1 Message autheticatio Bob receives a message m from Alice, he wats to ow (Data origi autheticatio) whether the message was really set by Alice.

More information

The Adjacency Matrix and The nth Eigenvalue

The Adjacency Matrix and The nth Eigenvalue Spectral Graph Theory Lecture 3 The Adjacecy Matrix ad The th Eigevalue Daiel A. Spielma September 5, 2012 3.1 About these otes These otes are ot ecessarily a accurate represetatio of what happeed i class.

More information

Orientation. Orientation 10/28/15

Orientation. Orientation 10/28/15 Orietatio Orietatio We will defie orietatio to mea a object s istataeous rotatioal cofiguratio Thik of it as the rotatioal equivalet of positio 1 Represetig Positios Cartesia coordiates (x,y,z) are a easy

More information

Performance Plus Software Parameter Definitions

Performance Plus Software Parameter Definitions Performace Plus+ Software Parameter Defiitios/ Performace Plus Software Parameter Defiitios Chapma Techical Note-TG-5 paramete.doc ev-0-03 Performace Plus+ Software Parameter Defiitios/2 Backgroud ad Defiitios

More information

n Some thoughts on software development n The idea of a calculator n Using a grammar n Expression evaluation n Program organization n Analysis

n Some thoughts on software development n The idea of a calculator n Using a grammar n Expression evaluation n Program organization n Analysis Overview Chapter 6 Writig a Program Bjare Stroustrup Some thoughts o software developmet The idea of a calculator Usig a grammar Expressio evaluatio Program orgaizatio www.stroustrup.com/programmig 3 Buildig

More information

CS 683: Advanced Design and Analysis of Algorithms

CS 683: Advanced Design and Analysis of Algorithms CS 683: Advaced Desig ad Aalysis of Algorithms Lecture 6, February 1, 2008 Lecturer: Joh Hopcroft Scribes: Shaomei Wu, Etha Feldma February 7, 2008 1 Threshold for k CNF Satisfiability I the previous lecture,

More information

UNIT 4 Section 8 Estimating Population Parameters using Confidence Intervals

UNIT 4 Section 8 Estimating Population Parameters using Confidence Intervals UNIT 4 Sectio 8 Estimatig Populatio Parameters usig Cofidece Itervals To make ifereces about a populatio that caot be surveyed etirely, sample statistics ca be take from a SRS of the populatio ad used

More information

Chapter 4 The Datapath

Chapter 4 The Datapath The Ageda Chapter 4 The Datapath Based o slides McGraw-Hill Additioal material 24/25/26 Lewis/Marti Additioal material 28 Roth Additioal material 2 Taylor Additioal material 2 Farmer Tae the elemets that

More information

Hash Tables. Presentation for use with the textbook Algorithm Design and Applications, by M. T. Goodrich and R. Tamassia, Wiley, 2015.

Hash Tables. Presentation for use with the textbook Algorithm Design and Applications, by M. T. Goodrich and R. Tamassia, Wiley, 2015. Presetatio for use with the textbook Algorithm Desig ad Applicatios, by M. T. Goodrich ad R. Tamassia, Wiley, 2015 Hash Tables xkcd. http://xkcd.com/221/. Radom Number. Used with permissio uder Creative

More information

n We have discussed classes in previous lectures n Here, we discuss design of classes n Library design considerations

n We have discussed classes in previous lectures n Here, we discuss design of classes n Library design considerations Chapter 14 Graph class desig Bjare Stroustrup Abstract We have discussed classes i previous lectures Here, we discuss desig of classes Library desig cosideratios Class hierarchies (object-orieted programmig)

More information

Visualization of Gauss-Bonnet Theorem

Visualization of Gauss-Bonnet Theorem Visualizatio of Gauss-Boet Theorem Yoichi Maeda maeda@keyaki.cc.u-tokai.ac.jp Departmet of Mathematics Tokai Uiversity Japa Abstract: The sum of exteral agles of a polygo is always costat, π. There are

More information

INSCRIBED CIRCLE OF GENERAL SEMI-REGULAR POLYGON AND SOME OF ITS FEATURES

INSCRIBED CIRCLE OF GENERAL SEMI-REGULAR POLYGON AND SOME OF ITS FEATURES INTERNATIONAL JOURNAL OF GEOMETRY Vol. 2 (2013), No. 1, 5-22 INSCRIBED CIRCLE OF GENERAL SEMI-REGULAR POLYGON AND SOME OF ITS FEATURES NENAD U. STOJANOVIĆ Abstract. If above each side of a regular polygo

More information

Designing a learning system

Designing a learning system CS 75 Itro to Machie Learig Lecture Desigig a learig system Milos Hauskrecht milos@pitt.edu 539 Seott Square, -5 people.cs.pitt.edu/~milos/courses/cs75/ Admiistrivia No homework assigmet this week Please

More information

Basic allocator mechanisms The course that gives CMU its Zip! Memory Management II: Dynamic Storage Allocation Mar 6, 2000.

Basic allocator mechanisms The course that gives CMU its Zip! Memory Management II: Dynamic Storage Allocation Mar 6, 2000. 5-23 The course that gives CM its Zip Memory Maagemet II: Dyamic Storage Allocatio Mar 6, 2000 Topics Segregated lists Buddy system Garbage collectio Mark ad Sweep Copyig eferece coutig Basic allocator

More information

APPLICATION NOTE PACE1750AE BUILT-IN FUNCTIONS

APPLICATION NOTE PACE1750AE BUILT-IN FUNCTIONS APPLICATION NOTE PACE175AE BUILT-IN UNCTIONS About This Note This applicatio brief is iteded to explai ad demostrate the use of the special fuctios that are built ito the PACE175AE processor. These powerful

More information

MULTIBEAM SONAR RECORDS DATA DECIMATION USING HIERARCHICAL SPLINE INTERPOLATION JERZY DEMKOWICZ

MULTIBEAM SONAR RECORDS DATA DECIMATION USING HIERARCHICAL SPLINE INTERPOLATION JERZY DEMKOWICZ MULTIBEAM SONAR RECORDS DATA DECIMATION USING HIERARCHICAL SPLINE INTERPOLATION JERZY DEMKOWICZ Gdask Uiversity of Techology Narutowicza 11/12, 8-233 Gdask, Polad dejot@eti.pg.gda.pl Multibea soar records

More information