1 / 57

Interpolation and elevation

CAGD&CG. Interpolation and elevation. zhu ping 07.10.10. Problem. T 0 ,T 1 ,T 2 ,…,T n ∈R 2 , T i ≠T i+1 Find a curve P (t) : P :[0,1] —> R 2 , satisfying P ( t i )= T i i =0,1,…,n. Paper.

thad
Download Presentation

Interpolation and elevation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. CAGD&CG Interpolation and elevation zhu ping 07.10.10

  2. Problem • T0,T1,T2,…,Tn∈R2 , Ti≠Ti+1 • Find a curve P(t): P:[0,1]—> R2 , satisfying P(ti)=Tii=0,1,…,n

  3. Paper • 1. <<Constructing Iterative Non-Uniform B-spline Curve and Surface to Fit Data Points>> Lin Hongwei Science in China 04 • 2.<<Totally Positive Bases and Progressive Iteration Approximation>> Lin Hongwei Computers and Mathematics with Application 05 • 3.<<Progressive iterative approximation and bases with the fastest convergence rates>> J.Delgado CAGD 07 • 4.<<Geometric interpolation by planar cubic polynomial curves>> Jernej Kozak CAGD 07 • 5.<<An alternative method of curve interpolation>> Les A.Piegl The Visual Computer 05 • 6.<<On the degree elevation of B-spline curve and corner cutting>> Wang Guozhao CAGD07 • 7.<<Interpolation by geometric algorithm>> Takashi Maekawa CAD 07

  4. Constructing Iterative Non-Unifrom B-spline Curve and Surface to Fit Data Points • Problem:Progressive iterative approximation • Proposed by Prof.Qi Dongxu and de Boor • In 1991, cubic uniform B-spline and convergence • Shortage:NURBS,convexity-preserving,explict expression

  5. Iterative algorithm (1) parameter (2)normal

  6. Parameter

  7. Normal

  8. Deduce:

  9. Iterative error:

  10. Iterative convergence

  11. Convexity-preserving algorithm Polyline L is convexity and if sgn(αi)=sgn(βi) polyline L’is also convexity

  12. Iterative formulae

  13. Surface iterative

  14. Example: 30 times convexity-preserving property of non-uniform B-spline curve

  15. Totally Positive Bases and Progressive Iteration Approximation • Totally Positive Bases Definition: Given a basis {Bi(t) ≥0|i=0,1,…,n} , an increasing sequence the collocation matrix is Basis {Bi} is totally positive basis if the matrix is a totally positive matrix

  16. Theorem. If the basis is totally positive and its collocation matrix B at {t0,t1,…,tn} is non-singular,the curve has progressive iteration appromation. Proof.

  17. Example: Bezier,B-spline and NUBRS curve and surface Bezier 20 times 60 times

  18. Progressive iterative approximation and bases with the fastest convergence rates • Outline: The normalized B-basis has optimal shape preserving properties and we prove that it satisfies the progressive iterative approximation property with the fastest convergence rates. A similar result for tensor product surfaces is also derived.

  19. Theorem: Given a space U with an TP basis,the normalized B-basis of U provided a progressive iterative approximation with the fastest convergence rates among all TP bases of U.

  20. Proof. will be minimum when the smallest eigenvalue of B is maximum. U:unique normalized B-basis (b0,…,bn),TP basis (v0,…,vn) . (v0,…,vn)=(b0,…,bn)K . K is a stochastic TP matrix. prove that the smallest eigenvalue of B is greater than the smallest eigenvalue of V

  21. Consider matrix since is similar to is similar to .We should prove

  22. Tensor product surfaces: • Kronecker product:

  23. Numerical test: Bernstein Basis VS Said-Ball Basis Definition:

  24. Comparion:

  25. Error compare:

  26. Geometric Method for Hermite Iterpolation by a class of PH Quintics • Outline: Geometric relationship among Bezier control points of PH Quintics is developed.And choose the best curve in the resulant PH quintics.

  27. Theorem 1: A Bezier curve is PH curve when two points A,B exist making

  28. Hermite Method:

  29. On the degree elevation of B-spline curves and corner cutting • Outline: Our idea is making bi-degree B-spline to elevate B-spline. Old Method:splite into pieces of Bezier curves

  30. Theorem 1: , are defined and Theorem 2:

  31. Property:positivity,partition of unity,linear independence globally • Theorem 3:

  32. An alternative method of curve interpolation • Author: Les A.Piegl, South Florida University, research in CAD/CAM,geometric modeling,computer graphics Wayne Tiller,in GeomWare, The NURBS Book

  33. Outline:given a point data set that contains several fairly unevently distributed random points,this paper presents a new paradigm of curve interpolation to fit a curve to the data with end tangent vector constraints.

  34. Major components: 1. Base curve 2. Localization 3. Constrained shape manipulation 4. Parametrization adjustment

  35. Definition:a B-spline curve of degree p

  36. Constrained shaping:

  37. Parametrization: 1.Obtain a polygonal aproximation of the base curve 2.Project all points onto the polygon and find the closest vertex or polygon leg 3.Obtain an approximation parameter from the parameters of the closest leg or vertex

  38. Geometric decomposition: 1.Decompose the NURBS curve into piecewise Bezier segments 2.Bezier curve decomposition

  39. Location: Greville abscissae: degree p degree p-1

  40. Procedure: 1.Go through the brackets (ηi,ηi+1) i=1,…,n-1 to see how many parameters fall within the brackets 2.If there are two or more parameters,insert a knot and recomputer the brackets 3.Repeat the process until each bracket contains no more than one parameter

  41. Let w∈ (ηi,ηi+1) be the new abscissa

  42. The algorithm: 1.Computer the Grevile brackets 2.Check end conditions and insert knots if necessary 3.While there are brakets with more than one parameter 3.1 Find first offending span 3.2 Average parameters lying inside the span.The average is to become the new Greville abscissa 3.3 Get the knot to be inserted 3.4 If the knot falls on an existing knot,select another parameter representative 3.5 Insert the knot and recompute Greville brackets

  43. Base curve:two methods (1)1.Obtain a local cubic C1 interpolation to the points. 2.Sample the interpolant 3.Approximate the smple points by least-squares.

More Related