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This presentation by Ying Liu, delivered on November 29, 2007, explores the innovative use of Pythagorean Hodograph (PH) spline curves for curve fitting through evolution-based least-squares methods. It introduces a general framework for curve fitting and discusses the relationship of this method to Gauss-Newton iteration. The talk covers regular and non-regular cases of PH curves and the implications for shape parameters and normal vectors. The effectiveness of this approach is supported by examples of fitting circular arcs and cubic segments, highlighting its practical applications in computational geometry.
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Evolution-based least-squares fitting using Pythagorean hodograph spline curves • Speaker: Ying .Liu • November 29. 2007
www.ag.jku.at Institute of Applied Geometry, Jphannes Kepler University ,Linz, Austira Bert Juttler Martin Aigner
Author: • Martin Aigner: • Dr. Mag., research assistant • Email: martin. aigner @ jku .at • Zbynek Sir: • Dr.; research assistant at FWF-Projekt P17387-N12 • Alumni
Author: Bert Juttler • Selected scientific activities: • Since 2003:associated editor of CAGD • Organizer of various Mini symposia • Member of program committees of numerous conferences • Research interests: • CAGD, Applied Geometry, Kinematics, Robotics, Differential Geometry
Introduction • Using PH spline curves to evoluted fitting a given set of data points or a curve • For example:
Steps: • Introduce a general framework for abstract curve fitting • Apply this framework to PH curves • Discuss the relationship between this method and Gauss-Newton iteration
An abstract framework for curve fitting via evolution • Parameterized family of curves: (s, u)-> • u is the curve parameter • s is the vector of shape parameters • Let s depend smoothly on an evolution parameter t, s( t)=( ) • Approximately compute the limit
An abstract framework for curve fitting via evolution • Each point travels with the velocity: • Normal velocity of the inner points:
An abstract framework for curve fitting via evolution • Assume a set of data points is given. • Let and • Expected to toward their associated data points if then
An abstract framework for curve fitting via evolution • Time derivatives of the shape parameters satisfied the following equation in least-squares sense Necessary condition for a minimum
An abstract framework for curve fitting via evolution • Definition: • A given curve: • a set of parameters U is said to be regular: • A set parameters: that and • Unit normal vectors • That the matrix has a maximal rank
An abstract framework for curve fitting via evolution • Lemma: in a regular case and if all closet points are neither singular nor boundary points, then any solution of the usual least-squares fitting is a stationary point of the differential equation derived from the evolution process
Evolution of PH splines • Ordinary PH curves c (u)=[x ( u) ,y (u)] satisfied the following conditions: • Regular PH curves: let w=1. • The difference : gcd (x’ ( u ),y’ (u)) is a square of a polynomial • called preimage curve
Evolution of PH splines • Proposition: if a regular PH curve c (u) and then: • Smooth field of unit tangent vectors for all u • Parametric speed and arc-length are polynomial functions • Its offsets are rational curves
Evolution of PH splines • Let an open integral B-spline curve, and • Let
Evolution of PH splines • In the evolution we fix the knot vector, so the shape parameters are • the velocity • The unit normals
Evolution of PH splines • The length of PH spline: • The regularization term: • Which forces the length to converge to some constant value
Examples of PH splines evolution • Simple example: • fitting two circular arcs with radius 1. • Two cubic PH segments depending on 8 shape parameters • Initial position: straight line
Initial: two straight segments For the global shape =8, Gradually raised length to 14 Fix end points Insert knots Examples of PH splines
Initial: two straight segments For the global shape =8, Gradually raised length to 14 Fix end points Insert knots Examples of PH splines
Initial: two straight segments For the global shape =8, Gradually raised length to 14 Fix end points Insert knots Examples of PH splines
Initial: two straight segments For the global shape =8, Gradually raised length to 14 Fix end points Insert knots Examples of PH splines
Initial: two straight segments For the global shape =8, Gradually raised length to 14 Fix end points Insert knots Examples of PH splines
Initial: two straight segments For the global shape =8, Gradually raised length to 14 Fix end points Insert knots Example of PH splines
Initial: two straight segments For the global shape =8, Gradually raised length to 14 Fix end points Insert knots Examples of PH splines
Initial: two straight segments For the global shape =8, Gradually raised length to 14 Fix end points Insert knots Examples of PH splines
Examples of PH splines • Initial value by Hermite interpolation • Split data points at estimated inflections
Speed of convergence • Lemma: the Euler update of the shape parameters for the evolution with step h is equivalent to a Gauss-Newton step with the same h of the problem • Provided that
Speed of convergence • Quadratic convergence of the method
Concluding remarks • Least-squares fitting by PH spline cuves is not necessarily more complicated than others • Future work is devoted to using the approximation procedure in order to obtain more compact representation of NC tool paths