1. Introduction
Shepard-type operators are rational, positive operators widely used in classical approximation theory and in scattered data interpolation problems (see, e.g., [
1,
2,
3,
4,
5,
6,
7]). In particular, they achieve approximation results not possible by polynomials and relative pointwise and uniform direct and converse results and simultaneous approximation statements can be found, for example, in [
5,
6,
7,
8]. In [
9], a modification of Shepard-type operators according to Gupta variants (based on a double summation depending on a positive parameter
) was introduced and studied. Its asymptotic behavior approaching piecewise constants made it suitable for applications to compression of piecewise images but not to CAGD. Recently, in [
1], Shepard-type operators have been studied to construct Shepard-type curves useful in CAGD. Such curves overcome some of the original Shepard operator’s drawbacks and have some advantages with respect to the Bézier case. A progressive iterative approximation (PIA in short) technique for Shepard-type curves and its weighted generalization (WPIA in short) were also developed in [
1]. Just by adjusting the control points iteratively, PIA process presents an intuitive and straightforward way to fit data points. It generates a curve sequence with finer and finer precision and the limit of the sequence interpolates the data points. An extension to surfaces case was also investigated in [
1]. Such procedure is faster than analogous PIA technique for Bézier, B-Splines, and NURBS curves (see e.g. [
10,
11]). Applications of PIA techniques to image super-resolution, video data compression, online handwritten synthesis, implicit curve reconstruction, fairing curve generation, local modeling, offset of planar curves, conversion of rational curves into nonrational curves, and least squares fitting are also studied [
12,
13,
14,
15,
16,
17,
18,
19,
20].
In [
1,
21], generalizations of PIA technique at a quicker convergence rate, but at a higher computational cost, were introduced; however, they are not always interesting from the modeling point of view: indeed, the designer’s purpose is to draw a pencil of curves shaping the given control polygon, so a too fast sequence of curves is not always useful.
Hence, it is interesting to construct alternative PIA techniques for Shepard-type curves, at suitable convergence rate and computational cost.
The aim of this paper is to give a positive answer to this problem, introducing variants of PIA process with such properties. In
Section 2, a preliminary critical revisitation of PIA technique is presented, pointing out matrix formulation and properties, error expressions, computational aspects, and interesting cases. Such analysis is extended in
Section 3 to four variants of PIA process based on simple iterative procedures approximating inverse of collocation matrix for Shepard-type operators at same computational cost as original PIA method; matrix formulations, convergence results, algorithmic considerations, error estimates, particular cases, and critical comparisons are shown. By acting on iteration level, like shape handle, the designer can choose among novel intermediate silhouettes between original Shepard-type curve and global interpolating Shepard-type curve at proper speed.
In
Section 4 by truncated wavelet transform and PIA technique, we construct a procedure to generate Shepard-type curves useful in CAGD modeling that enhances shaping capabilities of the analogous format by truncated Fourier transform considered in [
2]. We consider the number of base wavelet transform functions and the processing degree of PIA format as shape tools to draw novel curves going from Shepard-type curve to interpolating Shepard-type curve.
To prove the main results, we used the eigenstructure of Shepard-type operators, new numerical approximations of the inverse of collocation matrix and truncation wavelet transform.
Theoretical results are confirmed by numerical tests in
Section 5.
2. PIA Format for Shepard-Type Curves
First, we examine the PIA technique by introducing Shepard-type curves [
1]. Let
with
where
,
,
,
,
,
and
even.
If
,
,
,
is the given control vector, we define a parametric Shepard-type curve
as
Such curves have properties that are interesting in CAGD (see, e.g., [
1]):
is a rational curve that preserves points. It lies in the convex hull of the control polygon defined by
P. It satisfies the pseudolocal control property—i.e., all base functions
,
reach their maximum near to 1 for
or equivalently each point
deeply affects the silhouette of the curve close to
In addition, Shepard-type curves have the property of symmetry, i.e.,
with
The presence of
at the denominators in (
1) makes
a curve near-interpolating the control points, overcoming the flat spots drawback affecting the original Shepard operator (corresponding to
case).
In [
1], a PIA technique for Shepard-type curves was introduced and studied. Starting with an initial Shepard-type curve, PIA process constructs a sequence of fitting curves by adjusting the control points iteratively. The limiting curve is the global Shepard-type curve interpolating the data set, defined by
with
In details, given the control vector
and the basis
,
, defined by (
1), we generate the initial curve
with
,
. Then, we calculate the remaining curves of the sequence
, for
, as follows
with
and
the adjusting vectors given by
Then, the iterative process can be written in matrix form as follows:
with
B, the collocation matrix of
basis, i.e.,
We remark that
B is a positive, centrosymmetric, stochastic, diagonally dominant matrix (see [
1,
21]). Since
B is strictly diagonally dominant, it is invertible. Since
B is stochastic, we deduce that the eigenvalues of
B are positive and less or equal 1, hence the spectral radius of matrix
, i.e.,
is less than 1. Moreover, from the strict positivity of entries of stochastic matrix
B, it follows that
, with
, the row eigenvector associated with eigenvalue 1, which is unique.
We say that
curves defined by (
3) satisfy the PIA property, iff
,
.
Theorem 1. Since , curves satisfy the PIA property, i.e., The following relations are useful in implementing above process Proof. Relations (
6) descend from (
3) and (
4).
Now, we prove (
7). From (
6)
Now, we prove (
8). From (
7), we have
From
and (
8)
hence
i.e., curves
satisfy the PIA property, as already proved in [
1]. □
Remark 1. We remark that at each step, a matrix multiplication is required. Moreover, we observe that the above PIA process can be interpreted in terms of classical Richardson method for systems of linear equations (see [1]). Note that the PIA process is based on well-known approximation for
since
We observe that in the one-dimensional case, we find back an iterative procedure to approximate
,
i.e., to solve nonlinear equation
by a chord method, namely
The first three iterations are
In [
1,
21], faster converging modifications of the above technique were introduced (at higher computational cost), so that the data points are reached in a few iterations. This result is not always interesting in CAGD modeling; indeed, from one hand, the designer’s purpose is to construct intermediate shapes outlining given control polygon; from the other hand, the original PIA process is already fast enough (cfr. [
1]). Hence, it is useful to construct variants of PIA format at a suitable convergence rate and computational cost.
4. A PIA-Type Technique via Truncated Wavelet Transform
In [
2], a method to construct new Shepard-type curves with good shaping behaviour was introduced by PIA algorithm and truncated DFT. The number of base Fourier functions and the iteration level of PIA algorithm handle modeling the outline of Shepard-type curve, getting as a limit case the Shepard-type interpolating curve given by (
2). The truncation procedure was made in analogy with truncation occurring in some statistical contexts involving Fourier transform.
Here, we extend such technique to wavelet transform. As a representation of a function, we consider an orthonormal basis of functions obtained from dilations and translations of compactly supported and periodic scaling (
) and wavelet (
) functions:
where
and
is a reference level.
As a well-known method (e.g., [
23]), wavelet transform has excellent properties from the approximation point of view for wide classes of functions. In addition, it turns to have a sparse representation, in the sense that its coefficients quickly drop to 0. Therefore, a limited set of coefficients could be sufficient to accurately represent the function to be approximated. Further, orthogonality of the transform makes it very easy to compute inverse transforms. Finally, availability of a multiresolution algorithm for discrete data allows one to compute the discrete wavelet transform very quickly with a computational cost of
operations. In this case of discrete data, the discrete wavelet wransform,
of a set of dyadic data
z of length
n can be expressed as
, with
W being the wavelet matrix of order
n.
Therefore, the idea is to compute the wavelet transform of the discrete points and, before inverse transforming them, to threshold them with a decreasing threshold, retaining only coefficients higher than the threshold (in absolute value). Depending on the values of the threshold, a family of curves is obtained approximating the data.
We sketch the procedure. From the global interpolating Shepard-type curve Formula (
2) and by (
5)
or equivalently
From (
2) and (
29), putting
with
W the wavelet transform matrix giving back the wavelet coefficients
. Now let us retain only the highest
k,
, wavelet coefficients
(in absolute value). This corresponds to select only
k bases of the transform corresponding to the selected wavelet coefficients. Equivalently, in matrix notation we can consider truncated wavelet matrices
that include only rows corresponding to the retained wavelet coefficients. Analogously to the FT format introduced in [
2], such truncation procedure was inspired by some statistical problems involving wavelets.
Then, we introduce the truncated interpolation curve of Shepard-type:
From (
30) it follows that, when
,
and we deduce (
2). Therefore, when gradually
in (
30) we get different curves going closer and closer to the interpolating Shepard-type curve.
From (
30) and above PIA format, we deduce a technique constructing a pencil of curves of Shepard-type outlining the given data points. We summarize the process. In (
30), matrix
V is replaced by
with
m being the iteration level of PIA procedure. From (
9)
because of fast convergence, with a few iterations
can get close to
V.
So, if the designer plays with the number
k of basis wavelet functions in (
30) and the processing degree
m of the PIA algorithm in (
31), intermediate shapes not attainable by PIA procedure are drawn.