In this paper, we present wide-ranging families of subdivision schemes for fitting data to subdivision models. These schemes are constructed by fitting multivariate polynomial functions of any degree to different types of data by least squares techniques. Moreover, we also present the closed analytic expressions of the families of schemes for fitting data in 2 and 3 dimensional spaces. The sche...

The authors propose an algorithm for detection of three-dimensional bundaries in noisy images based on higher-order polynomial surface fitting and directional smoothing. Fitting a polynomial to the local intensities gives the intensity hypersurface. An isointensity surface i s derived from the hyperplane and directional smwthiag is defined as smoothing along this isointensjty surface. The devel...

  Objective: For the assessment of primary arch form, different methods have been used including qualitative classifications, inter-canine and inter-molar widths and quantitative and numerical methods using mathematical models. The purpose of this study was to compare the validity and reliability of Cast Analyzer X Iranian software with those of Curve Expert Professional version 1.1 for arch fo...

The article is intended to introduce and discuss a new quantile regression method for baseline detrending of chromatographic signals. It is compared with current methods based on polynomial fitting, spline fitting, LOESS, and Whittaker smoother, each with thresholding and reweighting approach. For curve flexibility selection in existing algorithms, a new method based on skewness of the residual...

The present study introduces a novel curve-fitting algorithm for surface plasmon resonance (SPR) curves using a self-constructed, wedge-shaped beam type angular interrogation SPR spectroscopy technique. Previous fitting approaches such as asymmetric and polynomial equations are still unsatisfactory for analyzing full SPR curves and their use is limited to determining the resonance angle. In the...

We present a novel polynomial functional neural networks using Distributed Approximating Functional (DAF) wavelets (infinitely smooth filters in both time and frequency regimes), for signal estimation and surface fitting. The remarkable advantage of these polynomial nets is that the functional space smoothness is identical to the state space smoothness (consisting of the weighting vectors). The...