Rabia Hameed
Government Sadiq College Women University
13 Papers
37 Citations
Rabia Hameed is an academic researcher from Government Sadiq College Women University. The author has contributed to research in topics: Subdivision & Polygon. The author has an hindex of 3, co-authored 13 publications. Previous affiliations of Rabia Hameed include Islamia University & The Islamia University of Bahawalpur Pakistan.
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Papers
Families of univariate and bivariate subdivision schemes originated from quartic B-spline
Ghulam Mustafa,Rabia Hameed +1 more
TL;DR: Families of parameter dependent univariate and bivariate subdivision schemes are presented, new variants of the Lane-Riesenfeld algorithm, that can be nicely generalized to contain local shape parameters that allow the user to adjust locally the shape of the limit curve/surface.
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Family of a-point b-ary subdivision schemes with bell-shaped mask
Rabia Hameed,Ghulam Mustafa +1 more
TL;DR: A generalized Refine-Smooth algorithm is presented to design a family of a-point b-ary approximating subdivision schemes with bell-shaped mask and it is observed that the proposed family is suitable for fitting the locally noisy, oscillatory, and irregular data.
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Construction and Analysis of Binary Subdivision Schemes for Curves and Surfaces Originated from Chaikin Points
Rabia Hameed,Ghulam Mustafa +1 more
TL;DR: In this article, a new variant of Lane-Riesenfeld algorithm for curves and surfaces both is presented, which is the modification of Chaikin/Doo-Sabin subdivision operator, while each smoothing operator is the weighted average of four/sixteen adjacent points.
A Class of Refinement Schemes With Two Shape Control Parameters
TL;DR: A class of combine refinement schemes with two shape control parameters is presented and can be considered as the generalized version of some of the interpolating and B-spline schemes.
Families of non-linear subdivision schemes for scattered data fitting and their non-tensor product extensions
TL;DR: Families of non-linear subdivision schemes are presented that are based on univariate polynomials up to degree three by using dynamic iterative re-weighed least squares method, which shows that these schemes have the ability to reproduce polynmials and do not cause over and under fitting of the data.
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