A Short Survey on B-spline and Bezier Methods

Two effective techniques for approximating data in all areas of engineering problems are the B-spline and Bezier methods. They can be used for data interpolation with a few tweaks and enhancements. Although there are various methods for formulating B-splines in the literature, the B-spline equation is defined by a set of independent functions. The number of pairs of data or control points is equal to the number of coefficients in the B-spline equation. The B-spline method's benefit is that most set functions decrease at specific control points. The governing parametric equations can be drawn to produce Bezier curves. Bernstein polynomials are the parametric equations. A Bezier curve passes through the end points but not all of the data points. It is typically used in approximation methods. The Bezier curve can be used for interpolation since it can be made to pass through every control point by taking into account a few complimentary points between the original locations. This procedure produces curves that are smoother and less sinuous. These two approaches handle a number of challenges for comparing the interpolation qualities. The findings demonstrate the strength and resilience of both approaches for interpolating a highly irregular data set. In terms of accuracy, smoothness, and less computations, the Bezier model outperforms B-splines