Background:
Curve interpolation is very important in engineering such as computer
aided design, image analysis and NC machining. Many patents on curve interpolation have been
invented.
Objective:
Since different knot vector configuration and data point parameterization can generate
different shapes of an interpolated B-spline curve, the goal of this paper is to propose a novel adaptive
genetic algorithm (GA) based interpolation method of B-spline curve.
Method:
Relying on geometric features owned by the data points and the idea of genetic algorithm
which liberalizes the knots of B-spline curve and the data point parameters, a new interpolation
method of B-spline curve is proposed. In addition, the constraint of a tangent vector is also
added to ensure that the obtained B-spline curve can approximately satisfy the tangential constraint
while ensuring strict interpolation.
Results:
Compared with the traditional method, this method realizes the adaptive knot vector selection
and data point parameterization. Therefore, the interpolation result was better than the traditional
method to some extent, and the obtained curve was more natural.
Conclusion:
The proposed method is effective for the curve reconstruction of any scanned data
point set under tangent constraints. Meanwhile, this paper put forward a kind of tangent calculation
method of discrete data points, where users can also set the tangent of each data point in order to
get more perfect interpolation results.
The linear interpolation method: a sampling theorem approach
