B-spline curve fitting based on dynamic adjustment of knot vector using feature points
Xiaobing Chen, Shuxin Guo, Rongrong Wang, Chuangchuang Zhang, Jianchu Lin, Shang Chen
TL;DR
This paper introduces a new B-spline curve fitting method that improves accuracy and efficiency by dynamically adjusting knot vectors using feature points.
Contribution
A novel algorithm for B-spline curve fitting with dynamic knot vector adjustment based on feature points.
Findings
The proposed method achieves higher fitting accuracy compared to traditional approaches.
It reduces the number of control points and fitting time while maintaining precision.
Abstract
An essential challenge in B-spline curve fitting is how to produce a B-spline curve that satisfies the accuracy requirement with a minimal number of knots and control points. This paper suggests a better algorithm based on feature points method. During the curve approximation process, the projection points of data points and their parameters are calculated, and the data point parameters are corrected to achieve dynamic adjustment of the knot vector. At the same time, traditional methods are improved in terms of initial feature point selection and new feature points determination. The experimental results indicate that the B-spline curve produced using the method in this work has higher fitting accuracy, fewer control points, and shorter fitting time.
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Taxonomy
TopicsAdvanced Numerical Analysis Techniques · Image and Video Stabilization · Image and Object Detection Techniques