1. The use of line integrals makes the proposed approach inherently robust to noise. Furthermore, the accuracy of curvature estimation is significantly improved by using wild bootstrapping to adaptively adjusting the data window for line integral. Compared to existing approaches, this new method promises enhanced performance, in terms of robustness and accuracy, as well as low
computation cost. A number of numerical examples using synthetic noisy and noiseless data clearly demonstrated the advantage
2. Curvature estimation algorithm, we evaluate line integrals over a window whose size is adaptively determined using the wild bootstrap procedure. The performance advantage of this proposed adaptive window curvature estimation algorithm has been examined analytically, and has been validated using several numerical experiments.
computation cost. A number of numerical examples using synthetic noisy and noiseless data clearly demonstrated the advantage
2. Curvature estimation algorithm, we evaluate line integrals over a window whose size is adaptively determined using the wild bootstrap procedure. The performance advantage of this proposed adaptive window curvature estimation algorithm has been examined analytically, and has been validated using several numerical experiments.
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