Journal
EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING
Volume -, Issue -, Pages -Publisher
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1155/2010/240309
Keywords
-
Categories
Funding
- National Science Council, Taiwan [98-2221-E-194-039-MY3]
Ask authors/readers for more resources
The task of curvature estimation from discrete sampling points along a curve is investigated. A novel curvature estimation algorithm based on performing line integrals over an adaptive data window is proposed. 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 both robustness and accuracy, as well as low computation cost. A number of numerical examples using synthetic noisy and noiseless data clearly demonstrated the advantages of this proposed method over state-of-the-art curvature estimation algorithms.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available