Robust and Accurate Curvature Estimation Using Adaptive Line Integrals. (15th August 2010)

• Record Type: Journal Article
• Authors: Lin Lin, Wei-Yang Wei-Yang; Chiu Chiu, Yen-Lin Yen-Lin; Widder Widder, Kerry R. Kerry R.; Hu Hu, Yu Hen Yu Hen; Boston Boston, Nigel Nigel
• Other Names: Cetin Cetin A. Enis A. Enis Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2010(2010)
• Publication Date: 2010-08-15
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2010/240309
• Languages: English
• ISSNs: 1687-6172

Abstract

Abstract : 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.