Robust and accurate curvature estimation using adaptive line integrals

Authors:
Wei-Yang Lin;Yen-Lin Chiu;Kerry R. Widder;Yu Hen Hu;Nigel Boston
Affiliations:
Department of CSIE, National Chung Cheng University, Min-Hsiung, Chia-Yi, Taiwan;Telecommunication Laboratories, Chunghwa Telecom Co., Ltd., Yang-Mei, Taoyuan, Taiwan;Department of ECE, University of Wisconsin-Madison, Madison;Department of ECE, University of Wisconsin-Madison, Madison;Department of ECE, University of Wisconsin-Madison, Madison
Venue:
EURASIP Journal on Advances in Signal Processing
Year:
2010

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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.