个人信息Personal Information
教授
博士生导师
硕士生导师
性别:男
毕业院校:大连理工大学
学位:硕士
所在单位:控制科学与工程学院
办公地点:大连理工大学创新大厦(大黑楼)B612
联系方式:13500757605
电子邮箱:autowxl@dlut.edu.cn
Geometric properties estimation from discrete curves using discrete derivatives
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论文类型:期刊论文
发表时间:2011-08-01
发表刊物:COMPUTERS & GRAPHICS-UK
收录刊物:Scopus、SCIE、EI
卷号:35
期号:4
页面范围:916-930
ISSN号:0097-8493
关键字:Geometric properties; Differential geometry; Discrete function; Discrete derivative; Discrete curve; Convergence analysis
摘要:Accurate geometric properties estimation from discrete curves is an important problem in many application domains, such as computer vision, pattern recognition, image processing, and geometric modeling. In this paper, we propose a novel method for estimating the geometric properties from discrete curves based on derivative estimation. We develop derivative estimation by defining the derivative of a discrete function at a point, which will be called the discrete derivative. Similarly, the second and higher order discrete derivatives at that point are also defined, and their convergence is demonstrated by theory analysis. These definitions of the different order discrete derivatives provide a simple and reliable way to estimate the derivatives from discrete curves. Based on the discrete derivatives, classical differential geometry can be discretized, and the geometric properties are estimated from discrete curves by using differential geometry theory. The proposed method is independent of any analytic curve and estimates the geometric properties directly from discrete data points, which makes it robust to the geometric shapes of discrete curves. Another advantage of the proposed method is the robustness to noise because of the calculation characteristics of the discrete derivatives. The proposed method is evaluated and compared with other existing methods in the experiments with both synthetic and real discrete curves. The test results show that the proposed method has good performance, and is robust to noise and suitable for different curve shapes. (C) 2011 Elsevier Ltd. All rights reserved.