Abstract
A novel circle fitting algorithm is proposed in this paper. The key points of this paper are given as follows: (i) it formulates the circle fitting problem into the special source localization one in wireless sensor networks (WSN); (ii) the multidimensional scaling (MDS) analysis is applied to the data points, and thus the propagator-like method is proposed to represent the circle center parameters as the functions of the circle radius; (iii) the virtual source localization model can be rerepresented as special nonlinear equations of a unique variable (the circle radius) rather than the original three ones (the circle center and radius), and thus the classical fixed-point iteration algorithm is applied to determine the radius and the circle center parameters. The effectiveness of the proposed circle fitting approach is demonstrated using the simulation and experimental results.
Highlights
Circle fitting receives considerable attention because it plays an important role in computer vision, observational astronomy, structural geology, industry inspection, medical diagnosis, Iris recognition, military, security, and so forth [1,2,3,4,5,6,7,8]
We develop a novel circle fitting approach by borrowing the idea from source localization in wireless sensor networks (WSN) [9, 10]
(ii) The multidimensional scaling (MDS) analysis [11] is applied to the data points, and a special covariancelike matrix is constructed
Summary
Circle fitting receives considerable attention because it plays an important role in computer vision, observational astronomy, structural geology, industry inspection, medical diagnosis, Iris recognition, military, security, and so forth [1,2,3,4,5,6,7,8]. Several classical approaches [1,2,3,4,5,6,7,8], including the Hough transform (HT) methods [4, 5] and the least square (LS) approaches [6,7,8], have been developed to solve this problem The former are to carry out a voting procedure in a threedimensional (3D) Hough accumulator space, where every point represents a circle of a certain size. The corresponding coordinate of the local maxima is obtained as the estimated parameters of the circle. The latter attempt to find the parameters of a circle by minimizing an error metric between the primitive and the data points
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