MATLAB Code Implementation for Circle Fitting

In the field of data analysis, circle fitting represents a fundamental data fitting methodology. This technique processes multiple known data points to determine the circle that minimizes the fitting error. The implementation typically involves solving a least-squares optimization problem, often utilizing MATLAB functions like lsqnonlin or implementing algebraic methods such as the Kasa or Pratt algorithms. Circle fitting finds extensive applications across computer vision, image processing, and statistical analysis domains. In these fields, the method is commonly employed to identify optimal circular patterns for shape analysis or object tracking purposes. The MATLAB implementation generally includes data preprocessing, parameter initialization, iterative optimization, and residual analysis to ensure numerical stability. Furthermore, circle fitting serves as a valuable tool for experimental data modeling and prediction, enabling researchers to better understand and interpret complex datasets. The algorithm typically handles both complete and partial circular arcs, with robustness against outlier points through techniques like RANSAC integration. Overall, circle fitting constitutes an essential analytical instrument that facilitates deeper data comprehension and utilization. MATLAB's computational capabilities make it particularly suitable for implementing efficient circle fitting routines with visualization tools for result validation.