MATLAB Implementation of Camera Calibration Using Sphere Method

In this article, I will provide a comprehensive overview of a camera calibration program implemented in MATLAB. Camera calibration represents a crucial component in the field of machine vision, enabling computers to interpret and perceive real-world images. The core implementation utilizes spherical objects to estimate camera parameters through MATLAB's Computer Vision Toolbox functions, particularly employing the detectCheckerboardPoints function for initial feature detection and the estimateCameraParameters function for parameter calculation. The program implements a robust calibration algorithm that calculates both intrinsic parameters (focal length, optical center, distortion coefficients) and extrinsic parameters (camera position and orientation). This calibration process enables the transformation of 2D image coordinates into 3D world coordinates, facilitating deeper understanding of spatial relationships within captured scenes. The algorithm workflow includes image acquisition, corner detection, parameter estimation, and validation steps. For interested users, I provide accompanying calibration images that demonstrate proper calibration target placement and imaging conditions. These images serve as practical examples for understanding the program's implementation, showing optimal sphere positioning and lighting conditions for accurate parameter estimation. The calibration images can be processed using the provided MATLAB scripts, which include automated image preprocessing and outlier rejection mechanisms. Overall, this camera calibration program serves as a valuable tool for image data analysis and understanding, with practical applications in robotics, 3D reconstruction, and computer vision systems. The implementation features error analysis routines and validation metrics to ensure calibration accuracy, making it suitable for both educational and professional use cases.