MATLAB-based Camera Calibration for 2D and 3D Applications

This documentation provides detailed explanations of MATLAB-based camera calibration techniques applicable to both 2D and 3D camera systems. Along with comprehensive documentation, I will provide supporting images and complete source code implementations. Camera calibration represents a crucial process that enables accurate determination of internal camera parameters (focal length, optical center, distortion coefficients) and external parameters (position and orientation relative to the world coordinate system). This process is fundamental for applications in image processing and computer vision domains. The calibration procedure involves multiple systematic steps and technical components including image acquisition from different angles, feature point extraction using corner detection algorithms, camera model derivation through projective geometry, and parameter optimization using least-squares methods. MATLAB's Computer Vision Toolbox provides essential functions such as detectCheckerboardPoints for automatic pattern recognition, estimateCameraParameters for mathematical model fitting, and generateCheckerboardPoints for calibration pattern generation. Through this calibration process, we can significantly enhance the accuracy and stability of image processing and computer vision algorithms by correcting lens distortion and establishing precise spatial relationships. The implementation includes code for handling radial and tangential distortion corrections using distortion coefficients, and coordinate transformations between world points and image pixels. Camera calibration is therefore an essential technique worth mastering, and I hope this documentation assists you in effectively understanding and applying MATLAB-based camera calibration methodologies.