Least Squares Image Matching Algorithm Implementation

This technical documentation presents a least squares image matching algorithm implementation developed by the author in August 2012. The program has undergone rigorous testing and contains extensive in-code annotations to facilitate understanding and practical application. This implementation specializes in precise image registration through mathematical optimization techniques, enabling users to perform accurate pixel correspondence matching between images. The core algorithm utilizes gradient descent optimization to minimize the sum of squared differences between image patches, incorporating features like: - Gaussian pyramid implementation for multi-scale matching - Affine transformation parameter estimation - Convergence criteria for iterative refinement - Residual error analysis for match quality assessment This tool provides researchers and developers with a reliable method for identifying corresponding points across images, supporting subsequent analysis and processing workflows in computer vision applications. The implementation demonstrates practical applications of numerical optimization in image processing, making it a valuable resource for understanding both theoretical concepts and practical implementation details of least squares matching methodologies.