Affine-Invariant Moment Features for Affine-Transformed Images

In computer vision and image processing, affine transformation represents a fundamental image manipulation technique. When an image undergoes affine transformation, its shape and angular characteristics become altered. To enable effective comparison and matching across different image versions, it becomes essential to extract stable image features. For images subjected to affine transformations, computational methods involving affine-invariant moments can derive transformation-resistant characteristics. This approach ensures feature invariance, preserving crucial image attributes without distortion even when images experience rotation, translation, scaling, or shearing transformations. The implementation typically involves calculating central moments and normalizing them against scale variations, followed by deriving moment invariants through specific algebraic combinations that cancel out affine effects. Key functions in OpenCV like cv2.moments() provide the foundation for such calculations, while higher-order moments require custom implementation of invariant formulas that maintain consistency across transformed image versions.