MATLAB Implementation of Image Feature Extraction Using Moment Invariants and Hu Moments

In image processing, image feature extraction represents a fundamental technique designed to extract meaningful information from images, enabling computers to analyze and recognize visual content effectively. During the feature extraction process, moment invariants and Hu moments serve as two widely adopted algorithms. Moment invariants function as mathematical tools characterizing image shapes and geometric features, while Hu moments constitute a mathematical algorithm for image recognition that extracts seven distinct features (f1 through f7). These features prove particularly valuable for enhancing computer vision systems' object identification accuracy, making them essential across numerous applications.

From an implementation perspective, MATLAB provides efficient functions for calculating these features. The regionprops function can compute basic geometric moments, while specialized implementations of Hu moments typically involve central moment calculations followed by nonlinear combinations to achieve rotation, scale, and translation invariance. The seven Hu moments (f1-f7) derive from normalized central moments through specific algebraic relations, where f1 represents the sum of variances, f2 captures covariance-like properties, and higher-order moments characterize more complex shape attributes. Proper implementation requires preprocessing steps like image binarization and centroid calculation to ensure moment stability across different object orientations and sizes.