Hu's Seven Invariant Moments in Image Processing and Feature Extraction

Hu's seven invariant moments play a critical role in image processing, particularly in feature extraction applications. These mathematical functions describe statistical characteristics of images through moment calculations. The implementation first computes central moments by calculating weighted averages of pixel intensities relative to the image centroid. Central moments are then normalized to achieve scale invariance before deriving the seven Hu moments through specific algebraic combinations. These invariant moments enable robust recognition of various image objects including faces, vehicles, and handwritten digits by maintaining consistency across different orientations and sizes. The algorithm processes grayscale images by converting them to binary format when necessary, calculating moment values from m00 to m03, and applying Hu's original formulas for the seven invariants. By leveraging these moment features, developers can significantly improve performance in image processing and pattern recognition tasks, as the moments provide distinctive characteristics that remain stable under geometric transformations.