An Edge-Based Formulation for the Exact Computation of High-Order Zernike Moments of 2D Shapes and Images

What the math gives to ML

The paper provides an exact, boundary-based alternative to pixel-center quadrature for computing polynomial shape moments. Its transferable asset is the Green-theorem pattern: convert an area integral of a polynomial feature into sums of low-dimensional boundary integrals, eliminating discretization aliasing while preserving differentiability with respect to vertex coordinates. This suggests a geometry-aware neural front-end for polygonal masks, contours, vector graphics, and differentiable segmentation outputs, where exact high-order moments can serve as stable inputs or auxiliary losses. The likely payoff is improved robustness to resolution and rasterization changes rather than a universal replacement for convolutional image features.