A Framework for Moment Invariants
This work addresses a foundational issue in pattern recognition for researchers and practitioners, offering a generalizable framework, though it appears incremental as it builds on long-established concepts of moments.
The paper tackles the problem of computing geometric moment invariants in n-dimensional space by providing a simple and systematic formalism, enabling easier derivation of invariant characteristics for objects under transformations.
For more than half a century, moments have attracted lot ot interest in the pattern recognition community.The moments of a distribution (an object) provide several of its characteristics as center of gravity, orientation, disparity, volume. Moments can be used to define invariant characteristics to some transformations that an object can undergo, commonly called moment invariants. This work provides a simple and systematic formalism to compute geometric moment invariants in n-dimensional space.