Moment Invariant

Moment invariants are mathematical descriptors of image shapes and patterns that remain unchanged under certain transformations like rotation, scaling, and translation. Current research focuses on extending moment invariants to handle more complex transformations, such as rotational motion blur, and developing efficient computational methods for their calculation, including the use of various orthogonal polynomials and novel algorithms for multi-channel data. These advancements improve the robustness and accuracy of image analysis techniques in applications ranging from object recognition and forgery detection to the analysis of multi-channel data like RGB images and vector fields. The development of robust and computationally efficient moment invariants is crucial for improving the performance of numerous computer vision and image processing tasks.