Zernike Moments MATLAB Source Code Implementation

This MATLAB implementation of Zernike moments provides an excellent foundation for image processing tasks. The code efficiently computes orthogonal polynomial moments using numerical integration techniques over the unit disk, making it particularly valuable for applications such as image recognition, feature extraction, and shape analysis. The implementation handles complex-valued Zernike polynomials through recursive computation methods, ensuring numerical stability and computational efficiency. Key functions include radial polynomial calculation using recurrence relations, moment computation through double integration over image coordinates, and normalization procedures for scale and rotation invariance. Zernike moments decompose images into orthogonal components using polynomials defined on the unit circle, providing robust descriptors that are insensitive to noise and geometric transformations. The algorithm's efficiency makes it suitable for real-time applications, with the MATLAB code optimized through vectorized operations and precomputed polynomial coefficients. The source code includes comprehensive error handling for boundary conditions and supports various image formats through MATLAB's image processing toolbox. For researchers and developers working in computer vision, this implementation offers a reliable tool for extracting rotation-invariant features and performing pattern recognition tasks with high accuracy.