Zernike Moments Source Code: MATLAB Implementation for Image Analysis

This MATLAB-implemented source code for Zernike moments (also known as Zernike polynomials) offers a comprehensive toolkit for advanced image processing and pattern recognition applications. The implementation efficiently calculates orthogonal moment invariants that are rotationally invariant, making them particularly valuable for shape analysis and feature extraction in digital images. Key algorithmic features include: - Computational efficiency through recursive polynomial calculation methods - Robust normalization techniques for scale and translation invariance - Orthogonal basis functions that minimize information redundancy - Flexible parameter configuration for different moment orders and repetitions The code architecture employs MATLAB's matrix operations for optimal performance, implementing the radial polynomial computation using efficient recurrence relations. The main functions handle image preprocessing, moment calculation, and feature vector generation through systematic coordinate transformation and integration over the unit disk. Zernike moments demonstrate exceptional capability in representing complex shapes with minimal coefficients, achieving high reconstruction accuracy while maintaining noise robustness. This implementation supports applications across diverse fields including astronomical image analysis, biomedical imaging diagnostics, computer vision systems, and industrial inspection. The modular design allows easy customization for specific requirements, with clear documentation on parameter adjustment and extension methodologies. This resource serves as an invaluable foundation for researchers and engineers working on image characterization, pattern classification, and shape recognition systems.