Zernike Subpixel Recognition Algorithm

In this documentation, I will introduce the Zernike subpixel recognition algorithm and demonstrate its MATLAB implementation. The Zernike subpixel recognition algorithm serves as a powerful tool for image processing and pattern recognition applications. This algorithm enables precise extraction of meaningful information from digital images, making it particularly valuable for applications such as facial recognition, fingerprint identification, and other computer vision tasks. The MATLAB implementation leverages built-in image processing functions and custom Zernike moment calculations to achieve subpixel accuracy. Key implementation aspects include using imread() for image input, creating Zernike polynomial basis functions through iterative calculations, and employing matrix operations for efficient moment computation. The implementation provides flexibility for parameter customization, including the ability to adjust Zernike moment orders and subpixel edge detection thresholds. Mastering both the theoretical foundations and practical MATLAB coding techniques offers significant value for professionals and researchers working in image processing and pattern recognition fields.