Algorithm Implementation for Pseudo Zernike Moments Calculation

In this article, we provide a comprehensive guide on implementing code to calculate pseudo Zernike moments. We begin by explaining the mathematical foundation of pseudo Zernike moments and their applications in image processing. Pseudo Zernike moments serve as powerful mathematical descriptors for shape characterization in images, widely used in image classification, pattern matching, and object recognition tasks. The implementation section demonstrates how to code pseudo Zernike moments calculation using Python, starting with importing essential libraries like NumPy for numerical computations and OpenCV for image handling. We detail each function's purpose, including radial polynomial computation using recursive algorithms and moment calculation through double integration over the unit disk. The code examples showcase efficient implementation techniques such as pre-computation of factorial values and coordinate normalization. We further discuss testing methodologies using standard image datasets and optimization strategies including vectorization for faster computation and precision enhancement through higher-order moments. Through this guide, you will gain practical skills in implementing pseudo Zernike moments and understand their significant role in advanced image processing applications.