Pseudo-Zernike Moment Transform

In this article, we explore the pseudo-Zernike moment transform and its applications in image processing. Pseudo-Zernike moments represent a specialized type of orthogonal polynomial that can be utilized to compute morphological features of images. By calculating pseudo-Zernike moments through numerical integration over the unit disk, we obtain detailed information about image shape and structure, providing a more comprehensive foundation for image processing and analysis. The implementation typically involves computing complex-valued radial polynomials using recurrence relations and performing double integration over image coordinates. These moments demonstrate rotational invariance properties and can be normalized for scale invariance through coordinate mapping. Furthermore, pseudo-Zernike moments serve as effective features for pattern recognition and image classification tasks, offering new approaches and possibilities for research and applications in the computer vision domain. The computational process generally includes image preprocessing, moment order selection, and feature vector construction for machine learning pipelines.