Feature Extraction Algorithms in Graphics and Image Processing: Zernike Moments

In graphics and image processing, feature extraction algorithms play a vital role. One commonly used extraction algorithm is Zernike moments. Zernike moments are mathematical tools used to describe shape and texture characteristics of images. Through image transformation and computation, they generate a series of moment values that represent various image features. These features can be applied in image recognition, image matching, image classification, and other application domains. From an implementation perspective, Zernike moments are calculated using orthogonal Zernike polynomials over a unit disk. The algorithm typically involves: 1. Image preprocessing and coordinate normalization 2. Computation of radial polynomials 3. Calculation of complex moments using integration over circular domains 4. Extraction of magnitude and phase information for feature representation Key functions in implementation include radial polynomial computation, moment coefficient calculation, and feature vector normalization. The orthogonal nature of Zernike polynomials provides rotation invariance and minimal information redundancy, making them particularly valuable for pattern recognition tasks. Therefore, mastering and applying Zernike moment extraction algorithms holds significant importance for both research and practical applications in graphics and image processing.