Explore MATLAB source code curated for "特征提取方法" with clean implementations, documentation, and examples.
This document covers various commonly used feature extraction methods for acoustic radiation noise, including waveform envelope features, higher-order spectral features, fractal dimension features, and singular exponent features, with implementation insights for signal processing applications.
MATLAB source code implementations for PCA and ICA - two classical feature extraction methods widely used in image processing, featuring dimensionality reduction and signal separation capabilities
The SURF (Speeded-Up Robust Features) algorithm, as a recently developed feature extraction method, surpasses or approaches previously proposed similar methods in three key aspects: repeatability, distinctiveness, and robustness, while demonstrating significant advantages in computational efficiency. This implementation utilizes the SURF algorithm for image detection, coordinate transformation, and image stitching. The core implementation involves using the Hessian matrix for image detection to identify feature points, followed by refinement through Fast Nearest Neighbor (NN) matching, Random Sample Consensus (RANSAC) algorithm, and Levenberg-Marquardt (LM) parameter optimization for precise feature matching. Finally, coordinate transformation is performed to unify the coordinate systems and achieve seamless image stitching.
MATLAB source code for ICA (Independent Component Analysis), a classical feature extraction method widely used in image processing applications, with implementation details and algorithm explanations
Gray-Level Co-occurrence Matrix (GLCM) and Gray-Level Gradient Co-occurrence Matrix (GLGCM) extraction methods are significant texture feature extraction techniques implemented using MATLAB with code-oriented implementation details.