Explore MATLAB source code curated for "特征提取" with clean implementations, documentation, and examples.
An implementation example of hyperspectral image analysis using Stacked Autoencoder (SAE) deep learning approach, featuring both SAE methodology and hyperspectral image feature extraction procedures with code-level implementation insights.
Feature extraction from fault signals using wavelet transform with feature vector construction methodology
Speech signal processing and speech recognition generally involve preprocessing, feature extraction, vectorization, and matching calculation stages. This guide explains how to implement filtering, framing, windowing, and endpoint detection on speech signals using MATLAB, with specific code examples and algorithm descriptions. Note: The Voicebox toolbox (available online) provides essential functions for comprehensive speech processing implementations.
Image Binarization, Discrete Noise Removal, Color Image Grayscale Conversion, Feature Extraction, Grayscaling Methods, and Average Grayscale Calculation for Color Images
The Statistical Pattern Recognition Toolbox includes: 1) Analysis of linear discriminant functions with implementation examples, 2) Feature extraction using Linear Discriminant Analysis (LDA) algorithms, 3) Probability distribution estimation and clustering techniques, 4) Support Vector Machines and other kernel-based methods with practical code demonstrations.
A MATLAB-based implementation for extracting texture features from digital images with code-level algorithm details
Extracting features from flame images, including flame flicker frequency, flame region area, and flame combustion region cropping, with implementation details using computer vision algorithms
Feature extraction from color images for classification purposes, with robust methods and implementation approaches
MATLAB implementation for face detection combining high wavelet feature extraction with neural network classification
PCA algorithm for feature extraction in facial recognition, which is the most widely used traditional facial recognition technique. This method achieves dimensionality reduction by constructing feature subspaces through eigenvalue decomposition of covariance matrices.