Explore MATLAB source code curated for "特征提取" with clean implementations, documentation, and examples.
MATLAB fingerprint recognition code designed for laboratory testing, evaluating the impact of various feature extraction methods on identification performance. Ideal for academic research validation and helpful for beginners and students learning biometric systems implementation.
A comprehensive PCA program for feature extraction and dimensionality reduction, implementing covariance matrix computation and eigenvalue decomposition algorithms. Available for download with complete source code.
MATLAB-based source code implementing Support Vector Machine (SVM) for feature extraction and data classification. Utilizes MATLAB's built-in SVM functions with customizable data types and parameter configurations. The implementation includes flexible data preprocessing and kernel function options suitable for various machine learning applications.
Implementation of feature extraction methods for 1D signal time series using MATLAB, including Independent Component Analysis (ICA) and wavelet packet-based approaches, with code-level implementation insights.
Application Background: This MATLAB-based learning resource focuses on HOG (Histogram of Oriented Gradients) and SIFT (Scale-Invariant Feature Transform) feature extraction methods. The SIFT implementation includes a ready-to-use match function for immediate deployment. Key Technologies: MATLAB programming environment with HOG and SIFT algorithms for image recognition and machine vision applications, featuring practical code implementation examples and algorithm explanations.
Gabor filters are widely used for shape detection and feature extraction applications, such as fingerprint image enhancement. This MATLAB implementation creates a 2D Gabor filter with adjustable frequency and orientation parameters. The core function gaborfilter1 processes input images through customizable frequency (f) and angle (theta) settings, producing multiple filtered outputs for comprehensive texture analysis.
MATLAB-based leaf image analysis system featuring image processing, segmentation algorithms, feature extraction techniques, and classification methods including SVM and neural networks
A custom-developed feature extraction algorithm implementing Empirical Mode Decomposition (EMD) - a method that transforms non-stationary signals into combined stationary components through adaptive signal decomposition.
A comprehensive and reliable program implementing Harris-based feature extraction for palmprint images, utilizing corner detection techniques with robust image processing operations.
Kernel Principal Component Analysis is an enhanced algorithm based on Principal Component Analysis, serving as a nonlinear feature extraction technique that utilizes kernel functions to map data into higher-dimensional spaces for improved pattern recognition.