Explore MATLAB source code curated for "互信息" with clean implementations, documentation, and examples.
This internationally sourced MATLAB toolbox efficiently computes mutual information and entropy for random variables, featuring probabilistic distribution functions, stochastic process modeling, and signal processing applications with comprehensive documentation.
Implementation guide for computing information entropy and mutual information between discrete time series variables with probability distribution calculations and algorithmic steps
This collection includes several practical algorithm implementations for EEG feature extraction, including wavelet entropy, Lempel-Ziv complexity (LZC) for EEG complexity analysis, and mutual information methods. All programs have been personally tested and verified to work correctly, providing researchers with reliable tools for EEG feature extraction. The implementation incorporates MATLAB-based signal processing techniques with proper parameter optimization for biomedical signal analysis.
A MATLAB-based image registration program utilizing mutual information metric and Particle Swarm Optimization algorithm for enhanced alignment accuracy and performance.
MATLAB source code for image registration using mutual information methods, achieving excellent registration results with ready-to-run implementation featuring optimization algorithms and similarity metrics.
Source code implementation for mutual information-based image registration with Particle Swarm Optimization (PSO) algorithm
Implementation of information entropy and mutual information calculations for two images using MATLAB with code examples and algorithm explanations.
Image registration using mutual information with PSO and Powell optimization algorithms, featuring a GUI interface implemented in proj.m
This is an image registration program based on mutual information, implemented using MATLAB with relatively straightforward code structure and essential algorithm implementation details.
An image registration algorithm using mutual information with optimization combining Powell method and Particle Swarm Optimization for enhanced performance