MATLAB Implementation of Gaussian Background Modeling with Code Examples
A comprehensive MATLAB implementation of Gaussian background modeling featuring algorithm explanations and practical code demonstrations for computer vision applications.
Explore MATLAB source code curated for "matlab代码" with clean implementations, documentation, and examples.
A comprehensive MATLAB implementation of Gaussian background modeling featuring algorithm explanations and practical code demonstrations for computer vision applications.
Source code for Naive Bayes classification implemented in MATLAB, featuring probability calculations and class prediction with detailed implementation insights
Track Association Algorithms for Target Tracking with MATLAB Implementation
MATLAB program for steepest descent gradient method - a gradient-based optimization algorithm that iteratively minimizes functions. Originally sourced from Science Research China platform.
MATLAB code for determining time series order through FPE (Final Prediction Error) or AIC (Akaike Information Criterion) criteria, including algorithm explanations and key function usage.
MATLAB code for discrete control system design, featuring comprehensive control learning algorithms including feedback control, feedforward control, and hybrid control implementations
This code demonstrates DCT transformation using MATLAB, showing signal processing effects through frequency domain conversion with dct() function implementation.
Implementing Gaussian integral computation using MATLAB. This serves as a fundamental example for learning advanced integration programming techniques, demonstrating numerical integration implementation with Gaussian quadrature methods.
This MATLAB M-file implements the Fast Fourier Transform (FFT) algorithm for efficient frequency domain analysis, accompanied by a detailed PDF documentation explaining Fourier transform principles and applications with practical examples.
Random network generation code implemented in MATLAB, capable of creating fundamental random network structures with configurable parameters for network analysis.