Explore MATLAB source code curated for "均值" with clean implementations, documentation, and examples.
This MATLAB implementation solves parameter estimation for Gaussian Mixture Models using the Expectation-Maximization (EM) algorithm. The program modularly separates mean, covariance, and weight estimation into independent functions saved as .M files. The main execution point is through main.m, with sample data provided in spreadsheet format for immediate testing and customization.
MATLAB implementation for function normalization to achieve zero mean and unit variance, enabling standardized data preprocessing for comparative analysis.
Gabor features including mean and variance with concise interface functions, extremely practical for implementation!
Generating Gaussian white noise with zero mean using MATLAB for editing and simulation purposes, including implementation methods and key function descriptions.
This MATLAB-based program implements the extraction of various parameter features essential for histogram analysis, including mean, variance, skewness, kurtosis, energy, and entropy. The code utilizes built-in statistical functions and custom calculations to accurately quantify distribution characteristics.
MATLAB-based implementation of the Expectation-Maximization algorithm for estimating means, variances, and weights in Gaussian Mixture Models, featuring iterative optimization and parameter estimation capabilities.
1. CENTER: Calculates the mean required for Gaussian model establishment, typically using sample data averaging in color spaces like YCbCr or HSV. 2. CHROMADIST: Plots skin color point distribution for visual analysis of chrominance characteristics. 3. COLORDISTPLOT: Visualizes Gaussian model parameters through probability density plots. 4. FACE: Direct RGB space segmentation (discontinued due to poor performance under varying lighting). 5. ZHIFANGTU: Grayscale histogram analysis for luminance distribution examination. 6. SKINMODEL: Constructs probabilistic skin color model using Gaussian mixture models. 7. SEGMENTSKIN: Performs skin segmentation with region labeling, area filtering, and aspect ratio validation. 8. Supporting subroutines for preprocessing and postprocessing operations.
Generating narrowband random processes based on Rice representation of stochastic processes, including mastering their key characteristics such as mean (mathematical expectation), variance, correlation function, and power spectral density through implementation approaches involving random variable generation and spectral analysis techniques.
Calculating entropy from four directional gray-level co-occurrence matrices (GLCM), deriving mean entropy values to construct feature matrices, and implementing texture segmentation through cluster analysis for image processing applications.
This experimental program computes various parameters of vibration signals, implementing key statistical and signal processing calculations including mean value, standard deviation, variance, skewness, kurtosis, peak-to-peak value, root mean square amplitude, average amplitude, peak value, waveform factor, crest factor, impulse factor, and margin indicator using efficient numerical algorithms.