特征值 Resources

Showing items tagged with "特征值"

This project implements a facial recognition system using MATLAB. Unlike traditional approaches that perform simple head-to-head comparisons with limited practical value, this system employs an innovative methodology. The recognition process involves capturing facial data for training to extract unique facial features. During testing, the system processes full upper-body or full-body images by detecting and isolating faces, performing dimensionality reduction, and comparing them against a database. The system outputs identified individuals with their personal information while tracking attendance records. The architecture also supports secondary development for recognizing unknown faces outside the database, enabling alarm-triggering functionality for enhanced security.

MATLAB 220 views Tagged
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This MATLAB program implements ellipse curve fitting using the least squares method through generalized matrix eigenvalue and eigenvector computation. The input variables x and y represent the horizontal and vertical coordinates of sample points respectively. The resulting coefficient matrix contains parameters [a, b, c, d, e, f] for the elliptical equation aX² + bXY + cY² + dX + eY + f = 0.

MATLAB 225 views Tagged
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Implementation of texture image classification using Gray-Level Co-occurrence Matrix (GLCM) feature extraction and k-Nearest Neighbor (k-NN) algorithm. The creat_apprentissage function handles training sample preparation, cooccurence performs GLCM-based feature extraction, knn implements the classification algorithm, and classif executes the final texture image categorization.

MATLAB 216 views Tagged
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The comparehist function calculates the histogram bus distance between two images for similarity comparison. xuefu employs the C-means clustering algorithm for color-based image segmentation, while ccv extracts color coherence vectors from clothing images to facilitate subsequent feature value operations.

MATLAB 226 views Tagged
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The MATLAB Mathematics Handbook Comprehensive Edition provides exhaustive coverage including: matrix operations and fundamental computations, eigenvalue and quadratic form numerical calculations with data analysis, interpolation, fitting and table lookup, numerical solutions for ordinary differential equations and partial differential equations, symbolic computation, integral transforms, Taylor series, probability and statistics, random number generation, probability density calculations for random variables, cumulative probability values (distribution function values) for random variables, frequency tables for positive integers, empirical cumulative distribution function plots, and least squares linear fitting. Additionally covers probability plotting for normal and Weibull distributions, box plots for sample data, adding reference lines to graphs, polynomial curve fitting to existing plots, sample probability plots, and histograms with superimposed normal density curves.

MATLAB 202 views Tagged
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