模式识别 Resources

Showing items tagged with "模式识别"

Application Background Traffic signs play a crucial role in road traffic systems by displaying current road conditions, alerting drivers to potential hazards, enforcing speed regulations, and prohibiting specific maneuvers like turning or parking in special zones. These functions significantly contribute to road safety. Therefore, traffic sign detection and recognition represent vital research areas for preventing accidents and ensuring driver safety. Among traffic signs, prohibition signs (43 types) hold particular importance by restricting specific behaviors. Speed limit signs and no-turn signs are especially critical for safe driving and remain focal points in current traffic sign recognition research.

MATLAB 2208 views Tagged
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This project demonstrates Parzen window method for probability density function estimation in pattern recognition. The complete program workflow includes: 1) Reading height/weight data from FAMALE.TXT into arrays, calculating sample size N1 and window parameters; 2) Processing MALE.TXT similarly to obtain N2 and parameters; 3) Loading test data from Test2.txt into array A with sample size M; 4) Implementing Parzen window density estimation for gender classification; 5) Statistical analysis of classification error rates. The implementation involves kernel function selection and bandwidth optimization for non-parametric density estimation.

MATLAB 255 views Tagged
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This project demonstrates data dimensionality reduction using Discrete Cosine Transform combined with Principal Component Analysis, applicable to pattern recognition tasks like face recognition, palmprint analysis, expression classification, and fingerprint identification. The implementation involves signal transformation followed by feature extraction techniques.

MATLAB 261 views Tagged
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Pattern Recognition MATLAB Toolbox containing approximately 130 pattern recognition programs and algorithms, including implementations for image recognition, speech recognition, and object detection.

MATLAB 219 views Tagged
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One challenge in applying statistical methods to pattern recognition is the dimensionality issue - classification problems are generally simpler in low-dimensional feature spaces than in high-dimensional ones. This leads to dimensionality reduction techniques, where a fundamental approach projects d-dimensional feature space onto a straight line to create one-dimensional space, which is mathematically straightforward. However, the key challenge is ensuring samples remain linearly separable after projection. While linearly separable samples can always find a projection direction maintaining linear separability after dimensionality reduction, Fisher Linear Discriminant specifically determines the optimal projection direction that maximizes separability by maximizing between-class distance while minimizing within-class variance.

MATLAB 230 views Tagged
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