Feature Extraction and Data Dimensionality Reduction Using MATLAB
Feature Extraction and Data Dimensionality Reduction Implementations Using MATLAB Algorithms
EEG Processing Pipeline
Key procedures in EEG signal processing including preprocessing, feature extraction, and classification algorithms with implementation approaches
Tamura Texture Feature Extraction Program
Tamura texture feature extraction program implemented in MATLAB, thoroughly tested and verified. Includes a supplementary PDF document explaining Tamura texture feature definitions to facilitate understanding and usage.
Feature Extraction for Human Body Detection
Implementation of feature extraction for human body detection using MATLAB, achieving good performance with potential for simple modifications and enhancements
Egg Shape Feature Extraction
MATLAB-based program for egg shape feature extraction, capable of extracting shape characteristics of cracked egg regions using digital image processing techniques
MATLAB for Neural Electrical Signal Analysis
Feature extraction and classification of time-series EMG signals using AR modeling techniques with MATLAB implementation
MATLAB Implementation of HOG Feature Extraction
HOG Feature Extraction Implementation - Successfully Tested with Code Examples. This resource provides practical implementation insights to assist developers in computer vision applications.
Several Useful Algorithm Programs for Feature Extraction in EEG Processing
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.
Pattern Recognition Course Project with Multiple Algorithm Implementations
My comprehensive pattern recognition project includes implementations of Bayes classification, Fisher linear discriminant analysis, PCA and LDA feature extraction, K-means clustering, and Parzen window algorithm. The code is thoroughly commented and comes with test datasets for validation.
Feature Extraction and Recognition in Face Recognition Systems
A MATLAB-based face recognition program I previously developed for feature extraction and identity matching, which implements core computer vision algorithms for practical applications.