Explore MATLAB source code curated for "Gabor小波变换" with clean implementations, documentation, and examples.
A MATLAB-implemented face recognition approach combining Gabor wavelet transform for feature extraction and artificial neural networks for pattern classification, including complete code implementation and algorithmic details.
An image retrieval program based on Gabor wavelet transform, featuring comprehensive modules for feature extraction, feature matching, and result return with algorithmic enhancements for improved accuracy.
Feature Extraction for Input Images Using Gabor Wavelet Transform with Implementation Details
Gabor Wavelet Transform code for local feature extraction demonstrates excellent performance in capturing discriminative patterns
Gabor Wavelet Transform and PCA Face Recognition Code – My Graduation Project Implementation that Combines Multi-orientation Feature Extraction with Dimensionality Reduction for Robust Face Recognition
This MATLAB program implements a comprehensive facial recognition pipeline that first processes face images using Gabor wavelet transformation for feature extraction, then applies PCA (Principal Component Analysis) for dimensionality reduction, and finally employs an SVM (Support Vector Machine) classifier for multi-class recognition. The implementation supports the complete ORL face database and utilizes the libsvm toolbox (version: libsvm-mat-2.89-3[FarutoUltimate3.0]). Key implementation details include Gabor filter parameter configuration, PCA eigenvector computation, and SVM kernel function selection for optimal multi-class classification performance.
Feature extraction from an input image using Gabor wavelet transform, a texture and edge analysis method implemented through multi-scale and multi-orientation filtering operations.
MATLAB program for face recognition using PCA feature dimensionality reduction and Gabor wavelet transform, including algorithm implementation and key function descriptions
Gabor Wavelet Transform for facial expression recognition, generating features across 5 scales and 8 directions. This implementation references the paper "Gabor feature classification using enhanced FLD model facial recognition" and involves convolutional operations with Gabor filter banks for optimized feature discrimination.