Principal Component Analysis for Image Recognition and Feature Extraction
MATLAB-based PCA implementation for image recognition and feature extraction applications, featuring dimensionality reduction and pattern discovery capabilities.
A Comprehensive Fingerprint Recognition Algorithm
A complete fingerprint recognition algorithm covering the entire process from fingerprint acquisition to feature extraction, including code implementation for key components.
Major Algorithms for Feature Extraction: Independent Component Analysis (ICA)
This uploaded code implements one of the primary feature extraction algorithms: Independent Component Analysis (ICA), which demonstrates superior performance compared to Principal Component Analysis (PCA) in various applications.
Multi-Difference Subpixel Algorithm for Image Enhancement
Implementation of noise filtering, edge detection, feature extraction, and image enhancement using multi-difference subpixel algorithms on sampled original images.
Implementation of Image Processing Tasks: Edge Detection, Segmentation, Feature Extraction, and Digit Recognition Using MATLAB
Implementation of image processing tasks in MATLAB including edge detection, image segmentation, feature extraction, and digital image recognition. The process involves applying blurring, sharpening, and histogram equalization operations to images with visualization of results. Complete image edge detection is implemented to achieve portrait or sketch effects, with graphical representation of processed outputs including key function explanations and algorithm implementations.
MATLAB Implementation of Face Recognition System
Complete face recognition system implementation featuring image preprocessing, K-L transform based feature extraction, and classifier design for fully automated facial identification
Feature Extraction Algorithms in Graphics and Image Processing: Zernike Moments
A crucial feature extraction algorithm in graphics and image processing - Zernike moments with implementation insights
SVM for Image Classification Using Block-Based Feature Extraction
SVM for image classification using block-based feature extraction primarily focuses on determining image categories such as ancient architecture, water bodies, vegetation, etc. Implementation involves feature vector extraction through image partitioning and SVM model training for multi-class classification.
Gabor Wavelet Feature Extraction
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.
Isomap Isometric Mapping and Dimensionality Reduction
Isomap Isometric Mapping, Feature Extraction Techniques, and Machine Learning Applications