Bayesian Image Classification for Pattern Recognition with Neural Networks
Bayesian image classification combined with neural networks for pattern recognition, implementing probability-based decision making and deep learning models
Feature Extraction of Fruit Images
This graduation project implements practical fruit image feature extraction by simply running the "apple.m" MATLAB script, which serves as the main executable file for the image processing pipeline.
Performing Gabor Transform on Images with MATLAB Implementation
Implementing Gabor transform using MATLAB to extract image eigenvalues, featuring code implementation details and texture feature analysis
Feature Extraction for Signals and Images
Nonnegative Matrix Factorization (NMF) – A Technique for Feature Extraction in Signals and Images, Also Applicable for Image Compression
Comparative Analysis of DCT and DWT Characteristics in Digital Image Watermarking Applications
Comparing the features of DCT and DWT in digital image watermarking applications with algorithm implementation insights and performance analysis.
A MATLAB Program for Acquiring Audio from Microphone and Video from Camera
A MATLAB-based application designed to capture audio input from microphones and video input from cameras, with comprehensive code implementation details for audio-visual data acquisition.
Two-Dimensional Discrete Wavelet Transform for Image Processing
Perform multi-level (≥3) 2D discrete wavelet transformation on images, reconstruct the transformed data, and calculate the Peak Signal-to-Noise Ratio (PSNR) of the reconstructed image. Implementation typically involves wavelet decomposition using functions like wavedec2(), reconstruction using waverec2(), and PSNR calculation through mean squared error computation.
Dataset of 213 Grayscale Images Featuring 7 Positive Facial Expressions from 10 Individuals
This database comprises 213 grayscale images representing 7 distinct positive facial expressions from 10 subjects. All images are stored as 256×256 pixel 8-bit grayscale TIFF files, with an average of 2-4 samples per expression per individual. The dataset structure facilitates implementation of facial expression recognition algorithms through standardized image preprocessing and classification techniques.
SIFT Feature Extraction for Images Using MATLAB
Implementing SIFT Feature Extraction in MATLAB with Feature Matching Under Varying Illumination and Viewpoints
YALE Face Database: A Comprehensive Toolkit for Face Recognition Research
YALE Face Database contains 15 subjects with 11 facial images per person, providing robust data for developing and testing face recognition algorithms through various lighting conditions and expressions.