Plotting DEMON Spectrum for Signal Analysis
Generate the DEMON spectrum of a signal to visualize its characteristics, providing an intuitive representation of spectral features for enhanced signal analysis.
Face Feature Localization Using Gray Projection - MATLAB Implementation
MATLAB-based face feature localization using gray projection algorithm with detailed code implementation
MATLAB Source Code for Texture-Based Image Retrieval System
This MATLAB program implements texture-based image retrieval source code, where texture features serve as fundamental image characteristics. The implementation focuses on texture feature extraction using Gray-Level Co-occurrence Matrix (GLCM) for statistical analysis of pixel relationships, making it particularly relevant for contemporary image processing applications.
Image Segmentation Using Support Vector Machines
This code demonstrates image segmentation using Support Vector Machines - The implementation utilizes an image featuring a yellow duck hunting insects on a blue lake surface. Due to the distinct color contrast, RGB pixel values are selected as features. The implementation includes feature extraction, SVM model training, and pixel classification for region separation.
Contourlet Transform Toolkit for Image Feature Extraction
A comprehensive Contourlet transform toolkit for image feature extraction supporting customizable decomposition levels - for instance, three-level decomposition extracts 17-dimensional feature vectors suitable for texture analysis and SAR image segmentation applications.
knn-k-Nearest Neighbors Algorithm Implementation for Binary Classification Function
Function code implementing the k-Nearest Neighbors (k-NN) algorithm for binary classification, which takes feature samples from two classes and a test sample vector as input, and outputs the classification result with detailed implementation explanations.
Wavelet-Based Multiscale Directional Feature Extraction for Images
Multiscale directional feature extraction from images using wavelet transform, implemented with db4 wavelet for effective image analysis
Modified Log Gabor Wavelet Image Texture Feature Analysis
Modified Log Gabor wavelet image texture feature analysis with enhanced implementation
Edges as the Most Fundamental Feature in Image Processing
Edges represent the most fundamental features in images and serve as the first step in image segmentation. Classical edge detection methods—such as Roberts, Sobel, Prewitt, Kirsch, and Laplace—generally operate by constructing small neighborhood operators to compute first or second-order derivatives, identifying gradient maxima or zero-crossings of the second derivative, and finally applying a suitable threshold to extract boundaries. These gradient-based methods, however, are noise-sensitive and computationally intensive. In contrast, the SUSAN (Smallest Univalue Segment Assimilating Nucleus) algorithm relies solely on grayscale comparisons of surrounding pixels without any gradient calculations, making it highly robust to noise and computationally efficient. SUSAN has been effectively applied in edge detection for various types of images.
Gabor Wavelet Transform for Facial Expression Recognition with Multi-Scale and Multi-Orientation Feature Extraction
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.