Image Fourier Transform
Performs Fourier transform on images, normalizes the frequency spectrum, and displays results on 2D visualizations with implementation insights
Converting Color Images to Grayscale and Performing Image Calculations (Mean, Variance, FFT Transform)
Reading 2D images, converting color images to grayscale, and performing image calculations including mean, variance, and FFT transformations
3D Model Reconstruction of Buildings from 2D Images
Reconstructing 3D building models using photographs captured by cameras, involving Structure from Motion (SfM) algorithms and multi-view stereo techniques.
2D to 3D Image Texture Mapping Functionality
This MATLAB program implements texture mapping from 2D images to 3D images, originally developed by the author for MRI medical imaging applications, featuring coordinate transformation algorithms and surface mapping techniques.
Useful 2D Image Spectrum Analysis in Image Processing with MATLAB Implementation
Practical 2D image spectral analysis techniques for image processing with complete MATLAB source code
Implementation of 2D Image Fourier Transform Algorithm Using MATLAB
Implementation of 2D image Fourier transform algorithm using MATLAB with code-level explanations for frequency domain analysis and processing
Two-Dimensional Walsh-Hadamard Transform for Image Processing
MATLAB implementation of the two-dimensional Walsh-Hadamard transform for image processing, including key algorithmic approaches and transformation matrix generation.
Phase Retrieval for Two-Dimensional Images
Phase retrieval using the GS (Gerchberg-Saxton) iterative algorithm for two-dimensional image reconstruction
Implementation of 2D Image Histogram Plotting with MATLAB
A MATLAB-based program for generating two-dimensional image histograms with enhanced code explanations and implementation algorithms
Source Code for Target Trajectory Tracking in 2D Images
This program implements target trajectory tracking in 2D images using Kalman filtering. The system employs background subtraction for target detection and feeds the results to a Kalman filter to predict the target's next appearance position (marked in red). The predicted position can be compared with the actual detected position (marked in green).