Image Registration with MATLAB Implementation
MATLAB-based image registration code for precise image alignment and processing applications, featuring key algorithms and implementation approaches
Integration of Fundamental Image Processing Methods with GUI
Image processing techniques integration based on MATLAB GUI, including histogram analysis, filtering, wavelet transforms, watermarking, segmentation, and more. Features adjustable parameters, detailed code comments for key functions, and completely original implementations. Shared for community exchange and learning purposes.
Direct Least Squares Ellipse Fitting
A practical ellipse fitting program based on direct least squares method, suitable for image processing applications and ideal for beginners in ellipse fitting research. The implementation efficiently solves the general conic equation using algebraic distance minimization with eigenvalue decomposition, featuring constraints to ensure elliptical solutions.
Fast Global Minimization Algorithm for Active Contour Models
This MATLAB implementation showcases a fast global minimization algorithm based on active contour models, providing valuable insights into modern image processing techniques through practical code demonstration.
Rapid Image Corner Detection using 9-Point Method
A MATLAB implementation for fast corner detection in binary images using an efficient 9-point neighborhood analysis algorithm, requiring input images to be two-value (binary) format for optimal performance.
Camera Calibration for Computer Vision Applications
Implementation of camera calibration for computer vision systems - a fundamental image processing technique that computes intrinsic and extrinsic camera parameters from multi-angle images using specialized algorithms.
Single Gaussian Background Modeling with Background Subtraction Method
Single Gaussian Modeling is a background extraction technique in image processing, suitable for static and uniform background scenes. This model offers simplicity and computational efficiency by employing parameter iteration instead of rebuilding the model each time, where t represents the timestamp. The algorithm compares the current color intensity xt of each pixel against a probability threshold—if xt is less than or equal to the threshold, the pixel is classified as foreground; otherwise, it is deemed part of the background. Implementation typically involves iterative updates of Gaussian parameters (mean and variance) using a learning rate to adapt to gradual changes.
Particle Filter Image Processing with Five Core Algorithms
Particle filter image processing implementation featuring five specialized subroutines for comprehensive image analysis and enhancement
MATLAB 7 Hu Moment Extraction for Image Processing
MATLAB 7 Hu Moment Extraction for Image Processing with Code Implementation and Algorithm Explanation
Image Median Filtering Using a 3x3 Template
MATLAB implementation of median filtering for images utilizing a 3x3 kernel