Explore MATLAB source code curated for "图像匹配" with clean implementations, documentation, and examples.
MATLAB implementation for generating disparity maps for image matching, applicable in 3D reconstruction systems and machine vision applications. The implementation includes stereo matching algorithms and depth estimation techniques.
This algorithm implements image matching using wavelet transform-based feature extraction techniques, which decompose images into multi-frequency components for robust feature analysis and comparison.
DAISY is a feature descriptor algorithm introduced in 2008, commonly used in image matching applications. The package includes the algorithm's PDF documentation and corresponding MATLAB implementation by the original authors, providing customizable resources for practical implementation.
MATLAB Image Processing with Simulated Annealing Algorithm for Direct Image Matching and Optimization Techniques
Template-based image matching implemented in MATLAB with concise, well-documented code featuring clear explanatory comments
This program implements infrared and visible image registration by utilizing edge information for image matching, performing edge detection to identify closed contours, and using chain code representation for precise image alignment.
Image matching implementation with three core components: match.m (testing program to verify matching accuracy), sift.m (core algorithm for Scale-Invariant Feature Transform), and appendimages.m (function that creates composite images showing matched pairs with connecting lines). Fully executable code.
Implementation of image matching with SSDA algorithm and custom screenshot selection for matching positions
Image matching can be primarily categorized into intensity-based matching and feature-based matching. The fundamental concept of intensity-based matching is to treat images as two-dimensional signals from a statistical perspective, using statistical correlation methods to find correlations between signals. By utilizing correlation functions between two signals, their similarity is evaluated to identify corresponding points. Intensity-based matching determines correspondence between two images through similarity measures such as correlation functions, covariance functions, sum of squared differences, and sum of absolute differences to find extreme values. Feature-based matching involves extracting features (points, lines, surfaces, etc.) from two or more images, parameterizing these features, and then using the described parameters for matching. This code implements two intensity-based image matching algorithms with MATLAB implementations including template matching using normalized cross-correlation and optimized rapid matching with sliding window techniques.
A truly classic MATLAB-based solution suitable for all image matching applications, featuring robust algorithms and practical implementation tools.