Feature Matching Using Correlation Coefficient Method After Corner Extraction
This method is used for image registration, where corner extraction is followed by feature matching through correlation coefficients
SIFT Image Matching Algorithm
A classic scale-invariant feature transform (SIFT) image feature matching algorithm implementation. This package includes detailed explanations and demonstration code adapted from international sources, featuring comprehensive line-by-line comments for easy code modification and extension.
MATLAB Code for Color Histogram in Image Retrieval
This file provides MATLAB implementation of color histogram feature extraction for image retrieval applications. The code demonstrates fundamental techniques in computer vision and digital image processing, offering a comprehensive approach to color feature analysis with examples of feature matching implementations.
Image Retrieval Using Gabor Wavelet Transform
An image retrieval program based on Gabor wavelet transform, featuring comprehensive modules for feature extraction, feature matching, and result return with algorithmic enhancements for improved accuracy.
SIFT Feature Extraction for Images Using MATLAB
Implementing SIFT Feature Extraction in MATLAB with Feature Matching Under Varying Illumination and Viewpoints
Feature Matching Using SIFT Algorithm
A program implementing SIFT algorithm for feature matching, which detects keypoints and establishes correspondences between keypoints in two images. The implementation includes sample images demonstrating excellent matching results with techniques for scale-space extrema detection and descriptor generation.
MATLAB Gesture Detection and Recognition System
Application Background: This gesture recognition simulation program serves as an excellent learning resource for MATLAB beginners to understand programming concepts. Once familiar with the implementation, users can extend functionality for algorithm validation and practical applications. Key Technology: The program employs popular image recognition algorithms including PCA (Principal Component Analysis) and feature extraction methods to identify regions of interest within images and interpret their semantic meaning. The implementation utilizes feature matching techniques to enhance recognition accuracy and reliability.
MATLAB Program Design for Image Matching
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.
Image Retrieval Technology Based on Gray-Level Co-occurrence Matrix
Image retrieval technology utilizing Gray-Level Co-occurrence Matrix (GLCM), featuring complete implementation of feature extraction, feature description, feature matching, and result return with algorithm optimization.
Image Retrieval Using Harris Interest Points
An image retrieval system based on Harris interest points featuring comprehensive implementation including feature extraction, feature matching algorithms, and result return mechanisms.