Explore MATLAB source code curated for "匹配" with clean implementations, documentation, and examples.
Iris recognition image normalization processing transforms the annular iris region into a standardized rectangular image, facilitating subsequent feature extraction and matching operations. The process typically involves implementing coordinate mapping algorithms to convert circular iris textures into linear representations.
A program designed for image category recognition that performs region matching between images based on preliminary segmentation results, implementing computer vision algorithms for accurate classification.
An image classification software developed using MATLAB, capable of object classification and matching recognition with comprehensive algorithmic workflows including preprocessing, feature extraction, and model training/testing.
Fast implementation of SIFT algorithm for feature point extraction and matching between reference and target images, with code-level optimization insights
User interface developed using MATLAB's GUI capabilities for Smith chart matching operations and related functionalities, featuring interactive controls for data input, graphical display, and parameter adjustments
Harris corner detection algorithm implementation in MATLAB for identifying corner points in images, enabling image matching through feature point correspondence
Implement feature extraction and matching between two images using SIFT and RANSAC algorithms, with a bounding box highlighting the smaller image region in the larger image. The implementation involves keypoint detection using SIFT, feature matching with distance ratio testing, and geometric verification through RANSAC-based homography estimation. Execute plot.m to visualize the matching results and region localization.
An improved ASIFT program implemented in MATLAB that delivers outstanding image recognition and matching capabilities. This enhanced version demonstrates significant performance improvements over traditional algorithms like SIFT and SURF through optimized code implementation and additional image processing features.
Successfully tested MATLAB code for SIFT algorithm implementation, featuring efficient feature point extraction and matching operations. Includes sample images for reference and demonstrates robust performance in computer vision applications.
This simple, compact MATLAB code implements speech recognition by comparing input audio signals with pre-stored voice templates; if a successful match occurs, an "access granted" signal is generated, otherwise an "access denied" signal is produced.