Explore MATLAB source code curated for "特征点" with clean implementations, documentation, and examples.
A comprehensive MATLAB digital image processing case study demonstrating image registration techniques using both feature points and feature regions, with detailed code implementation examples and algorithm explanations.
This implementation addresses limitations in standard SIFT by integrating Harris corner detection for more efficient feature point localization and reducing feature vector dimensionality while maintaining rotation and scale invariance. The approach significantly improves computational efficiency and robustness in image registration tasks.
Fast implementation of SIFT algorithm for feature point extraction and matching between reference and target images, with code-level optimization insights
Recovering 3D structure from motion in noisy 2D images is a fundamental problem addressed by computer vision researchers. By consistently tracking feature points across multiple images using the Lucas-Kanade optical flow algorithm, the Tomasi-Kanade factorization method enables 3D shape reconstruction through matrix decomposition of the feature point trajectories.
A robust image stitching program that utilizes SIFT algorithm for feature point extraction, implements mismatch filtering algorithms, and visualizes matched points with connecting lines. Key implementation note: The main function is match.m - after running the main function, enter match('image1.jpg', 'image2.jpg') in the command window.
The Scale-invariant Feature Transform (SIFT) is a computer vision algorithm for detecting and describing local image features. It identifies keypoints by finding extrema in scale space and extracts their position, scale, and rotation-invariant descriptors. Developed by David Lowe in 1999 and refined in 2004, this algorithm enables robust image matching by comparing feature descriptors. The implementation in t2.m demonstrates how to extract SIFT features and match tampered regions between two images using descriptor comparison algorithms.
Image registration process using thin plate splines involves the following steps: First, feature extraction from both images to obtain feature points; then finding matching feature pairs through similarity measurement. Accurate feature extraction ensures successful feature matching. Therefore, finding feature extraction methods with good invariance and accuracy is crucial for matching precision. This implementation uses thin plate splines for image registration, which provides smooth deformation fields while minimizing bending energy.
This MATLAB implementation demonstrates SURF (Speeded-Up Robust Features) algorithm for feature point extraction and image stitching. It provides faster feature detection compared to SIFT algorithm, with easier learning curve for beginners. The code includes feature detection, descriptor extraction, and matching techniques.
Image registration performed using MATLAB toolbox requires feature point extraction to achieve alignment between images
A corner detection algorithm implementation with good precision, suitable for image matching applications - demonstrates effective feature point detection with practical code examples