Feature Point Extraction from Point Cloud Data
This program implements algorithms for extracting feature points and detecting boundaries in 3D scattered point clouds
MATLAB Image Matching Implementation: Feature Detection and Sub-image Search
Image matching algorithm implementation in MATLAB - searching for sub-images within larger images by detecting and comparing distinctive feature points
SIFT (Scale-Invariant Feature Transform) Feature Point Extraction from Grayscale Images Using MATLAB
Implementing SIFT (Scale-Invariant Feature Transform) feature point extraction from grayscale images using MATLAB with detailed algorithm explanations and code implementation approaches
Feature Point Extraction in Computer Vision and Photogrammetry with MATLAB Implementation
A MATLAB-based program designed for feature point extraction in computer vision and photogrammetry applications, utilizing keypoint detection algorithms and descriptor computation methods.
MATLAB-Based Image Matching Code with Implementation Details
Image recognition and its applications are increasingly vital in modern society. Identifying corresponding points or feature points between two images is a fundamental prerequisite and critical step for image matching. This article includes relevant images and MATLAB source code, providing a comprehensive approach to image matching using MATLAB. The implementation demonstrates key algorithms such as feature detection, descriptor extraction, and matching techniques with practical code examples.
SIFT Feature Extraction Algorithm with RANSAC Matching and Debugging Mechanism
SIFT Feature Extraction Algorithm (including matching and RANSAC outlier removal mechanism) - suitable for feature point matching between two images with parameter optimization capabilities
Fingerprint Recognition Using Feature Point Matching
Several fingerprint matching techniques exist, with the most widely used being: Correlation-based matching, which computes similarity across different pixel paths and rotations but suffers from high computational complexity. Feature point-based matching, the most popular technique, identifies and maps minutiae points for comparison, yet faces challenges with low-quality fingerprints. Pattern-based (image-based) matching compares fingerprint patterns (arches, whorls, loops) between templates and candidate images but requires precise alignment. Implementation considerations include algorithmic efficiency, preprocessing requirements, and reliability under varying image quality conditions.
MATLAB Implementation of SIFT-Based Image Stitching
SIFT Image Stitching Algorithm: Feature Point Detection, Transformation Matrix Calculation, and Projective Image Alignment
Image Template Matching Computation
Image template matching calculation involves pixel-by-pixel movement to compute current template data and extract image feature points using correlation-based algorithms
Image Stitching using SIFT Feature Points
Image stitching with SIFT feature points - matching and merging two images by first identifying and matching feature points, featuring implementation approaches using OpenCV and key algorithm explanations