SIFT Feature Point Detection and Matching with MATLAB Implementation

This document explores SIFT feature point detection and matching along with its implementation using MATLAB source code. SIFT feature detection represents a fundamental computer vision technique that enables identification of distinctive keypoints in images and establishes correspondences between different images. Through MATLAB implementation, we gain deeper insights into the algorithmic details of SIFT, allowing for customization and parameter tuning. The implementation typically involves several key stages: constructing a Gaussian pyramid for scale-space representation, detecting scale-space extrema using Difference of Gaussians (DoG), orienting keypoints based on local gradient directions, and generating 128-dimensional feature descriptors. The matching process employs nearest-neighbor search with distance ratio testing to establish reliable correspondences. This paper provides comprehensive explanations of SIFT principles and workflow, accompanied by sample code demonstrations and experimental results to facilitate better understanding and practical application of these techniques. The MATLAB implementation includes essential functions for feature extraction, descriptor computation, and matching validation, demonstrating how to handle scale invariance and rotation robustness in real-world image analysis scenarios.