SIFT Image Matching Algorithm Implementation

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.

Scale-Invariant Feature Transform (SIFT) Source Code for Image Stitching Algorithms

Fully functional SIFT source code for image stitching algorithms, tested and optimized with detailed implementation notes. Particularly helpful for MATLAB beginners to understand feature detection, matching, and image composition techniques.