SIFT Image Matching Algorithm Implementation

The Scale-invariant Feature Transform (SIFT) is a fundamental computer vision algorithm designed to detect and describe local features within digital images. This algorithm operates by identifying keypoints as local extrema across scale-space representations and extracting invariant descriptors capturing their position, scale, and orientation. Originally published by David Lowe in 1999 with significant refinements in 2004, SIFT employs a multi-stage approach including Gaussian pyramid construction, keypoint localization, orientation assignment, and 128-dimensional descriptor generation.

By implementing SIFT algorithm for feature point extraction and descriptor computation, we can effectively match manipulated regions between images. The execution of t2.m script demonstrates a complete workflow: it loads two input images, detects SIFT features using difference-of-Gaussian (DoG) interest point detection, computes orientation-histogram-based descriptors, and performs feature matching through nearest-neighbor distance ratio tests. This process utilizes MATLAB's computer vision toolbox functions like detectSIFTFeatures() and matchFeatures() to identify potential tampered areas with high precision.

As a widely adopted algorithm in computer vision, SIFT plays crucial roles in image processing and recognition applications. Its implementation involves key programming components such as scale-space peak selection using 3D quadratic fitting, descriptor normalization for illumination invariance, and k-d tree based feature matching for efficiency. The algorithm's robustness against affine transformations makes it particularly valuable for forensic image analysis and duplicate region detection.

In summary, the SIFT algorithm serves as an essential tool for extracting distinctive local features and performing reliable image matching. Its MATLAB implementation provides practical insights into feature detection parameters, descriptor matching thresholds, and result visualization techniques, offering significant support for computer vision research and application development.