SIFT Feature Extraction for Images Using MATLAB

In this documentation, we demonstrate the implementation of Scale-Invariant Feature Transform (SIFT) algorithm using MATLAB, along with feature matching techniques applied to images under diverse lighting conditions and viewing angles. The SIFT algorithm represents a robust method for extracting distinctive image features with scale invariance, enabling the detection of consistent keypoints across images of varying scales. Through SIFT feature extraction and matching, we can perform essential computer vision tasks such as image comparison, retrieval, and registration - making it a critical tool in modern image processing applications. To implement SIFT in MATLAB, we typically utilize functions like detectSIFTFeatures() for keypoint detection and extractFeatures() for descriptor computation. The algorithm workflow involves: 1) constructing a scale-space pyramid using Gaussian blurring, 2) detecting scale-space extrema through Difference-of-Gaussian (DoG) approximation, 3) orienting keypoints based on local gradient directions, and 4) generating 128-dimensional feature vectors describing local texture patterns. For feature matching, we employ techniques like nearest-neighbor search with Euclidean distance metrics, often implemented using MATLAB's matchFeatures() function. The matching process includes applying distance ratio tests to eliminate ambiguous matches and using RANSAC (Random Sample Consensus) for robust homography estimation when handling viewpoint changes. This documentation provides practical implementation insights for handling illumination variations through intensity normalization and addressing perspective distortions via affine transformation estimation. We hope this resource enhances your understanding and application of SIFT feature extraction in computer vision projects.