Harris Corner Detector + SIFT Descriptor: A Classic Computer Vision Feature Extraction Pipeline

The Harris Corner Detector combined with SIFT (Scale-Invariant Feature Transform) Descriptor constitutes a classical computer vision pipeline for image feature extraction. The Harris Corner Detector identifies corner points in images by analyzing intensity variations, while SIFT Descriptor characterizes these detected keypoints to enable applications like feature matching and object recognition. The Harris detector operates by computing gradient covariance matrices (autocorrelation matrices) across image patches, where corner positions are determined through eigenvalue analysis of these matrices. In code implementation, this typically involves calculating image derivatives (Ix, Iy), constructing the Harris matrix M = [Ix² IxIy; IxIy Iy²], and evaluating the corner response function R = det(M) - k·trace(M)², where k is an empirical constant (usually 0.04-0.06). SIFT Descriptor builds rotation- and scale-invariant feature vectors by capturing local gradient orientation and magnitude information. The algorithm implementation involves creating gradient orientation histograms over 4×4 subregions around each keypoint, generating a 128-dimensional descriptor vector (16 subregions × 8 orientation bins). This descriptor demonstrates robustness through Gaussian pyramid construction for scale invariance and dominant orientation assignment for rotation invariance. By integrating Harris detector for precise corner localization with SIFT's invariant descriptors, this pipeline provides rich feature information that significantly enhances accuracy and robustness in image processing and computer vision tasks such as image stitching, 3D reconstruction, and object recognition systems.