Automatic Feature Point Detection in Two Images Using SIFT Algorithm

This implementation utilizes the SIFT (Scale-Invariant Feature Transform) operator for automatic feature point detection across two images. The algorithm operates through multiple computational stages: first detecting distinctive local features using difference-of-Gaussian (DoG) keypoint detection across scale spaces, then calculating orientation-invariant descriptors for each feature point. These descriptors enable robust feature matching and image registration tasks. SIFT's key advantage lies in its strong scale and rotation invariance properties, making it widely applicable in computer vision applications. The automatic feature detection process provides essential inputs for image processing pipelines, object tracking systems, and content-based image retrieval implementations. Typical code implementation involves using OpenCV's SIFT class with methods like detectAndCompute() to extract keypoints and descriptors, followed by feature matching algorithms (e.g., FLANN-based matcher) to establish correspondences between images.