SIFT Feature Extraction from Images with Code Implementation

Our objective is to implement SIFT feature extraction from images, utilizing clustering algorithms to generate visual dictionaries, accompanied by detailed code annotations and technical explanations. The codebase employs OpenCV's SIFT detector to extract keypoints and descriptors, followed by k-means clustering for visual vocabulary creation. This implementation is rigorously tested for SIFT-related image processing tasks and includes essential preprocessing routines and result visualization modules. The code structure demonstrates key computer vision concepts including scale-space extreme detection, keypoint orientation assignment, and descriptor computation. For learners seeking to understand SIFT fundamentals or developers integrating feature extraction into projects, this implementation provides robust functionality with clear parameter configuration interfaces and performance optimization guidelines. Additional capabilities include image preprocessing pipelines and matplotlib-based visualization tools for analyzing feature distribution and clustering results.