A Comprehensive Guide to Local Feature Detection and Description: Harris, LoG, and SIFT in Image Processing

Feature detection and description are foundational tasks in computer vision, enabling applications such as object recognition, image stitching, and 3D reconstruction. This study presents a comparative analysis of three widely used feature detection techniques: Harris Corner Detection, Laplacian of Gaussian (LoG), and Scale-Invariant Feature Transform (SIFT). The Harris method is explored for its corner detection capabilities based on intensity variations, while LoG is analyzed for its ability to detect blob-like structures through multi-scale processing. SIFT, known for its robustness to scale, rotation, and illumination changes, is evaluated as a comprehensive approach for detecting and describing keypoints. Using systematic experiments, this study examines response maps, keypoint distributions, and feature matching results to highlight the strengths and limitations of each method. The findings emphasize the trade-offs among computational efficiency, robustness, and accuracy, providing practical insights for selecting appropriate techniques for specific computer vision applications.