COMPARATIVE ANALYSIS OF KEYPOINT DETECTION METHODS IN IMAGES - HAVRYUSHENKO, Anna; BOROZDYKH , Kateryna (Cherkasy University Bulletin: Applied Mathematics. Informatics, 2025)
Introduction. Image search – finding one image within another – is a fundamental problem in computer vision, with applications in object recognition, motion tracking, 3D reconstruction, aerial image analysis and autonomous navigation systems. The core challenge is identifying local image features, called keypoints, that remain stable under changes in scale, rotation, viewpoint and illumination. Since the pioneering work of Moravec and Harris on corner detection, and the subsequent development of SIFT by Lowe, the field has produced a number of competing methods that vary significantly in their theoretical foundations, accuracy and computational efficiency. Purpose. This paper presents a systematic comparison of six keypoint detection methods – SIFT, SURF, BRISK, ORB, KAZE and AKAZE – evaluated on images of varying geometric complexity. Two evaluation criteria are used: the spatial configuration quality of detected keypoints relative to salient image features, and the average computation time per keypoint. Results. The mathematical background of SIFT is described in detail, covering scale-space extrema detection via the Difference of Gaussians function, precise keypoint localization using Taylor series expansion of the scale-space function, orientation assignment and descriptor computation. For all six methods, experiments were conducted on four synthetic images (line segments, closed polylines, ellipses, and a color image with programming language logos) and two real-world images (book covers). Results show that BRISK and ORB are the fastest methods, with per-keypoint times of 0.032 ms and 0.070 ms respectively on real images. However, BRISK detects an excessive number of keypoints (148,300 over 20 trials), many of which are unstable. ORB provides a more balanced result: moderate keypoint count (10,000), the second-fastest computation time, and a spatial configuration concentrated near meaningful image features such as corners and intersections. SIFT, while the slowest method (0.220 ms per keypoint), provides a theoretically well-grounded and selective response. KAZE is the slowest across all test cases (0.861 ms per keypoint), limiting its practical use in time-critical applications. Conclusion. No single method dominates across all evaluation criteria and image types. The optimal choice depends on the characteristics of the target images and the specific requirements of the application. For real-time systems with limited computational resources, ORB provides the best tradeoff between speed and keypoint quality. For tasks where accuracy is prioritized over speed, SIFT remains competitive. These findings highlight the importance of method selection guided by systematic benchmarking rather than general assumptions.