Sub-pixel Localization of Circular Markers and Its Applications

In this paper, we introduce a sub-pixel localization technique for circular markers and its practical applications. The implementation begins with edge detection using the Canny operator, which employs Gaussian filtering, gradient calculation, non-maximum suppression, and dual-threshold hysteresis to achieve optimal edge detection. Following edge extraction, we perform sub-pixel-level detection through surface fitting algorithms, typically using quadratic or cubic polynomial functions to interpolate intensity values around edge points. This methodology significantly improves positioning accuracy and reliability, making it valuable across various industrial applications. The sub-pixel localization technique finds extensive use in machine vision systems, image processing pipelines, and object tracking scenarios. By implementing this approach, researchers can achieve enhanced analysis of circular markers in digital images and obtain superior results in applications requiring high-precision measurements, such as photogrammetry, robotic guidance, and quality inspection systems.