A Segment-Based Algorithm for Grid Junction Corner Detection Used in Stereo Microscope Camera Calibration

Abstract

Corner detection is responsible for accurate camera calibration, which is an essential task for binocular three-dimensional (3D) reconstruction. In microscopic scenes, binocular 3D reconstruction has significant potential to achieve fast and accurate measurements. However, traditional corner detectors and calibration patterns (checkerboard) performed poorly in microscopic scenes due to the non-uniform illumination and the shallow depth of field of the microscope. In this paper, we present a novel method for detecting grid junction corners based on image segmentation, offering a robust alternative to the traditional checkerboard pattern. Model fitting was utilized to obtain the coordinates at a sub-pixel level. The procedures of the proposed method were elaborated, including image segmentation, corner prediction, and model fitting. The mathematical model was established to describe the grid junction. The experiment was conducted using both synthetic and real data and the experimental result shows that this method achieves high precision and is robust to image blurring, indicating this method is suitable for microscope camera calibration.