Calibration and Evaluation of Internal and External Parameters of Super-Wide Field-of-View Infrared Gaze Imaging System
Based on the fact that the existing calibration methods for imaging systems cannot effectively calibrate the super wide field-of-view infrared gaze imaging system,this research proposes a new indirect corner detection algorithm as well as an improved scheme to address the problem of local optimization in the iterative computation of the Scaramuzza calibration model.With the help of morphological operations,pixel-level detection is performed on the edges of the chessboard calibration board.Interpolation tech-niques are then applied to refine the edges,achieving sub-pixel accuracy.This allows for the acquisition of four chessboard unit corners near the true corner points.By averaging the coordinates of these four cor-ners,the coordinates of the true corner points are indirectly obtained,resulting in a corner detection accu-racy rate of 100％,which is significantly higher than that of general algorithms and more closely aligns with the true corner positions.During the iterative process,the entire region is sampled with a specific cir-cular region sampling grid to collect the Sum of Squared Residuals Errors(SSRE).By minimizing all col-lected data,this approach not only avoids falling into local minima,improving positioning accuracy,but also significantly reduces the number of iterations and computational load.It effectively addresses the shortcomings of existing general calibration algorithms that cannot meet the requirements of super-wide field of view infrared gaze imaging systems.
super wide field-of-view infrared imagecorner point detectionimaging system calibra-tioninternal and external parameters
万方数据
维普
