Parametric Subpixel Matchpoint Recovery with Uncertainty Estimation: A Statistical Approach

Abstract

We present a novel matchpoint acquisition method capable of producing accurate correspondences at subpixel precision. Given the known representation of the point to be matched, such as a projected fiducial in a structured light system, the method estimates the fiducial location and its expected uncertainty. Improved matchpoint precision has application in a number of calibration tasks, and uncertainty estimates can be used to significantly improve overall calibration results. A simple parametric model captures the relationship between the known fiducial and its corresponding position, shape, and intensity on the image plane. For each match-point pair, these unknown model parameters are recovered using maximum likelihood estimation to determine a sub-pixel center for the fiducial. The uncertainty of the match-point center is estimated by performing forward error analysis on the expected image noise. Uncertainty estimates used in conjunction with the accurate matchpoints can improve calibration accuracy for multi-view systems.