Rule-Based Segmentation for Intensity-Adaptive Fiducial Detection

This paper describes a new fiducial detection method for use under varying lighting conditions without manual control of any parameters. We developed the algorithm especially for vision-based Augmented Reality (AR) systems. The major problem in Augmented Reality is the registration between the virtual world and the real world. The user’s pose in both worlds should be exactly the same. Vision-based AR is an attractive approach to the registration problem, however the fiducial detection methods used in many systems operate only under restricted lighting conditions. We developed a rule-based algorithm to segment regions of an image to detect known fiducials under varying lighting conditions. The algorithm is based on simple spatial and intensity relations among fiducials and their backgrounds. Rules and membership functions are defined from those relations. Rules are applied to find transition regions, and membership functions locate an edge position within a transition region. Edges are clustered to segment regions in an image. A vision-based AR system using our method operates under varying lighting conditions, including uneven lighting. This detection method extends the operating conditions of vision-based AR systems.