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It is well known that fixed global thresholds have adverse effects on the reliability of marker-based optical trackers under non-uniform lighting conditions. Mobile augmented reality applications, by their very nature, demand a certain level of robustness against varying external illumination from visual tracking algorithms. Currently, ARToolKit depends on fixed-threshold image-binarization in order to detect candidate fiducials for further processing. In an effort to minimize tracking failure due to uniform shadows and reflections on a marker surface, we propose a fast algorithm for selecting adaptive threshold values, based on the arithmetic mean of pixel intensities over a region-of-interest around candidate fiducials.