Abstract

Reliable detection of fiducial targets in real-world images is addressed in this paper. We show that even the best existing schemes are fragile when exposed to other than laboratory imaging conditions, and introduce an approach which delivers significant improvements in relia- bility at moderate computational cost. The key to these improvements is in the use of machine learning techniques, which have recently shown impressive results for the general ob ject detection problem, for example in face detection. Although fiducial detection is an apparently simple special case, this paper shows why robustness to lighting, scale and fore- shortening can be addressed within the machine learning framework with greater reliability than previous, more ad-hoc, fiducial detection schemes.