2004 | OriginalPaper | Buchkapitel
Reliable Fiducial Detection in Natural Scenes
verfasst von : David Claus, Andrew W. Fitzgibbon
Erschienen in: Computer Vision - ECCV 2004
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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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 reliability 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 object 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 foreshortening can be addressed within the machine learning framework with greater reliability than previous, more ad-hoc, fiducial detection schemes.