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Abstract
We propose a probabilistic classifier for multi-touch gestures specified by users themselves. The template-based gesture classifier allows selecting gesture types more freely without constraints regarding implementation issues and considers multi-finger or bi-manual operations. The statistical approaches to the classification scheme are presented. The basic concepts of separating input into tokens, retrieving local features and applying a new method of sensor fusion under uncertainty are adaptive to broader application ranges. Results from testing against a set of sophisticated samples show that this approach performs well and, while recognition benefits from more complex gestures, it also distinguishes subtly different gestures.
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