Article ID Journal Published Year Pages File Type
530080 Pattern Recognition 2013 10 Pages PDF
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.

► Statistical classification, formalization/algorithm and empirical proof. ► Concept of dividing gestures into tokens (different sensors). ► Maximum likelihood matching of sensor data. ► Represents special sensor fusion (token features “of unknown sources/sensors”). ► Combining distance and probability measurements for classification.

Related Topics
Physical Sciences and Engineering Computer Science Computer Vision and Pattern Recognition
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