In modern society there is an increasing demand to access, record and manipulate large amounts of information. This has inspired a new approach to thinking about and designing personal computers, where the ultimate goal is to produce a truly wearable computer. In this work we present a non-invasive handgesture recognition system aimed at deictic gestures. Our system is based on the powerful Sequential Monte Carlo framework which is enhanced with respect to increased robustness. This is achieved by using ratios in the likelihood function together with two image cues: edges and skin color. The system proves to be fast, robust towards noise, and quick to lock on to the object (hand). All of which is achieved without the use of special lighting or special markers on the hands, hence our system is a non-invasive solution. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Moeslund, T. B., & Nørgaard, L. (2006). Recognition of deictic gestures for wearable computing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3881 LNAI, pp. 112–123). https://doi.org/10.1007/11678816_13
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