Abstract
The paper presents a novel method which allows to communicate with computers by means of hand postures. It is assumed that an input to the method is a binary image of a hand presenting a gesture. A curvature of a hand boundary is analysed in the proposed method. Boundary points which correspond to the boundary parts with specified curvature are used to create a feature vector describing a hand shape. Feature vectors corresponding to shapes which are to be recognised by a system are recorded in a model set. They serve as patterns in a recognition phase. In this phase an analysed shape is compared with all patterns included in the database. A similarity measure, proposed specifically for the method, is used here. One advantage of the method is that it allows to easily add a shape to the recognised shapes set. Moreover, the method can be applied to any shapes, not only hand shapes. The results of the tests carried out on the posture database, which includes 12 664 images of 8 hand shapes, are also presented in the paper.
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Marnik, J. (2009). Hand Shape Recognition for Human-Computer Interaction. In: Cyran, K.A., Kozielski, S., Peters, J.F., Stańczyk, U., Wakulicz-Deja, A. (eds) Man-Machine Interactions. Advances in Intelligent and Soft Computing, vol 59. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00563-3_9
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DOI: https://doi.org/10.1007/978-3-642-00563-3_9
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