Abstract.Developing new techniques for human-computer interaction is very challenging. Vision-based techniques have the advantage of being unobtrusive and hands are a natural device that can be used for more intuitive interfaces. But in order to use hands for interaction, it is necessary to be able to recognize them in images. In this paper, we propose to apply to the hand posture classification and recognition tasks an approach that has been successfully used for face detection [3]. The features are based on the Modified Census Transform and are illumination invariant. For the classification and recognition processes, a simple linear classifier is trained, using a set of feature lookup-tables. The database used for the experiments is a benchmark database in the field of posture recognition. Two protocols have been defined. We provide results following these two protocols for both the classification and recognition tasks. Results are very encouraging.
In this paper, we address the problem of the recognition of isolated complex mono-and bi-manual hand gestures. In the proposed system, hand gestures are represented by the 3D trajectories of blobs obtained by tracking colored body parts. In this paper, we study the results obtained on a complex database of mono-and bi-manual gestures. These results are obtained by using Input/Output Hidden Markov Model (IOHMM), implemented within the framework of an open source machine learning library, and are compared to Hidden Markov Model (HMM).
In this paper, we address the problem of the recognition of isolated complex mono-and bi-manual hand gestures. In the proposed system, hand gestures are represented by the 3D trajectories of blobs. Blobs are obtained by tracking colored body parts in real-time using the EM algorithm.In most of the studies on hand gestures, only small vocabularies have been used. In this paper, we study the results obtained on a more complex database of mono-and bi-manual gestures. These results are obtained by using a state-of-theart sequence processing algorithm, namely Hidden Markov Models (HMMs), implemented within the framework of an open source machine learning library.
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