By Karl-Friedrich Kraiss

Modern man-machine interfaces are more and more characterised by means of multimodality, nonintrusiveness, context-sensitivity, adaptivity, and teleoperability. The implementation of such homes is dependent upon novel recommendations in felds similar to, e.g., desktop imaginative and prescient, speech know-how, trainable classifiers, robotics, and digital fact. This publication places specific emphasis on technological features of complicated interface implementation. additionally it makes a speciality of interface layout and usefulness. For readers with a heritage in engineering and desktop technology, so much chapters supply layout directions and case reports, in addition to an outline of the functioning and boundaries of the algorithms required for implementation. furthermore, complementary code examples in C++ are given the place applicable. As a distinct function the publication is followed via easy-to-handle software program improvement environments, which provide entry to wide public area software program for machine imaginative and prescient, class, and digital truth. those environments additionally offer real-time entry to peripheral parts like, e.g., webcams or microphones, permitting hands-on experimentation and checking out.

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Extra info for Advanced Man-Machine Interaction: Fundamentals and Implementation (Signals and Communication Technology)

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1 Hand Localization The identification of foreground or target regions constitutes an interpretation of the image based on knowledge which is usually specific to the application scenario. ). Known properties of the target object, such as shape, size, or color, can be exploited. In gesture recognition, color is the most frequently used feature for hand localization since shape and size of the hand’s projection in the twodimensional image plane vary greatly. It is also the only feature explicitly stored in the image.

Fig. 16. Overlay plot of the feature x˙ cog (t) for three productions of the gesture “clockwise”, divided into 12 segments of 5 observations (frames) each. The gray bars visualize mean and variance for each segment. This represents a first crude model (though not an HMM) of the time dependent stochastic process x˙ cog (t), as shown in Fig. 17. The model consists of 12 states 9 The segment size of 5 observations was empirically found to be a suitable temporal resolution. Hand Gesture Commands 39 si , each characterized by μi and σi2 .

If ω ˆ = ω then the classification result is correct, otherwise it is wrong. To account for the case that O does not bear sufficient similarity to any element in Ω, one may wish to allow a rejection of O. 37) becomes ˆ ω ˆ ∈Ω where ω ˆ = ωk , k ∈ {0, 1, . . 39) 30 Non-Intrusive Acquisition of Human Action Modeling Classification Effects Several types of classifiers can be designed to consider a cost or loss value L for classification errors, including rejection. 41) This allows to model applications where certain misclassifications are more serious than others.

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