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 houses depends on novel recommendations in felds equivalent to, e.g., laptop imaginative and prescient, speech know-how, trainable classifiers, robotics, and digital fact. This ebook places specified emphasis on technological features of complex interface implementation. additionally it makes a speciality of interface layout and value. For readers with a history in engineering and desktop technology, such a lot chapters supply layout guidance and case stories, 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 acceptable. As a different function the booklet is followed through easy-to-handle software program improvement environments, which supply entry to wide public area software program for computing device imaginative and prescient, type, and digital truth. those environments additionally offer real-time entry to peripheral parts like, e.g., webcams or microphones, allowing hands-on experimentation and trying out.
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Additional info for Advanced Man-Machine Interaction: Fundamentals and Implementation (Signals and Communication Technology)
The entailed loss in accuracy is negligible in most appli- 20 Non-Intrusive Acquisition of Human Action cations, especially when consumer cameras with high noise levels are employed. Usually the downsampling is even advantageous because it constitutes a generalization and therefore reduces the amount of data required to create a representative histogram. The LTI-L IB class skinProbabilityMap comes with the histograms presented in . The algorithm described in Fig. 6 is implemented in the class optimalThresholding.
54)). Thus far we have modeled the ﬁrst stochastic process, the amplitude of x˙ cog (t), for ﬁxed time segments. To extend the model to the second stochastic process inherent in x˙ cog (t), the variation in execution speed and temporal displacement apparent in Fig. 16, we abandon the rigid mapping of observations to states. As before, s1 and s12 are deﬁned as the initial and ﬁnal state, respectively, but the model is extended by allowing stochastic transitions between the states. At each time step the model may remain in its state si or change its state to si+1 or to si+2 .
We will ignore this for now and divide the time axis into segments of 5 observations each (one observation corresponds to one frame). 9 For each segment i, i = 1, 2, . . e. from 15 values each), as visualized in Fig. 16 by gray bars of length 2σi , centered at μi . 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.
Advanced Man-Machine Interaction: Fundamentals and Implementation (Signals and Communication Technology) by Karl-Friedrich Kraiss