Advanced Man-Machine Interaction: Fundamentals and by Karl-Friedrich Kraiss

By Karl-Friedrich Kraiss

Modern man-machine interfaces are more and more characterised by way of multimodality, nonintrusiveness, context-sensitivity, adaptivity, and teleoperability. The implementation of such homes depends upon novel strategies in felds resembling, e.g., laptop imaginative and prescient, speech know-how, trainable classifiers, robotics, and digital truth. This booklet places unique emphasis on technological points of complex interface implementation. moreover it makes a speciality of interface layout and usefulness. For readers with a history in engineering and machine technological know-how, such a lot chapters provide layout instructions and case reports, in addition to an outline of the functioning and obstacles of the algorithms required for implementation. additionally, complementary code examples in C++ are given the place applicable. As a different characteristic the booklet is observed by way of easy-to-handle software program improvement environments, which provide entry to huge public area software program for computing device imaginative and prescient, class, and digital truth. those environments additionally supply real-time entry to peripheral elements like, e.g., webcams or microphones, permitting hands-on experimentation and trying out.

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17. A simple model for the time dependent process x˙ cog (t) that rigidly maps 60 observations to 12 states. 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.

60 for the dynamic gestures “clockwise” (left column), “open” (middle column), and “grab” (right column) from the example vocabulary shown in Fig. 3. 3 Feature Classification The task of feature classification occurs in all pattern recognition systems and has been subject of considerable research effort. A large number of algorithms is available to build classifiers for various requirements. The classifiers considered in this section operate in two phases: In the training phase the classifier “learns” the vocabulary from a sufficiently large number of representative examples (the training samples).

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. Using the color attribute to localize an object in the image requires a definition of the object’s color or colors. In the RGB color model (and most others), even objects that one would call unicolored usually occupy a range of numerical values.

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