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 houses depends on novel concepts in felds reminiscent of, e.g., computing device imaginative and prescient, speech know-how, trainable classifiers, robotics, and digital truth. This publication places specified emphasis on technological features of complex interface implementation. moreover it specializes in interface layout and usefulness. For readers with a historical past in engineering and desktop technology, such a lot chapters provide layout instructions and case reviews, in addition to an outline of the functioning and obstacles of the algorithms required for implementation. furthermore, complementary code examples in C++ are given the place acceptable. As a unique characteristic the booklet is observed by way of easy-to-handle software program improvement environments, which supply entry to broad public area software program for desktop imaginative and prescient, category, 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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Extra resources for Advanced Man-Machine Interaction: Fundamentals and Implementation (Signals and Communication Technology)
Fig. 5 shows the source image of a hand (a) and a corresponding, manually generated binary mask (b) that indicates object pixels (white) and background pixels (black). In (c) the histogram computed from (a), considering only object pixels (those that are white in (b)), is shown. The three-dimensional view uses points to indicate colors that occurred with a certain minimum frequency. The three one-dimensional graphs to the right show the projection onto the red, green, and blue axis. Not surprisingly, red is the dominant skin color component.
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. For example, let us consider a gesture recognition application that performs navigation of a menu structure. Misclassifying the gesture for “return to main menu” as “move cursor down” requires the user to perform “return to main menu” again.
Since “counterclockwise”, “close”, and “drop” are backwards executions of the above gestures, their feature plots are simply temporally mirrored versions of these plots. 5. Features computed from the boundaries shown in Fig. 13. /Max. 036 Fig. 14. Normalized area a , compactness c, and normalized x coordinate of the center of gravity xcog plotted over time t = 1, 2, . . , 60 for the dynamic gestures “clockwise” (left column), “open” (middle column), and “grab” (right column) from the example vocabulary shown in Fig.
Advanced Man-Machine Interaction: Fundamentals and Implementation (Signals and Communication Technology) by Karl-Friedrich Kraiss