Read e-book online Advanced Man-Machine Interaction: Fundamentals and PDF

By Karl-Friedrich Kraiss

Modern man-machine interfaces are more and more characterised through multimodality, nonintrusiveness, context-sensitivity, adaptivity, and teleoperability. The implementation of such houses is determined by novel recommendations in felds comparable to, e.g., machine imaginative and prescient, speech know-how, trainable classifiers, robotics, and digital fact. This e-book places targeted emphasis on technological points of complicated interface implementation. additionally it makes a speciality of interface layout and value. For readers with a history in engineering and computing device technological know-how, so much chapters supply layout instructions and case stories, 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 acceptable. As a unique function the e-book is followed via easy-to-handle software program improvement environments, which supply entry to vast public area software program for machine imaginative and prescient, category, and digital truth. those environments additionally supply real-time entry to peripheral elements like, e.g., webcams or microphones, allowing hands-on experimentation and checking out.

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This can be Hand Gesture Commands (a) Region pixels 21 (b) Border points (c) Polygon Fig. 10. Description of an image region by the set of all of its pixels (a), a list of its border pixels (b), and a closed polygon (c). Light gray pixels in (b) and (c) are not part of the respective representation, but are shown for comparison with (a). interpreted as a closed polygon whose vertices are the centers of the border points (Fig. 10c). In the following, the object’s border is defined to be this polygon.

From a source image (a) and a corresponding, manually generated object mask (b), an object color histogram (c) is computed. 16 Non-Intrusive Acquisition of Human Action On the basis of hobject, color-based object detection can now be performed in newly acquired images of the object. The aim is to compute, from a pixel’s color, a probability or belief value indicating its likeliness of representing a part of the target object. This value is obtained for every pixel (x, y) and stored in a probability image Iobject(x, y) with the same dimensions as the analyzed image.

Fig. 19. Trellis diagram for an HMM with Bakis topology. The optimal state sequence q∗ can efficiently be computed using the Viterbi algorithm (see Fig. 84) t =2 All others need not be considered, because regardless of the subsequence of states following si , the resulting total probability would be lower than that achieved by following q∗i by the same subsequence. ) Viterbi Search Algorithm Define t as a loop index and Qt as a set of state sequences of length t. 1. Start with t = 1, Q1 = {(s1 )} and Qt = ∅ for t = 2, 3, .

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