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

By Karl-Friedrich Kraiss

ISBN-10: 3540306188

ISBN-13: 9783540306184

Modern man-machine interfaces are more and more characterised via multimodality, nonintrusiveness, context-sensitivity, adaptivity, and teleoperability. The implementation of such homes depends upon novel strategies in felds similar to, e.g., computing device imaginative and prescient, speech expertise, trainable classifiers, robotics, and digital truth. This booklet places particular emphasis on technological features of complicated interface implementation. in addition it makes a speciality of interface layout and usefulness. For readers with a history in engineering and machine technological know-how, so much chapters provide layout instructions and case experiences, in addition to an outline of the functioning and barriers of the algorithms required for implementation. moreover, complementary code examples in C++ are given the place applicable. As a unique function the e-book is observed by way of easy-to-handle software program improvement environments, which supply entry to wide public area software program for desktop imaginative and prescient, class, and digital fact. those environments additionally supply real-time entry to peripheral parts like, e.g., webcams or microphones, allowing hands-on experimentation and trying out.

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The six dynamic gestures to be recognized. e. corresponds to reading the image sequence from right to left. irrelevant information (background) is discarded, while relevant information (foreground or target) is isolated. Most pattern recognition systems perform this step, because processing the complete image is computationally too demanding and introduces an unacceptably high amount of noise (images often contain significantly more background pixels than foreground pixels). Using two-dimensional images of the unmarked hand, it is at present not possible to create a three-dimensional model of a deformable object as complex and flexible as the hand in real-time.

QT ) with qt ∈ {1, 2, . . , N }, t = 1, 2, . . 70) where qt indicates the state index at time t and T equals the number of observations. 72) One may want to loosen the restriction that q1 = s1 by introducing an initial state probability vector π: 10 If the number of states N is significantly smaller than the number of observations T , acceleration can also be modeled solely by the transition from si to si+1 . Hand Gesture Commands π = (π1 π2 . . 73) i This can be advantageous when recognizing a continuous sequence of gestures (such as a sentence of sign language) and coarticulation effects might lead to the initial states of some models being skipped.

Transitions from si to si allow to model a “slower-thannormal” execution part, while those from si to ss+2 model a “faster-than-normal” execution part. 69) is not a general requirement for HMMs; it is a property of the specific Bakis topology used in this introduction. g. a transition from si to si+3 , in order to optimize classification performance. In this section we will stick to the Bakis topology because it is widely used 40 Non-Intrusive Acquisition of Human Action Fig. 18. A Hidden Markov Model in Bakis topology.

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Advanced Man-Machine Interaction: Fundamentals and Implementation (Signals and Communication Technology) by Karl-Friedrich Kraiss


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