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

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 upon novel concepts in felds equivalent to, e.g., desktop imaginative and prescient, speech expertise, trainable classifiers, robotics, and digital truth. This publication places specified emphasis on technological features of complicated interface implementation. additionally it makes a speciality of interface layout and value. For readers with a heritage in engineering and laptop technological know-how, so much chapters supply layout instructions and case stories, in addition to an outline of the functioning and barriers of the algorithms required for implementation. additionally, complementary code examples in C++ are given the place applicable. As a different characteristic the e-book is observed via easy-to-handle software program improvement environments, which provide entry to broad public area software program for desktop imaginative and prescient, category, and digital fact. those environments additionally offer real-time entry to peripheral parts like, e.g., webcams or microphones, allowing hands-on experimentation and checking out.

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Advanced Man-Machine Interaction: Fundamentals and Implementation (Signals and Communication Technology)

Modern man-machine interfaces are more and more characterised by means of multimodality, nonintrusiveness, context-sensitivity, adaptivity, and teleoperability. The implementation of such homes is dependent upon novel ideas in felds resembling, e. g. , laptop imaginative and prescient, speech expertise, trainable classifiers, robotics, and digital fact.

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Not surprisingly, red is the dominant skin color component. (a) Source image (b) Object mask (c) Object color histogram Fig. 5. 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.

11 shows an algorithm that processes Iobj,mask and computes, for every region R, a list of its border points BR = {(x0 , y0 ), (x1 , y1 ), . . 14) Fig. 12 (a, b, c) shows the borders computed by this algorithm from the masks shown in Fig. 8 (b, c, d). In areas where the skin probability approaches the threshold Θ, the borders become jagged due to the random nature of the input data, as can be seen especially in (b) and (c). This effect causes an increase in border length that is random as well, which is undesirable because it reduces the information content of the computed border length value (similar shapes may have substantially different border lengths, rendering this feature less useful for recognition).

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. In this section, however, we stick to π1 = 1 and πi = 0 for i = 1. To achieve a compact notation for a complete HMM we introduce a vector containing the states’ distribution functions: B = (b1 b2 . . 74) where bi specifies the distribution of the observation o in state si .

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