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 means of multimodality, nonintrusiveness, context-sensitivity, adaptivity, and teleoperability. The implementation of such houses depends on novel thoughts in felds equivalent to, e.g., desktop imaginative and prescient, speech expertise, trainable classifiers, robotics, and digital truth. This ebook places particular emphasis on technological features of complex interface implementation. in addition it makes a speciality of interface layout and value. For readers with a historical past in engineering and desktop technology, such a lot chapters provide layout instructions and case experiences, in addition to an outline of the functioning and boundaries of the algorithms required for implementation. moreover, complementary code examples in C++ are given the place applicable. As a unique function the booklet is followed by way of easy-to-handle software program improvement environments, which supply entry to wide public area software program for computing device 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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For the moment we will assume these values to be given. In the following a general HMM with N states in Bakis topology is considered. The sequence of states adopted by this model is denoted by the sequence of their indices q = (q1 q2 . . 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 .

7 3. Create a list B of border points and store (x, y) as the first element. 4. Set (x , y ) = (x, y). 5. Scan the 8-neighborhood of (x , y ) using (x , y ), starting at the pixel that follows the butlast pixel stored in B in a counterclockwise orientation, or at (x − 1, y − 1) if B contains only one pixel. Proceed counterclockwise, skipping coordinates that lie outside of the image, until Iobj,mask (x , y ) = 1. If (x , y ) is identical with the first element of B, goto step 6. Else store (x , y ) in B.

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. 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.

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