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 homes depends on novel thoughts in felds reminiscent of, e.g., computing device imaginative and prescient, speech know-how, trainable classifiers, robotics, and digital fact. This booklet places distinct emphasis on technological points of complex interface implementation. additionally it makes a speciality of interface layout and usefulness. For readers with a history in engineering and laptop technological know-how, so much chapters supply 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 function the booklet is followed via easy-to-handle software program improvement environments, which supply entry to broad public area software program for machine imaginative and prescient, category, and digital truth. those environments additionally offer real-time entry to peripheral parts like, e.g., webcams or microphones, allowing hands-on experimentation and trying out.
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Additional info for Advanced Man-Machine Interaction: Fundamentals and Implementation (Signals and Communication Technology)
In case the recording conditions remain constant and are known in advance, Θ can be set manually, but for uncontrolled environments an automatic determination is desirable. Two different strategies are presented below. If no knowledge of the target object’s shape and/or position is available, the following iterative low-level algorithm presented in  produces good results in a variety of conditions. It assumes the histogram of Iobj,prob to be bi-modal but can also be applied if this is not the case.
A source image (a) and the corresponding skin probability image (b). An object/background classiﬁcation was performed for three different thresholds Θ1 < Θ2 < Θ3 (c-e). While the human perception of an object’s color is largely independent of the current illumination (an effect called color constancy), colors recorded by a camera are strongly inﬂuenced by illumination and hardware characteristics. 4 In , a skin and a non-skin color histogram are presented that were created from several thousand images found on the WWW, covering a multitude of skin 4 In many digital cameras, color constancy can be achieved by performing a white balance: The camera is pointed to a white object (such as a blank sheet of paper), and a transformation is performed so that this object appears colorless (r = g = b) in the image as well.
Finally the feature must be computable with sufﬁcient accuracy and speed. A certain feature’s suitability thus depends on the constellation of the vocabulary (because this affects inter- and intra-gesture variance) and on the actual application scenario in terms of recording conditions, hardware etc. It is therefore a common approach to ﬁrst compute as many features as possible and then examine each one with respect to its suitability for the speciﬁc recognition task (see also Sect. 3). The choice of features is of central importance since it affects system design throughout the processing chain.
Advanced Man-Machine Interaction: Fundamentals and Implementation (Signals and Communication Technology) by Karl-Friedrich Kraiss