Download 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 through multimodality, nonintrusiveness, context-sensitivity, adaptivity, and teleoperability. The implementation of such homes will depend on novel recommendations in felds similar to, e.g., desktop imaginative and prescient, speech expertise, trainable classifiers, robotics, and digital fact. This e-book places specified emphasis on technological facets of complex interface implementation. additionally it makes a speciality of interface layout and usefulness. For readers with a historical past in engineering and desktop technological know-how, such a lot chapters supply layout directions 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 unique characteristic the publication is followed via easy-to-handle software program improvement environments, which supply entry to large public area software program for computing device imaginative and prescient, class, and digital fact. those environments additionally offer real-time entry to peripheral elements like, e.g., webcams or microphones, allowing hands-on experimentation and trying out.

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

Example text

2 Feature Extraction The transition from low-level image data to some higher-level description thereof, represented as a vector of scalar values, is called feature extraction. In this process, Hand Gesture Commands 13 (a) “clockwise” (left to right) and “counterclockwise” (right to left) (b) “open” (left to right) and “close” (right to left) (c) “grab” (left to right) and “drop” (right to left) Fig. 3. The six dynamic gestures to be recognized. e. corresponds to reading the image sequence from right to left.

Hidden Markov Models (HMMs) for dynamic gestures. HMMs are frequently used for the classification of various dynamic processes, including speech and sign language. Further algorithms, such as artificial neural networks, can be found in the literature on pattern recognition and machine learning. A good starting point is [15]. 3 Feature Selection The feature classification stage receives, for every frame of input data, one feature vector from the feature extraction stage. For static gestures this constitutes the complete observation, while dynamic gestures are represented by a sequence of feature vectors.

Other skin colored objects may be visible, but they must be small compared to the hand. This also applies to the arm; it should be covered by a long-sleeved shirt if it is visible. In dynamic gestures the hand should have completely entered the image before recording starts, and not leave the image before recording ends, so that it is entirely visible in every recorded frame. Lighting Lighting is sufficiently diffuse so that no significant shadows are visible on the hand. Slight shadows, such as in Fig.

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