By Karl-Friedrich Kraiss

ISBN-10: 3540306188

ISBN-13: 9783540306184

Modern man-machine interfaces are more and more characterised by way of multimodality, nonintrusiveness, context-sensitivity, adaptivity, and teleoperability. The implementation of such homes is determined by novel thoughts in felds reminiscent of, e.g., computing device imaginative and prescient, speech expertise, trainable classifiers, robotics, and digital truth. This publication places designated emphasis on technological elements of complicated interface implementation. moreover it specializes in interface layout and value. For readers with a history in engineering and laptop technology, so much chapters provide layout directions and case reviews, 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 acceptable. As a distinct function the e-book is followed through easy-to-handle software program improvement environments, which provide entry to vast public area software program for laptop imaginative and prescient, class, and digital truth. those environments additionally offer real-time entry to peripheral parts like, e.g., webcams or microphones, permitting hands-on experimentation and checking out.

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Camera Resolution may vary, but should be at least 320 × 240. Aspect ratio remains constant. The camera is adjusted so that no overexposure and only minor color cast occurs. ) For dynamic gestures, a frame rate of 25 per second is to be used, and shutter speed should be high enough to prevent motion blur. A consumer quality camera is sufficient. such as those shown in Fig. 3. This process, which occurs within the feature extraction module in Fig. 1, is visualized in Fig. 4. Fig. 4. Visualization of the feature extraction stage.

An extensive discussion can be found in [22]. This section provides a short introduction. We assume the observation O to be a vector of K scalar features fi : ⎛ ⎞ f1 ⎜ f2 ⎟ ⎟ ⎜ O=⎜ . 46) ⎝ .. 50) ω∈Ω Estimation of Observation Probabilities For reasons of efficiency and simplicity, the observation probability P (O|ω) is commonly computed from a class-specific parametric distribution of O. This distribution is estimated from a set of training samples. Since the number of training samples is often insufficient to identify an appropriate model, a normal distribution is assumed in most cases.

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. Since the hand has 27 degrees of freedom (DOF), this would require an amount of information that cannot be extracted with sufficient accuracy and speed.

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


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