By Karl-Friedrich Kraiss
Contemporary man-machine interfaces are more and more characterised by way of multimodality, nonintrusiveness, context-sensitivity, adaptivity, and teleoperability. The implementation of such houses is determined by novel concepts in felds similar to, e.g., desktop imaginative and prescient, speech know-how, trainable classifiers, robotics, and digital reality.
This publication places unique emphasis on technological facets of complex interface implementation. additionally it makes a speciality of interface layout and usability.
For readers with a historical past in engineering and computing device technological know-how, so much chapters supply layout instructions and case stories, in addition to an outline of the functioning and boundaries of the algorithms required for implementation. furthermore, complementary code examples in C++ are given the place appropriate.
As a unique characteristic the ebook is followed through easy-to-handle software program improvement environments, which supply entry to huge public area software program for computing device imaginative and prescient, class, and digital fact. those environments additionally supply real-time entry to peripheral elements like, e.g., webcams or microphones, allowing hands-on experimentation and testing.
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Modern man-machine interfaces are more and more characterised by way of multimodality, nonintrusiveness, context-sensitivity, adaptivity, and teleoperability. The implementation of such houses will depend on novel strategies in felds akin to, e. g. , desktop imaginative and prescient, speech know-how, trainable classifiers, robotics, and digital fact.
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Extra info for Advanced Man-Machine Interaction: Fundamentals and Implementation (Signals and Communication Technology)
Another example is to determine whether an object in an image sequence is moving or idle. 45) the hand is idle. where i and j are frame indices, and Θmotion speciﬁes the minimum dislocation for the hand to qualify as moving, relative to the image width. 5 Maximum Likelihood Classiﬁcation Due to its simplicity and general applicability, the concept of maximum likelihood classiﬁcation is widely used for all kinds of pattern recognition problems. An extensive discussion can be found in . This section provides a short introduction.
E. the class of each training sample is known. Classiﬁcation of a new observation is performed by a comparison with these samples (or models created from them) and yields the class that best matches the observation, according to a matching criterion. In unsupervised classiﬁcation the training samples are unlabeled. Clustering algorithms are used to group similar samples before classiﬁcation is performed. Parameters such as the number of clusters to create or the average cluster size can be speciﬁed to inﬂuence the clustering process.
It is obvious that, in order to determine intra- and inter-gesture variance, a feature has to be computed for numerous productions of every gesture in the vocabulary. g. by lighting conditions, image noise etc. For person-independent gesture recognition this should include multiple persons. Based on these samples features are then selected either by manual inspection according to the criteria mentioned above, or by an automatic algorithm that ﬁnds a feature vector which optimizes the performance of the chosen classiﬁer on the available data.