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Imaging systems for computer vision play an important role in today's world. Typical computer vision systems operate on large scale scenes, where objects are relatively far from the camera and the depth of field in which objects appear focussed is large. Close-range camera systems, on the other hand, typically have a narrow depth of field. World features outside this depth of field are blurred, and...
Imaging systems for computer vision play an important role in today's world, with applications ranging from automated assembly and tolerance analysis, to spacecraft guidance and control. The accuracy of many of these systems relies heavily upon calibration of the vision system, and although many algorithms and techniques exist for camera calibration, most are designed for large-scene applications...
High-fidelity haptic interaction in multi-user environments over general Ethernet-based local area networks (LAN) and metropolitan area networks (MAN) can be challenging but has promising applications. Under typical network traffic conditions, the 1kHz real-time control rate suggested in the literature for stable haptic simulation is well above that achievable by conventional network protocols such...
This paper is concerned with haptic simulation in multi-user virtual environments in which the users can haptically interact in a shared virtual world from separate workstations over an Ethernet local-area network (LAN). High-fidelity haptic rendering requires a minimum control update rate of 1000Hz which is beyond the capability of popular network protocols such as the UDP and TCP/IP. Consequently,...
A method is presented for deriving an optimal basis for features classified with a support vector machine. The method is based on minimizing the leave-one-out error which is approximated by the radius-margin bound. A gradient descent method provides a learning rule for the basis in an outer loop of an iteration. The inner loop performs support vector machine training and provides support vector coefficients...
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