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Software or Hardware for Robot Vision?
Comparison of Five Methods for the Recognition
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adaptive ALEKSANDER algorithms applications approximately assembly automation binary bits Brunei classifier component contour contour lines coordinates curve defined described descriptor detection Digital discriminant function edge Electronics Letters emergent property example external operators feedback field of view gripping Hamming distance ideal position data image processing implementation Industrial Robots input internal holes iterative jaws learning length lines located machine manipulator memory elements method n-connected n-tuple Networks of memory neural optical orientation output overlap paper parallel parameters pattern class pattern recognition perceptron periphery photodiode picture pixels primitive figures problem processors Random Access Memory recognized reference object response ring robot vision scanning scene analysis scene table search range segment selected sequence shape shown in Figure simple STONHAM storage stored strategy structure sub-operations sucker task technique tion tower TV camera University of Kent University of Nottingham value of resemblance vector vision system visual WISARD workpieces
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Neural Network Design and the Complexity of Learning
J. Stephen Judd
Limited preview - 1990