B. K. Horn. Parallel networks for machine vision. Technical Report 1071, MIT AI Lab, December 1988. Home/Search Document Details and Download
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Gaussian Networks for Direct Adaptive Control - Sanner, Slotine (1991) (64 citations) (Correct)
....instead of modeled in software by discrete approximations. These facts suggest examining neural models for control which are amenable to a direct, physical implementation, for example, as the output of the continuous, analog electronic networks suggested by researchers in the field of early vision [12, 28]. Specifically, the goal of the research described in this paper is to develop adaptive architectures capable of exploiting these analog network designs for the control of continuous time, nonlinear dynamic systems. Our approach is to treat the entire problem in the context of adaptive systems ....
Horn, B.K.P., "Parallel networks for machine vision," Artificial Intelligence Lab. Memo, No. 1071, MIT, Cambridge, MA, December 1988. 29
Direct Adaptive Control Using Gaussian Networks - Sanner, Slotine (1991) (Correct)
....of modeled in software by discrete approximations. These facts suggest examining neural models for control which are amenable to a direct, physical implementation, for example, as the output of the continuous, analog electronic networks suggested by researchers in the field of early 1 vision [14, 32]. Our goal is hence to develop adaptive architectures capable of exploiting continuous analog networks for the control of continuous time, nonlinear dynamic systems. Our approach is to treat the entire problem in the context of adaptive systems theory, avoiding iterative training procedures in ....
Horn, B.K.P., "Parallel networks for machine vision," Artificial Intelligence Lab. Memo, No. 1071, MIT, Cambridge, MA, December 1988.
Computation of Smooth Optical Flow in a Feedback Connected.. - Stocker, Douglas (1998) (1 citation) (Correct)
....aVLSI implementation this requires a much shorter settling time constant for the network than the brightness changes in the image. 3 A Physical Analog Model 3. 1 Continuous space Standard regularization problems can be mapped onto electronic networks consisting of conductances and capacitors [5]. Hutchinson et al. 6] showed how resistive networks can be used to compute optical flow and Poggio et al. 7] introduced electronic network solutions for second order derivative optic flow computation. However, these proposed network architectures all require complicated and sometimes negative ....
B. K. Horn. Parallel networks for machine vision. Technical Report 1071, MIT AI Lab, December 1988.
Toward 3D Vision from Range Images: An Optimization Framework and.. - Li (Correct)
....In fact, the gradient based optimization algorithms work through local propagation in a parallel and distributed way and therefore are suited for implementation on neural network architectures. Much research has been devoted to developing such architectures for solving lower level problems [13, 39, 48, 58, 69, 79, 89, 98], and higher level ones mostly of combinatorial optimization [46, 77, 99] Table 8: about here = CVGIP: Image Understanding, 55(3) 231 260, May 1992 35 Table 9: about here = Table 10: about here = 7.2 Curvature Image Estimation and Segmentation Currently, the ....
B. K. P. Horn. "Parallel networks for machine vision". A. I. Memo. No. 1071, MIT, December 1988.
Computation of Smooth Optical Flow in a Feedback Connected.. - University And Eth (Correct)
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B. K. Horn. Parallel networks for machine vision. Technical Report 1071, MIT AI Lab, December 1988.
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