Abstract
The detection of discontinuities in motion, intensity, color, and depth is a well studied but difficult problem in computer vision. We discuss our “resistive fuse” circuit—the first hardware circuit that explicitly implements either analog or binary line processes in a controlled fashion. We have successfully designed and tested an analog CMOS VLSI circuit that contains a 1-D resistive network of fuses implementing piece-wise smooth surface interpolation. The segmentation ability of this network is demonstrated for a noisy step-edge input.
We derive the specific current-voltage relationship of the resistive fuse from a number of computational considerations, closely related to the early vision algorithms of Koch, Marroquin and Yuille (1986) and Blake and Zisserman (1987). We discuss the circuit implementation and the performance of the chip. In the last section, we show that a model of our resistive network—in which the resistive fuses have no internal dynamics—has an associated Lyapunov function, the co-content. The network will thus converge, without oscillations, to a stable solution, even in the presence of arbitrary parasitic capacitances throughout the network.
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References
Birkhoff, G. and Diaz, J. B. (1956). Nonlinear network problems. Quart. Appl. Math. 13:431–443.
Blake, A. (1989). Comparison of the efficiency of deterministic and stochastic algorithms for visual reconstruction. IEEE Trans. Pattern Anal. Mach. Intell. 11:2–12.
Blake, A. and Zisserman, A. (1987). Visual Reconstruction. Cambridge, MA: MIT Press.
Brayton, R. K. and Moser, J. K. (1964). A theory of nonlinear networks—I, II. Quart. Appl. Math. 22(1):1–33 (April)
Brayton, R. K. and Moser, J. K. (1964). A theory of nonlinear networks—I, II. Quart. Appl. Math. 22(2):81–104 (July).
Chua, L. O., Desoer, C. A., and Kuh, E. S. (1987). Linear and Nonlinear Circuits. New York: McGraw-Hill, pp. 23–34.
Duffin, R. J. (1947). Nonlinear networks IIa. Bull. Amer. Math. Soc. 53:963–971.
Geman, S. and Geman, D. (1984). Stochastic relaxation, Gibbs distribution and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6:721–741.
Grimson, W. E. L. (1981). From Images to Surfaces. Cambridge, MA: MIT Press.
Harris, J. G. (1989). An analog VLSI chip for thin plate surface interpolation. In Neural Information Processing Systems, ed. D. Touretzky. Palo Alto: Morgan Kaufmann.
Harris, J. G. and Koch, C. (1989). Resistive fuses: circuit implementations of line discontinuities in vision. Snowbird Neural Network Workshop, April 4–7.
Harris, J. G., Koch, C., Staats, E., Luo, J. and Wyatt, J. (1989). Analog hardware for detecting discontinuities in early vision: computational justification and VLSI circuits, in preparation.
Hasler, M. and Neirynck, J., (1986). Nonlinear Circuits. Norwood, MA: Artech House Inc., pp. 172–173.
Hildreth, E. C. (1984). The Measurement of Visual Motion. Cambridge, MA: MIT Press.
Hopfield, J. J. and Tank, D. W. (1985). Neural computation in optimization problems. Biol. Cybern. 52:141–152.
Horn, B. K. P. (1989). Parallel networks for machine vision. Artif. Intell. Lab. Memo No. 1071 (MIT, Cambridge).
Horn, B. K. P. and Schunck, B. G. (1981). Determining optical flow. Artif. Intell. 17:185–203.
Ikeuchi, K. and Horn, B. K. P. (1981). Numerical shape from shading and occluding boundaries. Artif. Intell. 17:141–184.
Hutchinson, J., Koch, C., Luo, J., and Mead, C. (1988). Computing motion using analog and binary resistive networks. IEEE Computer 21:52–63.
Ikeuchi, K. and Horn, B. K. P. (1981). Numerical shape from shading and occluding boundaries. Artif. Intell. 17:141–184.
Koch, C., Marroquin, J., and Yuille, A. (1986). Analog “neuronal” networks in early vision. Proc. Natl. Acad. Sci. USA 83:4263–4267.
Koch, C. (1989). Seeing chips: analog VLSI circuits for computer vision. Neural Computation 1:184–200.
Liu, S. C. and Harris, J. G. (1989). Generalized smoothing networks in solving early vision problems. Computer Vision and Pattern Recognition Conference.
Luo, J., Koch, C., and Mead, C. (1988). An experimental subthreshold, analog CMOS two-dimensional surface interpolation circuit. Neural Information Processing Systems Conference, Denver, November.
Marr, D. and Poggio, T. (1976). Cooperative computation of stereo disparity. Science 194:283–287.
Marroquin, J., Mitter, S., and Poggio, T. (1987). Probabilistic solution of ill-posed problems in computational vision. J. Am. Statistic Assoc. 82:76–89.
Maxwell, J. C. (1891). A Treatise on Electricity and Magnetism, 3rd ed., Vol. I, pp. 407–408. Republished by New York: Dover Publications, 1954.
Mead, C. A. (1985). A sensitive electronic photoreceptor. In 1985 Chapel Hill Conference on Very Large Scale Integration, pp. 463–471.
Mead, C. A. (1989). Analog VLSI and Neural Systems. Reading: Addison-Wesley.
Millar, W. (1951). Some general theorems for non-linear systems possessing resistance. Phil. Mag. 42:1150–1160.
Nagel, H. H. (1987). On the estimation of optical flow: relations between different approaches and some new results. Artif. Intell. 33:299–324.
Penfield, P., Jr., Spence, R., and Duinker, S. (1970). Tellegen’s Theorem and Electrical Networks, Cambridge, MA: MIT Press.
Perona, P. and Malik, J. (1988). A network for multiscale image segmentation. Proc. 1988 IEEE Int. Symp. on Circuits and Systems, Espoo, Finland, June, pp. 2565–2568.
Poggio, T., Gamble, E. B., and Little, J. J. (1988). Parallel integration of vision modules. Science 242:436–440.
Poggio, T. and Koch, C. (1985). Ill-posed problems in early vision: from computational theory to analogue networks. Proc. R. Soc. Lond. B 226:303–323.
Poggio, T., Torre, V., and Koch, C. (1985). Computational vision and regularization theory. Nature 317:314–319.
Poggio, T., Voorhees, H., and Yuille, A. (1986). A regularized solution to edge detection. Artif. Intell. Lab Memo No. 833 (MIT, Cambridge).
Sivilotti, M.A., Mahowald, M.A. and Mead, C.A., Real-time visual computation using analog CMOS processing arrays. In: 1987 Stanford Conf. VLSI, pp. 295–312 (MIT Press, Cambridge, 1987).
Standley, D. L., and Wyatt, J. L., Jr. (1989). Stability criterion for lateral inhibition and related networks that is robust in the presence of integrated circuit parasitics. In IEEE Trans. Circuits and Systems 36, May., pp. 675–681
Standley, D. L. (1989). Design criteria extensions for stable lateral inhibition networks in the presence of circuit parasitics. Proc. 1989 IEEE Int. Symp. on Circuits and Systems, Portland, Oregon, May, pp. 837–840.
Tellegen, B. D. H. (1952). A general network theorem, with applications. Phillips Research Reports 7:259–269.
Terzopoulos, D. (1983). Multilevel computational processes for visual surface reconstruction. Comp. Vision Graph. Image Proc. 24: 52–96.
Terzopoulos, D. (1986). Regularization of inverse problems involving discontinuities. IEEE Trans. Pattern Anal. Machine Intell. 8:413–424 (1986).
Wyatt, J. L., Jr. and Standley, D. L. (1989). Criteria for robust stability in a class of lateral inhibition networks coupled through resistive grids. Neural Computation 1:58–67.
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Harris, J., Koch, C., Luo, J., Wyatt, J. (1989). Resistive Fuses: Analog Hardware for Detecting Discontinuities in Early Vision. In: Mead, C., Ismail, M. (eds) Analog VLSI Implementation of Neural Systems. The Kluwer International Series in Engineering and Computer Science, vol 80. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1639-8_2
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DOI: https://doi.org/10.1007/978-1-4613-1639-8_2
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