Ackley, D.H., Hinton, G.E., and Sejnowski, T.J. 1985. A learning algorithm for Boltzmann machines.

*Cognitive Science*, 9: 147–169.

Google ScholarAdelson, E.H., and Bergen, J.R. 1985. Spatioternporal energy models for the perception of motion.

*J. Opt. Soc. Amer.* A2: 284–299.

Google ScholarAloimonos, J., Weiss, I., and Bandyopadhyay, A. 1987. Active vision. In *Proc. 1st Intern. Conf. Comput. Vision*, London, pp. 35–54.

Anandan, P. 1989. A computational framework and an algorithm for the measurement of visual motion.

*Intern. J. Comput. Vision* 2: 283–310.

Google ScholarAnandan, P., and Weiss, R. 1985. Introducing a smoothness constraint in a matching approach for the computation of displacement fields. In *Proc. DARPA Image Understanding Workshop*, Miami Reach, FL, pp. 186–196.

Baker, H.H. and Bolles, R.C., 1989. Generalizing epipolar-plane image analysis on the spatiotemporal surface,

*Intern. J. Comput. Vision* 3: 33–49.

Google ScholarBarnard, S.T. 1989. Stochastic stereo matching over scale,

*Intern. J. Comput. Vision* 3: 17–32.

Google ScholarBarnard, S.T., and Fischler, M.A. 1982. Computational stereo.

*Computing Surveys* 14: 553–572.

Google ScholarBarrow, H.G., and Tenenbaum, J.M. 1978. Recovering intrinsic scene characteristics from images. In Allen R.Hanson and Edward M.Riseman (eds.)

*Computer Vision Systems*, pp. 3–26. Academic Press: New York.

Google ScholarBertero, M., Pogglo, T., and Torre, V. 1987. III-posed problems in early vision. A.I. Memo 924, Massachusetts Institute of Technology.

Bierman, G.J. 1977.

*Factorization Methods for Discrete Sequential Estimation*. Academic Press: New York.

Google ScholarBlake, A., and Zisserman, A. 1987.

*Visual Reconstruction* MIT Press: Cambridge, MA.

Google ScholarBolles, R.C., Baker, H.H., and Marimont, D.H. 1987. Epipolar-plane image analysis: An approach to determining structure from motion.

*Intern. J. Comput. Vision* 1: 7–55.

Google ScholarBoult, T.E. 1986. Information based complexity in non-linear equations and computer vision, Ph.D. thesis, Columbia University.

Bracewell, R.N. 1978.

*The Fourier Transform and Its Applications*. 2nd ed. McGraw-Hill: New York.

Google ScholarBriggs, W.L. 1987.

*A Multigrid Tutorial*. Society for Industrial and Applied Mathematics: Philadelphia.

Google ScholarBurt, P.J., and Adelson, E.H. 1983. The Laplacian pyramid as a compact image code.

*IEEE. Trans. Commun.* COM-31: 532–540.

Google ScholarCanny, J. 1986. A computational approach to edge detection.

*IEEE Trans. Patt. Anal. Mach. Intell.* PAMI-8: 679–698.

Google ScholarChen, L.-H., and Boult, T.E. 1988. An integrated approach to stereo matching, surface reconstruction and depth segmentation using consistent smoothness assumptions. In *Proc. DARPA Image Understanding Workshop*, Cambridge, MA, pp. 166–176.

Choi, D.J. 1987. Solving the depth interpolation problem on a fine grained, mesh-and tree-connected SIMD machine. In *Proc. DARPA Image Understanding Workshop*, Los Angeles, pp. 639–643.

Christ, J.P. 1987. Shape estimation and object recognition using spatial probability distributions. Ph.D. thesis, Carnegie Mellon University.

Craven, P., and Wahba, G. 1979. Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation.

*Numerische Mathematik* 31: 377–403.

Google ScholarCrowley, J.L., and Stern, R.M. 1982. Fast computation of the difference of low-pass transform. Tech. Rept. CMU-RI-TR-82-18: The Robotics Institute, Carnegic Mellon University

Dev, P. 1974. Segmentation processes in visual perception: a cooperative neural model. COINS Technical Report 74C-5, University of Massachusetts at Amherst.

Duda, R.O., and Hart, P.E. 1973.

*Pattern Classification and Scene Analysis*, Wiley: New York.

Google ScholarDurbin, R., Szeliski, R., and Yuille, A. 1989. An analysis of the elastic net approach to the travelling salesman problem.

*Neural Computation* 1: 348–358.

Google ScholarDurbin, R., and Willshaw, D. 1987. An analogue approach to the traveling salesman problem using an elastic net method.

*Nature* 326: 689–691.

Google ScholarDurrant-Whyte, H.F. 1987. Consistent integration and propagation of disparate sensor observations.

*Intern. J. Robotics Res.* 6: 3–24.

Google ScholarElfes, A., and Matthies, L. 1987. Sensor integration for robot navigation: Combining sonar and stereo range data in a grid-based representation. In *Proc. IEEE Conf. Decision and Control*.

Faugeras, O.D., Ayache, N., and Faverjon B. 1986. Building visual maps by combining noisy stereo measurements. In *Proc. IEEE Intern. Conf. Robotics and Automation*, San Francisco, pp. 1433–1438.

Faugeras, O.D., and Hebert, M. 1987. The representation, recognition and positioning of 3-D shapes from range data. In TakeoKanade (ed.),

*Three-Dimensional Machine Vision*. Kluwer Academic Publishers: Boston, pp. 301–353.

Google ScholarGamble, E., and Poggio, T. 1987. Visual integration and detection of discontinuities: The key role of intensity edges. A.I. Memo 970, Artif. Intell. Lab., Massachusetts Institute of Technology.

Geiger, D., and Girosi, F. 1989. Mean field theory for surface reconstruction. In *Proc. Image Understanding Workshop*, Palo Alto, CA, pp. 617–630.

Gelb, Arthur (ed.). 1974,

*Applied Optimal Estimation*, MIT Press: Cambridge, MA.

Google ScholarGeman, S., and Geman, D. 1984. Stochastic relaxation, Gibbs distribution, and the Bayesian restoration of images. In *IEEE Trans. Patt. Anal. Mach. Intell. PAMI-6*: 721–741.

Geman, S., and McClure, D.E. 1987. Statistical methods for tomographic image reconstruction. In *Proc. 46th Session of the Intern. Statistical Inst*.

Grimson, W.E.L. 1981.

*From Images to Surfaces: a Computional Study of the Human Early Visual System*. MIT Press: Cambridge, MA.

Google ScholarGrimson, W.E.L. 1983. An implementation of a computational theory of visual surface interpolation.

*Comput. Vision, Graphics, and Image Process*. 22: 39–69.

Google ScholarHackbusch, W. 1985.

*Multigrid Methods and Applications*. Springer-Verlag: Berlin.

Google ScholarHeeger, D.J. 1987. Optical flow from spatiotemporal filters. In *Proc. Ist Intern. Conf. Comput. Vision*, London, pp. 181–190.

Heel, J. 1989. Dynamic motion vision. In *Proc. Image Understanding Workshop*, Palo Alto, CA, pp. 702–713.

Hinton, G.E. 1977. Relaxation and its role in vision. Ph.D. thesis, University of Edinburgh.

Hinton, G.E., Sejnowski, T.J. 1983. Optimal perceptual inference In *Proc. Conf. Comput. Vision and Patt. Recog.*, Washington, D.C., pp. 448–453.

Hoff, W., and Ahuja, N. 1986. Surfaces from stereo. In *Proc. 8th Intern. Conf. Patt. Recog.*, Paris, pp. 516–518.

Horn, B.K.P. 1977. Understanding image intensities.

*Artificial Intelligence* 8: 201–231.

Google ScholarHorn, B.K.P., and Brooks, M.J. 1986. The variational approach to shape from shading.

*Comput. Vision, Graphics, Image Process*. 33: 174–208.

Google ScholarHorn, B.K.P., and Schunck, B.-G., 1981. Determining optical flow,

*Artificial Intelligence* 17: 185–203.

Google ScholarHueckel, M.H. 1971. An operator which locates edges in digitized pictures.

*J. Assoc. Comput. Mach.* 18: 113–125.

Google ScholarIkeuchi, K., and Horn, B.K.P. 1981. Numerical shape from shading and occluding boundaries.

*Artificial Intelligence* 17: 141–184.

Google ScholarJulesz, B. 1971.

*Foundations of Cyclopean Perception*. Chicago University Press: Chicago.

Google ScholarKass, M., Witkin, A., and Terzopoulos, D. 1988. Snakes: Active contour models.

*Intern. J. Comput. Vision* 1: 321–331.

Google ScholarKimeldorf, G., and Wahba, G. 1970. A correspondence between Bayesian estimation on stochastic processes and smoothing by splines.

*Ann. Math. Stat.* 41: 495–502.

Google ScholarKirkpatrick, S., Gelatt, C.D.Jr., and Vecchi, M.P. 1983. Optimization by simulated annealing.

*Science* 220: 671–680.

Google ScholarKoch, C., Marroquin, J., and Yuille, A. 1986. Analog “neuronal” networks in early vision.

*Proc. Nat. Acad. Sci. U.S.A.* 83: 4263–4267.

Google ScholarKonrad, J., and Dubois, E. 1988. Multigrid Bayesian estimation of image motion fields using stochastic relaxation. In *Proc. 2nd Intern. Conf. Comput. Vision*, Tampa, FL, pp. 354–362.

Leelere, Y.G., 1989. Constructing simple stable descriptions for image partitioning.

*Intern. J. Comput. Vision* 3: 75–102.

Google ScholarLowe, D.G. 1985.

*Perceptual Organization and Visual Recognition*. Kluwer Academic Publishers: Boston.

Google ScholarMandelbrot, B.B. 1982.

*The Fractal Geometry of Nature*. W.H. Frecman: San Francisco.

Google ScholarMarr, D. 1978. Representing visual information. In Allen R.Hanson and Edward M.Riseman (eds.),

*Computer Vision Systems*, pp. 61–80, Academic Press: New York.

Google ScholarMarr, D. 1982,

*Vision: A Computational Investigation into the Human Representation and Processing of Visual Information*. W.H. Freeman: San Francisco.

Google ScholarMarr, D., and Hildreth, E. 1980, Theory of edge detection.

*Proc. Roy. Soc. London* B 207: 187–217.

Google ScholarMarr, D., and Poggio, T. 1976. Cooperative computation of stereo disparity.

*Science* 194: 283–287.

Google ScholarMarroquin, J.L. 1984, Surface reconstruction preserving discontinuities. A.I. Memo 792, Artificial Intelligence Laboratory, Massachusetts Institute of Technology.

Marroquin, J.L. 1985. Probabilistic Solution of Inverse Problems. Ph.D. thesis, Massachusetts of Technology.

Matthies, L., and Shafer, S.A. 1987. Error inodeling in stereo navigation.

*IEEE J. Robotics Automation* RA-3: 239–248.

Google ScholarMatthies, L.H., Kanade, T., and Szeliski, R. 1989. Kalman filter-based algorithms for estimating depth from image sequences.

*Intern. J. Comput. Vision* 3: 209–236.

Google ScholarMcDermott, D. 1980. Spatial inferences with ground, metric formulas on simple objects. Department of Computer Science, Yale University, Res. Rept. 173.

Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., and Teller, E. 1953. Equations of state calculations by fast computing machines.

*J. Chem. Physics* 21: 1087–1091.

Google ScholarMoravec, H.P. 1988. Sensor fusion in certainty grids for mobile robots.

*Al Magazine* 9: 61–74.

Google ScholarPentland, A.P. 1986. Perceptual organization and the representation of natural form.

*Artificial Intelligence* 28: 293–331.

Google ScholarPoggio, T., Torre, V., and Koch, C. 1985. Computational vision and regularization theory.

*Nature* 317: 314–319.

Google ScholarPoggio, T., Voorhees, H., and Yuille, A. 1985. A regularized solution to edge detection. A. I. Memo 833. Artificial Intelligence Laboratory, Massachusetts Institute of Technology.

Poggio, T., et al. 1988. The MIT vision machine. In *Proc. DARPA Image Understanding Workshop*, Boston, pp. 177–198.

Rensink, R.A. 1986. *On the Visual Discrimination of Self-Similar Random Textures*. Master's thesis, The University of British Columbia.

Rives, P., Breuil, E., and Espiau, B. 1986. Recursive estimation of 3D features using optical flow and camera motion. In *Proc. Conf. Intell. Autonomous Systems*. pp. 522–532. (Also in 1987 *Proc. IEEE Intern. Conf. Robotics and Automation*.)

Roberts, L.G. 1965. Machine perception of three dimensional solids. In Tippett et al., (eds.),

*Optical and Electro-Optical Information Processing*, ch. 9, pp. 159–197, MIT Press: Cambridge, MA.

Google ScholarRosenfeld, A. 1980. Quadtrees and pyramids for pattern recognition and image processing. In *5th Intern. Conf. Patt. Recog.*, Miami Beach, FL, pp. 802–809.

Rosenfeld, A. (ed.). 1984,

*Multiresolution Image Processing and Analysis*, Springer-Verlag: New York.

Google ScholarRosenfeld, A., Hummel, R.A., and Zucker, S.W. 1976. Scene labeling by relaxation operations,

*IEEE Trans. Syst., Man, and Cybern.* SMC-6: 420–433.

Google ScholarSzeliski, R. 1986. Cooperative algorithms for solving random-dot stereograms. Tech. Rept. CMU-CS-86-133, Computer Science Department, Carnegie Mellon University.

Szeliski, R. 1987. Regularization uses fractal priors. In *Proc. 6th Nat. Conf. Artif. Intell.*, Seattle, pp. 749–754.

Szeliski, R. 1988. Estimating motion from sparse range data without correspondence. In *Proc. 2nd Intern. Conf. Comput. Vision*, Tampa, FL, pp. 207–216.

Szeliski, R. 1989.

*Bayesian Modeling of Uncertainty in Low-Level Vision*. Kluwer Academic Publishers: Boston.

Google ScholarSzeliski, R. 1990a. Fast shape from shading. In *Proc. 1st European Conf. Comput. Vision*, Antibes, Frane, pp. 359–368.

Szeliski, R. 1990b. Fast surface interpolation using hierarchical basis functions.

*IEEE Trans. Patt. Anal. Mach. Intell.* PAMI-12: 513–528.

Google ScholarSzeliski, R., and Terzopoulos, D. 1989a. From splines to fractals.

*Computer Graphies* 23: 51–60.

Google ScholarSzeliski, R., and Terzopoulos, D. 1988b. Parallel multigrid algorithms and computer vision applications. In *4th Copper Mountain Conf. on Multigrid Methods*, Copper Mountain, Colorado, pp. 383–398.

Terzopoulos, D. 1983. Multilevel computational processes for visual surface reconstruction.

*Comput. Vision, Graphics, Image Process.* 24: 52–96.

Google ScholarTerzopoulos, D. 1986a. Image analysis using multigrid relaxation methods.

*IEEE Trans. Patt. Anal. Mach. Intell.* PAMI-8: 129–139.

Google ScholarTerzopoulos, D. 1986b. Regularization of inverse visual problems involving discontinuities.

*IEEE Trans. Patt. Anal. Mach. Intell.* PAMI-8: 413–424.

Google ScholarTerzopoulos, D. 1987. Matching deformable models to images: Direct and iterative solutions. In *Topical Meeting on Machine Vision*, Washington, D.C., pp. 164–167.

Terzopoulos, D. 1988. The computation of visible-surface representations.

*IEEE Trans. Patt. Anal. Mach. Intell.* PAMI-10: 417–438.

Google ScholarTerzopoulos, D., Witkin, A., and Kass, M. 1987. Symmetry-seeking models and 3D object reconstruction.

*Intern. J. Comput. Vision* 1: 211–221.

Google ScholarTikhonov, A.N., and Arsenin, V.Y. 1977.

*Solutions of Ill-Posed Problems*, V.H. Winston: Washington, D.C.

Google ScholarTsai, R.Y., and Huang, T.S. 1984. Uniqueness and estimation of threedimensional motion parameters of rigid objects with curved surfaces.

*IEEE Trans. Patt. Anal. Mach. Intell.* PAMI-6: 13–27.

Google ScholarUllman, S. 1979.

*The Interpretation of Visual Motion*. MIT Press: Cambridge, MA.

Google ScholarVanEssen, D.C., and Maunsell, J.H.R. 1983. Hierarchical organization and functional streams in the visual cortex.

*Trends in Neuroscience* 6: 370–375.

Google ScholarVoss, R.F., 1985. Random fractal forgeries. In R.A.Earnshaw (ed.),

*Fundamental Algorithms for Computer Graphics*, Springer-Verlag, Berlin.

Google ScholarWahba, G. 1983. Bayesian “confidence intervals” for the crossvalidated smoothing spline.

*J. Roy. Statist. Soc.* B 45: 133–150.

Google ScholarWaltz, D.L. 1975. Understanding line drawing of scenes with shadows. In P.Winston, (ed.),

*The Psychology of Computer Vision*, McGraw-Hill, New York.

Google ScholarWitkin, A., Terzopoulos, D., and Kass, M. 1987, Signal matching through scale space.

*Intern. J. Comput. Vision* 1: 133–144.

Google ScholarYserentant, H. 1986. On the multi-level splitting of finite element spaces.

*Numerische Mathematik* 49: 379–412.

Google Scholar