Albert, M.K. and Hoffman, D.D. 1995. Genericity in spatial vision. In D. Luce (Ed.), Geometric Representations of Perceptual Phenomena: Papers in Honor of Tarow Indow's 70th Birthday. L. Erlbaum (in press).
Albrecht D.G. and Geisler W.S. 1991. Motion selectivity and the contrast-response function of simple cells in the visual cortex. Visual Neuroscience
, 7:531–546.Google Scholar
Belhumeur, P.N. 1996. A computational theory for binocular stereopsis. In D. Knill and W. Richards (Eds.), Perception as Bayesian Inference. Cambridge University Press.
Berger, J.O. 1985. Statistical Decision Theory and Bayesian Analysis. Springer.
Bichsel, M. and Pentland, A.P. 1992. A simple algorithm for shape from shading. In Proc. IEEE CVPR, Champaign, IL, pp. 459–465.
Biederman I. 1985. Human image understanding: Recent research and a theory. Comp. Vis., Graphics, Image Proc.
, 32:29–73.Google Scholar
Binford T.O. 1981. Inferring surfaces from images. Artificial Intelligence
, 17:205–244.Google Scholar
Bleistein, N. and Handelsman, R.A. 1986. Asymptotic Expansions of Integrals. Dover.
Box G.E.P. and Tiao G.C. 1964. A Bayesian approach to the importance of assumptions applied to the comparison of variances, Biometrika
, 51(1, 2):153–167.Google Scholar
Box, G.E.P. and Tiao, G.C. 1973. Bayesian Inference in Statlstical Analysis. John Wiley and Sons, Inc.
Brainard, D.H. and Freeman, W.T. 1994. Bayesian method for recovering surface and illuminant properties from photosensor responses. In Proceedings of SPIE, vol. 2179, San Jose, CA.
Brooks M.J. and Horn B.K.P. 1989. Shape and source from shading. In B.K.P.Horn and M.J.Brooks (Eds.), Shape from Shading
, MIT Press: Cambridge, MA, Chap. 3.Google Scholar
Brooks M.J., Chojnacki W., and Kozera R. 1992. Impossible and ambiguous shading patterns. Int. J. Comp. Vis.
, 7(2):119–126.MathSciNetMATHGoogle Scholar
Bulthoff, H.H. 1991. Bayesian models for seeing shapes and depth. Journal of Theoretical Biology, 2(4).
Carandini M. and Heeger D.J. 1994. Summation and division by neurons in primate visual cortex. Science
, 264:1333–1336.Google Scholar
Cook, R.L. and Torrance, K.E., 1981. A reflectance model for computer graphics. In SIGGRAPH-81.
Cornsweet, T.N. 1970. Visual Perception. Academic Press.
Darrell, T., Sclaroff, S., and A. Pentland. 1990. Segmentation by minimal description. In Proc. 3rd Intl. Conf. Computer Vision, Osaka, Japan, IEEE, pp. 112–116.
Dickinson S.J., Pentland A.P., and Rosenfeld A., 1992. 3-d shape recovery distributed aspect matching. IEEE Pat. Anal. Mach. Intell.
, 14(2):174–198.Google Scholar
Fisher, R.A. 1959. Statistical Methods and Scientific Inference. Hafner.
Freeman, W.T. 1993. Exploiting the generic view assumption to estimate scene parameters. In Proc. 4th Intl. Conf. Comp. Vis., Berlin, IEEE, pp. 347–356.
Freeman W.T. 1994. The generic viewpoint assumption in a framework for visual perception. Nature
, 368(6471):542–545.Google Scholar
Freeman, W.T. and Brainard, D.H. 1995. Bayesian decision theory, the maximum local mass estimate, and color constancy. In Proc. 5th Intl. Conf. Comp. Vis., Boston, IEEE, pp. 210–217.
Freeman, W.T. 1996. The generic viewpoint assumption in a Bayesian framework. In D. Knill and W. Richards (Eds.) Perception as Bayesian Inference. Cambridge University Press.
Geman S. and Geman D. 1984. Stochastic relaxation, Gibbs distribution, and the Bayesian restoration of images. IEEE Pat. Anal. Mach. Intell.
, 6:721–741.Google Scholar
Grimson E. 1984. Bionocular shading and visual surface reconstruction. Comp. Vis., Graphics, Image Proc.
, 28:19–43.Google Scholar
Gull, S.F. 1988. Bayesian inductive inference and maximum entropy. In G.J. Erickson and C.R. Smith (Eds.), Maximum Entropy and Bayesian Methods in Science and Engineering, Kluwer, vol. 1.
Gull S.F. 1989. Developments in maximum entropy data analysis. In J.Skilling (Ed.), Maximum Entropy and Bayesian Methods
, Cambridge. Kluwer, pp. 53–71.Google Scholar
Heeger, D.J. and Simoncelli, E.P. 1992. Model of visual motion sensing. In L. Harris and M. Jenkin (Eds.), Spatial Vision in Humans and Robots. Cambridge University Press.
Horn B.K.P. 1989. Height and gradient from shading, Technical Report1105, MIT Artificial Intelligence Lab. MIT, Cambridge, MA 02139.Google Scholar
Horn B.K.P. and BrooksM.J. 1989. Shape from Shading
. MIT Press: Cambridge, MA.Google Scholar
Horn B.K.P., Szeliski R., and Yuille A. 1993. Impossible shaded images. IEEE Pat. Anal. Mach. Intell.
, 15(2):166–170.Google Scholar
Horn B.K.P., Woodham R.J., and Silver W.M. 1978. Determining shape and reflectance using multiple images. Technical Report 490, Artificial Intelligence Lab. Memo. Massachusetts Institute of Technology, Cambridge, MA 02139.Google Scholar
Human Vision, Visual Processing and Digital Display V.
Jeffreys H. 1961. Theory of Probability
. Clarendon Press: Oxford.Google Scholar
Jepson, A.D. and Richards, W. 1992. What makes a good feature? In L. Harris and M. Jenkin (Eds.), Spatial Vision in Humans and Robots. Cambridge Univ. Press. See also MIT AI Memo. 1356 (1992).
Johnson R.A. 1970. Asymptotic expansions associated with posterior distributions. The Annals of Mathematical Statistics
, 41(3):851–864.Google Scholar
Kersten D. 1991. Transparency and the cooperative computation of scene attributes. In M.S.Landy and J.A.Movshon (Eds.), Computational Models of Visual Processing
. MIT Press: Cambridge, MA, Chapter 15.Google Scholar
Knill, D.C., Kersten, D., and Yuille, A. 1996. A Bayesian formulation of visual perception. In D. Knill and W. Richards (Eds.), Perception as Bayesian Inference. Cambridge University Press.
Koenderink J.J. and vanDoorn A.J. 1979. The internal representation of solid shape with respect to vision. Biol. Cybern.
, 32:211–216.Google Scholar
Laplace, P.S. 1812. Theorie Analytique des Probabilites. Courcier.
Leclerc Y.G. 1989. Constructing simple stable descriptions for image partitioning. Intl. J. Comp. Vis.
, 3:73–102.Google Scholar
Leclerc, Y.G. and Bobick, A.F. 1991. The direct computation of height from shading. In Proc. IEEE CVPR, Maui, Hawii, pp. 552–558.
Lee C.-H. and Rosenfeld A. 1989. Improved methods of estimating shapefrom shading using the light source coordinate system. In B.K.P.Horn and M.J.Brooks (Eds.), Shape from Shading
. MIT Press: Cambridge, MA, Chapter 11.Google Scholar
Lindley, D.V. 1972. Bayesian Statistics, A Review. Society for Industrial and Applied Mathematics (SIAM).
Lowe D.G. and Binford T.O. 1985. The recovery of threedimensional structure from image curves. IEEE Pat. Anal. Mach. Intell.
, 7(3): 320–326.PubMedGoogle Scholar
MacKay D.J.C. 1992. Bayesian interpolation. Neural Computation
, 4(3): 415–447.Google Scholar
Malik J. 1987. Interpreting line drawings of curved objects. Intl. J. Comp. Vis.
, 1:73–103.Google Scholar
Nakayama K. and Shimojo S. 1992. Experiencing and perceiving visual surfaces. Science
, 257:1357–1363.Google Scholar
Papoulis A. 1984. Probability, Random Variables, and Stochastic Processes
. McGraw-Hill: New York.Google Scholar
Pentland A.P. 1984. Local shading analysis. IEEE Pat. Anal. Mach. Intell.
, 6(2): 170–187.Google Scholar
Pentland, A.P. 1990a. Photometric motion. In Proceedings of 3rd International Conference on Computer Vision.
Pentland A.P. 1990b. Automatic extraction of deformable part models. Intl. J. Comp. Vis.
, 4:107–126.Google Scholar
Pentland T., Torre V. and Koch C. 1985. Computational vision and regularization theory. Nature
, 317(26):114–139.Google Scholar
Press, W.H., Teukolsky, S.A., Vetterling, W.T., and Flannery B.P. 1992, Numerical Recipes in C. Cambrige University Press.
Richards W.A., Koenderink J.J., and Hoffman D.D. 1987. Inferring three-dimensional shapes from two-dimensional silhouettes. J. Opt. Soc. Am. A
, 4(7):1168–1175.Google Scholar
Schreiber, W.F. 1986. Fundamentals of Electronic Imaging Systems. Springer Verlag.
Skilling J. 1989. Classic maximum entropy. In J.Skilling (Ed.), Maximum Entropy and Bayesian Methods
. Cambridge, Kluwer, pp. 45–52.Google Scholar
Szeliski J. 1989. Bayesian Modeling of Uncertainty in Low-Level Vision
, Kluwer Academic Publishers: Boston.Google Scholar
Terzopoulos D. 1986. Regularization of inverse problems involving discontinuities. IEEE Pat. Anal. Mach. Intell.
, 8(4):413–424.Google Scholar
Tikhonov A.N. and Arsenin V.Y. 1977. Solutions of ll-Posed Problems
, Winston: Washington, DC.Google Scholar
Weinshall, D., Werman, M., and Tishby, N. 1994. Stability and likelihood of views of three dimensional objects. In Proceedings of the 3rd European Conference on Computer Vision, Stockholm, Sweden.
Witkin A.P. 1981. Recovering surface shape and orientation from texture. Artificial Intelligence
, 17:17–45.Google Scholar
Woodham R.J. 1980. Photometric method for determing surface orientation from multiple images. Optical Engineering
Yuille, A.L. and Bulthoff, H.H. 1996. Bayesian decision theory and psychophysics. In D. Knill and W. Richards (Eds.), Perception as Bayesian Inference. Cambridge University Press.
Zheng Q. and Chellapa R. 1991. Estimation of illuminant direction, albedo, and shape from shading. IEEE Pat. Anal. Mach. Intell.
, 13(7):680–702.Google Scholar