Guest Editorial: Computational Vision at Brown

  • Michael J. Black
  • Benjamin B. Kimia
Editorial Board


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Andrews, S. and Laidlaw, D.H. 2002. Toward a framework for assembling broken pottery vessels. In Proceedings of the 18th National Conference on Artificial Intelligence, pp. 945-946.Google Scholar
  2. Andrews, S., Tsochantaridis, I., and Hofmann, T. 2003. Support vector machines for multiple-instance learning. In Advances in Neural Information Processing Systems (NIPS*15), MIT Press (to appear).Google Scholar
  3. Bakircioglu, M., Grenander, U., Khaneja, N., and Miller, M.I. 1998. Curve matching on brain surfaces using induced Frenet distance metrics. Human Brain Mapping, 6 (5):329-331.Google Scholar
  4. Balmelli, L., Taubin, G., and Bernardini, F. 2002 Space-optimized texture maps. In Proceedings of Eurographics 2002, Germany.Google Scholar
  5. Barzohar, M. and Cooper, D.B. 1996. Automatic finding of main roads in aerial images by using geometric-stochastic models and estimation. IEEE Trans. On PAMI, 18(7):707-721.Google Scholar
  6. Bernardini, F., Martin, I., Mittleman, J., Rushmeier, H., and Taubin, G. 2002. Building a digital model of Michelangelo's Florentine Pieta. IEEE Computer Graphics & Applications, January/ February 2002.Google Scholar
  7. Bienenstock, E. and Doursat, R. 1994. A shape-recognition model using dynamical links. Network: Computation in Neural Systems, 5:241-258.Google Scholar
  8. Bienenstock, E., Geman, S., and Potter, D. 1997. Compositionality, MDL priors, and object recognition. In Advances in Neural Information Processing Systems 9, M.C. Mozer, M.I. Jordan, and T. Petsche (Eds.), MIT Press, pp. 838-844.Google Scholar
  9. Black, M.J. and Anandan, P. 1996. The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields. Computer Vision and Image Understanding, CVIU, 63(1):75-104.Google Scholar
  10. Black, M.J. and Fleet, D.J. 2000. Probabilistic detection and tracking of motion discontinuities. International Journal of Computer Vision, 38(3):231-245.Google Scholar
  11. Black, M.J., Fleet, D.J., and Yacoob, Y. 2000. Robustly estimating changes in image appearance. Computer Vision and Image Processing, 78(1):8-31.Google Scholar
  12. Black, M.J. and Yacoob, Y. 1997. Recognizing facial expressions in image sequences using local parameterized models of image motion. Int. Journal of Computer Vision, 25(1):23-48.Google Scholar
  13. Black, M.J. and Jepson, A. 1998. EigenTracking: Robust matching and tracking of articulated objects using a view-based representation. Int. Journal of Computer Vision, 26(1):63-84.Google Scholar
  14. Blais, B.S., Intrator, N., Shouval, H., and Cooper, L.N. 1998. Receptive field formation in natural scene environments: Comparison of single cell learning rules. Neural Computation. 10(7):1797-1813.Google Scholar
  15. Blane, M., Lei, Z., Civi, H., and Cooper, D.B. 2000. The 3L algorithm for fitting implicit polynomial curves and surfaces to data. IEEE Trans. On PAMI, 22(3):298-313.Google Scholar
  16. Cooper, D.B., Willis, A., Andrews, S., Baker, J., Cao, Y., Han, D., Kang, K., Wong, W., Leymarie, F.F., Orriols, X., Velipasalar, S., Vote, E.L., Joukowsky, M.S., Kimia, B.B., Laidlaw, D.H., and Mumford, D. 2001. Assembling virtual pots from 3D measurements of their fragments. In Proc. Of the Virtual Reality Archaeology and Cultural heritage Symposium (VAST), Athens, Greece, Nov. 2001.Google Scholar
  17. Cooper, D.B., Willis, A., Andrews, S., Baker, J., Cao, Y., Han, D., Kang, K., Kong, W., Leymarie, F., Orriols, X, Velipasalar, S., Vote, E., Joukowsky, M., Kimia, B.J., Laidlaw, D.H., and Mumford, D. 2002. Bayesian virtual pot-assembly from fragments as problems in perceptual-grouping and geometric-learning. In Proceedings of ICPR, vol. 3, pp. 30297-30302.Google Scholar
  18. Domini, F. and Braunstein, M.L. 1998. Recovery of3D structure from motion is neither Euclidean nor affine. Journal of Experimental Psychology: Human Perception and Performance, 24:1273-1295.Google Scholar
  19. Domini, F. and Caudek, C. 1999. Perceiving surface slant from deformation of optic flow. Journal of Experimental Psychology: Human Perception and Performance, 25:426-444.Google Scholar
  20. Domini, F., Caudek, C., and Proffitt, D.R. 1997. Misperceptions of angular velocities influence the perception of rigidity in the kinetic depth effect. Journal of Experimental Psychology: Human Perception and Performance, 23:1111-1129.Google Scholar
  21. Domini, F., Caudek, C., and Richman, S. 1998. Distortions of depthorder relations and parallelism in structure from motion. Perception and Psychophysics, 60:1164-1174.Google Scholar
  22. Domini, F., Vuong, Q., and Caudek, C. 2002. Temporal integration in structure from motion. Journal of Experimental Psychology: Human Perception and Performance, 28:816-838.Google Scholar
  23. Duchon, A.P. and Warren, W.H. 2002. A visual equalization strategy for locomotor control: Of honeybees, humans, and robots. Psychological Science, 13:272-278.Google Scholar
  24. Duchon, A., Warren, W.H., and Kaelbling, L.P. 1998. Ecological robotics. Special issue of Adaptive Behavior, 6:473-507.Google Scholar
  25. Edelman, S. and Intrator, N. 2000. Coarse coding of shape fragments + retinotopy representation of structure. Spatial Vision, 13:255-264.Google Scholar
  26. Festa, E.K. and Welch, L. 1997. Recruitment mechanisms in speed and fine-direction discrimination tasks. Vision Research, 37(22):3129-3144.Google Scholar
  27. Gauthier, I., Hayward, W.G., Tarr, M.J., Anderson, A., Skudlarski, P., and Gore, J.C. 2002. BOLD activity during mental rotation and viewpoint-dependent object recognition. Neuron, 34(1):161-171.Google Scholar
  28. Geman, S. and Geman, D. 1984. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE-PAMI, 6:721-741.Google Scholar
  29. Geman, D., Geman, S., Graffigne, C., and Dong, P. 1990. Boundary detection by constrained optimization. IEEE-PAMI, 12:609-628.Google Scholar
  30. Geman, D., Geman, S., and McClure, D.E. 1992. Anonlinear filter for film restoration and other problems in image processing. CVGIP: Graphical Models and Image Processing, 54:281-289.Google Scholar
  31. Geman, S., Bienenstock, E., and Doursat, R. 1992. Neural networks and the bias/variance dilemma. Neural Computation, 4:1-58.Google Scholar
  32. Geman, S., Manbeck, K., and McClure, D.E. 1993. A comprehensive statistical model for single-photon emission tomography. In Markov Random Fields: Theory and Applications R. Chellappa and A. Jain (Eds.) Academic Press, Boston, pp. 93-130.Google Scholar
  33. Geman, S., Manbeck K., and McClure, D.E. 1993. A comprehensive statistical model for single photon emission computed tomography. In Markov Random Fields: Theory and Application, R. Chellappa and A. Jain (Eds.), Academic Press (Harcourt Brace Jovanovich), pp. 93-130.Google Scholar
  34. Geman S. and McClure, D.E. 1987. Statistical methods for tomographic image reconstruction. Bulletin of the International Statistical Institute, 52.Google Scholar
  35. Geman, S., Potter, D.F., and Chi, Z. 2002. Composition systems. Quarterly of Applied Mathematics, LX:707-736.Google Scholar
  36. Gidas, B. 1989. A renormalization group approach to image processing problems. IEEE Transactions PAMI, 11(2):164-180.Google Scholar
  37. Gidas, B., Almeida, M., and Robertson, C. 2000. Tracking of moving objects in cluttered enviroments via Monte Carlo filter. In Proceedings of the International Conference on Pattern Recognition (ICPR2000), Barcelona, Spain, pp. 175-179.Google Scholar
  38. Gidas, B. and Geman, D. 1991. Image analysis and computer vision. Spatial Statistics and Image Processing, National Research Council, National Academy Press, pp. 1-36.Google Scholar
  39. Gidas, B., Gomes, F. C., and Robertson, C. 2002. Model-based tracking of moving objects in cluttered enviroments. Quarterly of Applied Mathematics, LX(4):737-771.Google Scholar
  40. Gidas, B. and Mumford, D. 2001. Stochastic models for generic images. Quarterly of Applied Mathematics, LIX(1):85-111.Google Scholar
  41. Grenander, U. and Miller, M.I. 1998. Computational anatomy: An emerging discipline. Quarterly of Applied Mathematics, LVI(4):617-694.Google Scholar
  42. Grenander, U., Miller, M.I., and Srivastava, A. 1998. Hilbert-Schmidt lower bounds for estimators on matrix Lie groups for ATR. IEEE Trans. PAMI, 20(8):790-802.Google Scholar
  43. Grenander, U. and Srivastava, A. 2001. Probably models for clutter in natural images. IEEE Trans. PAMI, 23(4):424-429.Google Scholar
  44. Grimm, C., Crisco, J.J., and Laidlaw, D.H. 2002. Fitting manifold surfaces to 3D point clouds. Journal of Biomechanical Engineering, 124(1):136-140.Google Scholar
  45. Heller, A. and Mundy, J.L. 1991. Benchmark evaluation of a model-based object recognition system. In Proc. 3rd International Conference on Computer Vision, 1990, Reprinted in Computer Vision: Advances and Applications, R. Kasturi and R. Jain (Eds.) IEEE Computer Society Press.Google Scholar
  46. Hofmann, T. 2001. Unsupervised learning by probabilistic latent semantic analysis. Machine Learning Journal, 42(1):177-196.Google Scholar
  47. Hofmann, T., Puzicha, J., and Buhmann, J.M. 1998. Unsupervised texture segmentation in a deterministic annealing framework. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20 (8):803-818.Google Scholar
  48. Intrator, N. and Edelman, S. 1997. Learning low dimensional representations of visual object with extensive use of prior knowledge. Network, 8(3):283-296.Google Scholar
  49. Kang, K., Tarel, J.-P., Fishman, R., and Cooper, D.B. 2001. A linear dual-space approach to 3D surface reconstruction from occluding contours using algebraic surfaces. In Proc. IEEE Intl. Conf. On Computer Vision, Vancouver, Canada, pp. 198-204.Google Scholar
  50. Kearns, M., Warren, W.H., Duchon, A., and Tarr, M. 2002. Path integration from optic flow and body senses in a homing task. Perception, 31:349-374.Google Scholar
  51. Kimia, B.B. To Appear. On the role of medial geometry in human vision. Journal of Physiology, 2003.Google Scholar
  52. Kimia, B.B., Tannenbaum, A.R., and Zucker, S.W. 1992. On the evolution of curves via a function of curvature, I: The classical case. JMAA, 163(2):438-458.Google Scholar
  53. Kimia, B.B., Tannenbaum, A.R., and Zucker, S.W. 1995. Shapes, shocks, and deformations, I: The components of shape and the reaction-diffusion space. IJCV, 15(3):189-224.Google Scholar
  54. Kunsch, H., Geman, S., and Kehagias, A. 1995. Hidden Markov random fields. Ananals of Applied Probability, 5:577- 602.Google Scholar
  55. Kutliroff, G. 2002. Approximation in an Adaptive Cosine Basis and Its Application to Image Compression, Ph.D. Thesis, Division of Applied Mathematics, Brown University, May 2002. Available at Scholar
  56. Laidlaw, D.H., Fleischer, K.W., and Barr, A.H. 1998. Partial-volume Bayesian classification of material mixtures in MR volume data using voxel histograms. IEEE Transactions on Medical Imaging, 17(1):74-86.Google Scholar
  57. Lee, T. S. and Mumford, D., to appear. Hierarchical Bayesian inference in the visual cortex. J. of the Optical Society of America (submitted).Google Scholar
  58. Lee, A.B., Pedersen, K.S., and Mumford, D. 2003. The nonlinear statistics of high-contrast patches in natural images. Int. J. Comp. Vision. 54(1/2/3):83-103.Google Scholar
  59. Li, L. and Warren, W.H. 2000. Perception of heading during rotation: Sufficiency of dense motion parallax and reference objects. Vision Research, 40:3873-3894.Google Scholar
  60. Macevoy, S.P. and Paradiso, M.A. 2001. Lightness constancy in primary visual cortex. Proceedings of the National Academy of Science, 98:8827-8831.Google Scholar
  61. Matthews, N. and Welch, L. 1997. The effect of inducer polarity and contrast on the perception of illusory figures. Perception, 26:1431-1443.Google Scholar
  62. Miller, M., Christensen, G., Amit, Y., and Grenander, U. 1993. Mathematical textbook of deformable neuroanatomies. Proc. of the National Academy of Sciences, 90(24):11944-11948.Google Scholar
  63. Mumford, D. 2002. Pattern theory: The mathematics of perception. In Proceedings of ICM 2002, vol. 1, Beijing.Google Scholar
  64. Mumford, D. 2003. The shape of objects in two and three dimensions, Gibbs Lecture. Notices of the Amer. Math. Soc., to appear.Google Scholar
  65. Mumford, D. and Gidas, B. 2001. Stochastic models for generic images. Quarterly Appl. Math., 59:85-111.Google Scholar
  66. Mundy, J.L. 1995. Object recognition: The search for representation. Lecture Notes in Computer Science, vol. 994.Google Scholar
  67. Mundy, J.L. 1998. Object recognition based on geometry: Progress over three decades. Philosophical Transactions: Mathematical, Physical and Engineering Sciences, of the Royal Society, 356:1213-1231.Google Scholar
  68. Mundy, J.L. and Zisserman, A. 1992. Applications of Geometric Invariance in Computer Vision. MIT Press.Google Scholar
  69. Paradiso, M.A. 2002. Neuronal and perceptual correspondence in primary visual cortex. Current Opinion in Neurobiology, 12:155-161.Google Scholar
  70. Puzicha, J., Hofmann, T., and Buhmann, J.M. 1999. Histogram clustering for unsupervised segmentation and image retrieval. Pattern Recognition Letters, 20:899-909.Google Scholar
  71. Rossi, A.F. and Paradiso, M.A. 1999. Neural correlates of brightness in the responses of neurons in the retina, LGN, and primary visual cortex. Journal of Neuroscience, 19:6145-6156.Google Scholar
  72. Rothwell, C.A., Forsyth, D.A., Zisserman, A., and Mundy, J.L. 1993. Extracting projective structure from single perspective views of 3-d point sets. In Proc. 4th International Joint Conference on Computer Vision.Google Scholar
  73. Sebastian, T., C risco, J., and Kimia, B. 2003. Segmentation of carpal bones for accurate measurement of 3D in vivo carpal kinematics. Medical Image Analysis, 7(1):21-45.Google Scholar
  74. Sebastian, T., Klein, P., and Kimia, B.B. 2003. Recognition of shapes by editing their shock graphs. PAMI. Under minor revision.Google Scholar
  75. Sheinberg, D.L., Logothetis, N.K. 1997. The role of temporal cortical areas in perceptual organization. Proceedings of the National Academy of Sciences, 94:3408-3413.Google Scholar
  76. Sheinberg, D.L. and Logothetis, N.K. 2001. Noticing familiar objects in real world scenes: The role of temporal cortical neurons in natural vision. Journal of Neuroscience, 21:1340-1350.Google Scholar
  77. Sheinberg, D.L. and Logothetis, N.K. 2002. Perceptual learning and the development of complex visual representations in temporal cortical neurons. In Perceptual Learning, M. Fahle and T. Poggio (Eds.), MIT Press, 95-124.Google Scholar
  78. Siddiqi, K., Kimia, B.B., Tannenbaunm, A.R., and Zucker, S.W. 2001. On the psychophysics of the shape triangle. Vision Research, 41(9):1153-1178.Google Scholar
  79. Tarel, J.-P. and Cooper, D.B. 2000. The complex representation of algebraic curves and its simple exploitation for pose estimation and invariant recognition. IEEE Trans. On Pattern. Anal. and Machine Learning, 22(7):663-674.Google Scholar
  80. Tarr, M.J. and Cheng, Y.D. 2003 Learning to see faces and objects. TRENDS in Cognitive Sciences, 7(1):23-30.Google Scholar
  81. Tarr, M.J. and Gauthier, I. 2000. FFA: A Flexible Fusiform Area for subordinate-level visual processing automatized by expertise. Nature Neuroscience, 3(8):764-769.Google Scholar
  82. Tarr, M.J. and Warren, W.H. 2002. Virtual reality in behavioral neuroscience and beyond. Nature Neuroscience Supplement, 5:1089- 1092.Google Scholar
  83. Tarr, M.J., Williams, P., Hayward, W.G., and Gauthier, I. 1998. Three-dimensional object recognition is viewpoint-dependent. Nature Neuroscience, 1(4):275-277.Google Scholar
  84. Taubin, G. 2002. BLIC: Bi-level isosurface compression. In Proceedings of IEEE Visualization 2002, Boston.Google Scholar
  85. Taubin, G., Gueziec, A., Horn, W., and Lazarus, F. 1998. Progressive forest split compression. Siggraph'98, Orlando, FL.Google Scholar
  86. Taubin, G. and Rossignac, J. 1998. Geometric compression through topological surgery. ACM Transactions on Graphics, 17(2).Google Scholar
  87. van Dam, A., Laidlaw, D.H., and Simpson, R.M. 2002. Experiments in immersive virtual reality for scientific visualization. Computers and Graphics, 26(4):535-555.Google Scholar
  88. von der Malsburg, C. and Bienenstock, E. 1987. A neural network for invariant pattern recognition. Europhysics Letters, 4:121- 126.Google Scholar
  89. Warren, W.H. In press. Optic flow. In The Visual Neurosciences, ed L. Chalupa and J. Werner (Eds.), Cambridge, MA: MIT Press.Google Scholar
  90. Warren, W.H., Kay, B.A., Duchon, A.P., Zosh, W., and Sahuc, S. 2001. Optic flow is used to control human walking. Nature Neuroscience, 4:213-216.Google Scholar
  91. Welch, L., MacLeod, D.I.A., and McKee, S.P. 1997. Motion interference: Perturbing perceived direction. Vision Research, 37(19):2725-2736.Google Scholar

Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • Michael J. Black
    • 1
  • Benjamin B. Kimia
    • 2
  1. 1.Department of Computer ScienceBrown UniversityProvidenceUSA
  2. 2.Division of EngineeringBrown UniversityProvidenceUSA

Personalised recommendations