Visual Shape Computation

Proceedings of the IEEE

Volumn 76, Issue 8, 1988, Pages 899-916

 

^a Yiannis) Aloimonos, John   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE PROCESSING -- RECONSTRUCTION; SURFACES;

ACTIVE VISION; ILLPOSED PROBLEMS; REGULARIZATION THEORY; VISUAL SHAPE COMPUTATION;

VISION;

EID: 0024056020     PISSN: 00189219     EISSN: 15582256     Source Type: Journal    
DOI: 10.1109/5.5964     Document Type: Article

References (110)

1. J. A. Feldman, “Four frames suffice: A provisional model of vision and space”, Behavioral and Brain Sciences, June 1985.
2. D. Marr, Vision. San Francisco, CA: Freeman, 1981.
3. L. Weiskrantz, E. K. Warrington, M. D. Sanders, and J. Marshall, “Visual capacity in the hemianopic field following a restricted occipital ablation”, Brain, vol. 97, pp. 709–728, 1974.
4. K. A. Stevens, “Surface perception by local analysis of texture and contour”, Al Tech. Rep. 512, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 1980.
5. J. C. Marshall and F. Newcombe, “Patterns of paralexia”, Psycholinguis. Res., vol. 2, pp. 175–199, 1973.
6. J. J. Gibson, This Perception of the Visual World. Boston, MA: Houghton-Mifflin, 1950.
7. E. H. Land and J. J. McCann, “Lightness and retinex theory”, J. Opt. Soc. Am., vol. 61, pp. 1–11, 1971.
8. B. Julesz, Foundations of Cyclopean Perception. Chicago, IL: Univ. of Chicago Press, 1971.
9. M. Brady, “Computational approaches to image understanding”, ACM Computing Surveys, vol. 14, Mar. 1982.
10. B. K. P. Horn and M. J. Brooks, “The variational approach to shape from shading”, Al Memo 813, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, Mar. 1985.
11. J. Aloimonos and M. Swain, “Paraperspective projection: Between orthography and perspective”, CAR-TR-320, Computer Vision Laboratory, Center for Automation Research, University of Maryland, College Park, MD, Aug. 1987.
12. B. K. P. Horn, Robot Vision. New York, NY: McGraw-Hill, 1986.
13. A. Pentland, “Perceptual organization and the representation of natural form”, Artif. Intell., vol. 28, no. 3, pp. 293–331, 1986.
14. R. Bajcsy and F. Solina, “Three dimensional object representation revisited”, in Proc. 1st International Conf. on Computer Vision, ‘London, England’, pp. 321-340, 1987.
15. D. Mumford and M. Shah, personal communication.
16. B. K. P. Horn, “Understanding image intensities”, Artif. Intell., vol. 8, pp. 201–231, 1977.
17. B. K. P. Horn and R. W. Sjoberg, “Calculating the reflectance map”, Appl. Opt., vol. 18, pp. 1770–1779, 1979.
18. S. Shafer, Ph.D. Thesis, Dept. of Computer Science, Carnegie-Mellon University, 1982.
19. K. Ikeuchi and B. K. P. Horn, “Numerical shape from shading and occluding boundaries”, Artif. Intell., vol. 17, pp. 141–144, 1981.
20. R. Courant and D. Hilbert, Methods of Mathematical Physics. New York, NY: Interscience, 1955.
21. H.G. Barrow and J. M.Tenenbaum, “Computational vision”, Proc. IEEE, vol. 69, pp. 572–595, 1981.
22. B. K. P. Horn, “The curve of least energy”, Al Memo 610, Artificial Intelligence Laboratory, Massachusetts Instituteof Technology, Cambridge, MA, 1981.
23. R. Frankot and R. Chellappa, “A method for enforcing integrability in shape from shading algorithms”, in Proc. 1st International Conf. on Computer Vision, ‘London, UK’, pp. 118-127, 1987.
24. M.’. Brooks, “Surface normals from closed paths”, in Proc. 1979 Int. Jt. Conf. Artificial Intelligence, ‘Tokyo, Japan’, vol. 6, pp. 98–101, 1979.
25. T. M. Strat, “A numerical method for shape from a single image”, M.Sc. Thesis, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 1979.
26. R.J. Woodham, “Analyzing images of curved surf aces”, Artif. Intell., vol. 17, pp. 117–141, 1981.
27. A. R. Bruss, “The eikonal equation: some results applicable to computer vision”, J. Math. Phys., vol. 23, no. 5, pp. 890–896, May 1982.
28. A. P. Pentland, “Local shading analysis”, IEEE Trans. Pattern Anal. Machine Intell., vol. 6, pp. 170–187, Mar. 1984.
29. A. P. Pentland, “Finding the illuminant direction”, Optical Soc. Amer., vol. 72, no. 4, pp. 448–455, Apr. 1982.
30. C.-H. Lee, “Improved methods of estimating shape from shading using the light source coordinate system”, CS-TR-1277, Computer Vision Laboratory, Center for Automation Research, University of Maryland, College Park, MD, Oct. 1983.
31. M.J. Brooks and B. K. P. Horn, “Shape and source from shading”, in Proc. of the Intern, joint Conf. on Artificial Intelligence, ‘Los Angeles, CA’ pp. 932-936, Aug. 1985.
32. J. J. Koenderick and A. J. van Doom, “Photometric invariants related to solid shape”, Acta Optica, vol. 27, pp. 981–996, July 1980.
33. E. Grimson, “Binocular shading and visual surface reconstruction”, CVGIP, pp. 18-44, 1984.
34. B. Phong, “Illumination for computer generated pictures”, Commun. ACM, vol. 18, pp. 311–317, 1975.
35. E. Coleman and R. Jain, “Obtaining 3-D shape of textured and specular surfaces using four-source photometry”, CVGIP, vol. 18, pp. 309–328, 1982.
36. A. Blake, “Specular stereo”, in Proc. IJCAI, vol. 9, ‘Los Angeles, CA’, pp. 973–976, 1985.
37. G. Healey and T. O. Binford, “Local shape from specularity”, in Proc. 1st ICCV, ‘London, UK’, pp. 151-160, 1987.
38. J. R. Kender and E. M. Smith, “Shape from darkness: Deriving surface information from dynamic shadows”, Proc. of the National Conf. on Artificial Intelligence, pp. 664-669, Aug. 1986.
39. P. Deift and J. Sylvester, “Some remarks on the shape-from-shading problem in computer vision”, J. Math. Anal. Appl., vol. 84, pp. 235–248, Nov. 1981.
40. A. P. Witkin, “Recovering surface shape and orientation from texture”, Artif. Intell., vol. 17, pp. 17–47, 1981.
41. R. Bajcsy and L. Lieberman, “Texture gradient as a depth cue”, Comp. Gr. Image Process, vol. 5, pp. 52–67, 1976.
42. J. Kender, Ph.D. Thesis, Carnegie-Mellon University, Pittsburgh, PA, 1980.
43. J. Kender, “Shape from texture: An aggregation transform that maps a class of textures into surface orientation”, in Proc. IJCAI, pp. 475-480, 1981.
44. K.-l. Kanatani, “Detectionof surfaceorientationand motion from texture by a stereological technique”, Artif. Intell., vol. 23, pp. 213–237, 1984.
45. J. Aloimonos, Ph.D. Thesis, Department of Computer Science, University of Rochester, Rochester, NY, 1986.
46. J. Aloimonos, Ph.D. Thesis, “Shape from texture”, Biological Cybernetics, vol. 58, pp. 345–360, 1988.
47. Walker and T. Kanade, “Shape from patterns”, private communication.
48. K. Ikeuchi, “Shape from regular patterns”, Artif. Intell., vol. 22, pp. 29–75, 1984.
49. J. Aloimonos and M. Swain, “Shape from patterns: Regularization”, to appear in Int. J. Computer Vision, 1988.
50. H. Li, M. A. Lavin, and R.J. LeMaster, “Fast Hough transform: A hierarchical approach”, CVGIP, vol. 36, pp. 139–161, 1981.
51. M. L. Braustein and J. W. Payne, “Perspective and form ration as determinants of relative slant judgements”, J. Exp. Psychol., vol. 3, pp. 584–590, 1969.
52. H.G. Barrow and J. M.Tenenbaum, “Interpreting line drawings as three dimensional surfaces”, Artif. Intell., vol. 17, pp. 75–117, 1981.
53. T. Kanade, “Recovery of the three-dimensional shape of an object from a single view”, CMU-CS-79-153, Dept. of Computer Science, Carnegie-Mellon University, 1979.
54. M. Brady and A. Yuille, “An extremum principle for shape from contour”, IEEE Trans. Pattern Anal. Machine Intell., vol. 6, 1984.
55. R. Horaud and M. Brady, “On the geometric interpretation of image contours”, in Proc. 1stICCV, ‘London, UK’, pp. 374-382, 1987.
56. C. T. Chou, J. Aloimonos, and A. Rosenfield, “Correspon-denceless model based and active perception of shape form contour”, TR-1800, Center for Automation Research, University of Maryland, College Park, 1987.
57. J. Malik, “Interpreting line drawings of curved objects”, Intl. I. Computer Vision, vol. 1, pp. 73–103, 1987.
58. V. Nalwa, Ph.D. Thesis, Department of Computer Science, Stanford University, 1987.
59. J. Koenderick, “The internal representation of solid shape based on the topological properties of the apparent contour”, in Image Understanding 1986, W. Richards and S. Ullman, Eds. Norwood, NJ: Ablex Publishing Co., 1986.
60. B. D. Lucas, Ph.D. Thesis, Dept. of Computer Science, Carnegie-Mellon University, Pittsburgh, PA, 1985.
61. A. Witkin, D. Terzopoulos, and M. Kass, “Signal matching through scale space”, Int.’. Computer Vision, vol. 1, pp. 133–144, 1987.
62. S. T. Barnard, “A stochastic approach to stereo vision”, AAAI, pp. 676-680, 1986.
63. Y. Ohta and T. Kanade, “Stereo by intra- and interscanline search using dynamic programming”, CMU-CS-83-162, Dept. of Computer Science, Carnegie-Mellon, Univ., Pittsburgh, PA, Oct. 1983.
64. M. Kass, “Linear image features in stereopsis”, AAAI, pp. 707-713, 1986.
65. D. Terzopoulos, “Multilevel computational processes for visual surface reconstruction”, CVGIP, vol. 24, pp. 52–96, Oct. 1983.
66. D. Terzopoulos, “Multiresolution algorithms in computational vision”, in Image Understanding 1984, S. Ullman and W. Richards, Eds. Norwood, NJ: Ablex Publishing Corp., 1984, ch. 10.
67. R. C. Bolles, H. H. Baker, and D. H. Marimont, “Epipolar-plane image analysis: An approach to determining structure”, Int. J. Computer Vision, vol. 1, pp. 7–55, 1987.
68. D. Arnold, “Local context in matching edges for stereo vision”, in Proc. 1978 Image Understanding Workshop ‘Pittsburgh, PA, May 1978’, L. S. Baumann, Ed. McLean, VA: Science Applications, 1978.
69. H. H. Baker and T. O. Binford, “Depth from edge and intensity based stereo”, in Int. 1981 Jt. Conf. Artificial Intell. ‘Vancouver, B.C.’, vol. 6, pp. 631–636, Aug. 1981.
70. J. Mayhew and J. P. Frisby, “Psychophysical and computational studies toward a theory of human stereopsis”, Artif. Intell., vol. 17, pp. 349–387, 1981.
71. D. Marr and T. Poggio, “Cooperative computation of stereo disparity”, Science, vol. 194, pp. 283–287, 1976.
72. R. Nelson and J. Aloimonos, “Using flow field divergence for obstacle avoidance in visual navigation”, CAR-TR-322, Computer Vision Laboratory, Center for Automation Research, University of Maryland, College Park, MD, Sept. 1987.
73. R. Nelson and J. Aloimonos, “Finding motion parameters from spherical flow fields ‘Or the advantages of having eyes in the back of your head’”, Biological Cybernetics, vol. 58, pp. 261–273, 1988.
74. E. Ito and J. Aloimonos, “Is correspondence necessary for the perception of structure from motion?”, TR-1966, Center of Automation Research, University of Maryland, College Park, Jan. 1988.
75. A. N. Tichonov and V. Y. Arsenin, Solutions of Ill-Posed Problems. Washington: Winston, 1977.
76. V. A. Morozov, Regularization Methods for Solving Ill-Posed Problems. New York, NY: Springer-Verlag, 1984.
77. T. Poggio and C. Koch, “An analog model of computation for the ill-posed problems of early vision”, Memo 783, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, May 1984.
78. T. Poggio and V. Torre, “Ill-posed problems and regularization analysis in early vision”, Memo 773, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, Apr. 1984.
79. T. Poggio and the staff, “MIT progress in understanding images”, in Proc. Image Understanding Workshop, Los Angeles, CA, 1985.
80. W. E. L. Grimson and T. Pavlidis, “Discontinuity detection for visual surface reconstruction”, CVGIP, vol. 30, pp. 316–330, 1985.
81. D. Lee and T. Pavlidis, “Regularization in the presence of discontinuities”, in Proc. 1st ICCV, London, U.K., 1987.
82. S. Geman and B. Geman, “Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images”, IEEE Trans. Pattern Anal. Machine Intell., vol. 16, pp. 721–741, 1985.
83. J. Marroquin, Ph.D. Thesis, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 1986.
84. A. Blake and S. Zimmerman, Visual Reconstruction. Cambridge, MA: MIT Press, 1987.
85. E. Allgower and K. Georg, “Simplicial and continuation methods of approximating fixed points and solutions to systems of equations”, SIAM Rev., vol. 22, pp. 28–85, 1980.
86. D. Terzopoulos, “Regularization of inverse visual problems involving discontinuities”, IEEE Trans. Pattern Anal. Machine Intell., vol. 8, p. 413, 1986.
87. D. Shulman and J. Aloimonos, “A theory of boundary preserving regularization”, TR-2011, Computer Vision Laboratory, Center of Automation Research, University of Maryland, College Park, MD, Apr. 1988.
88. D. Shulman and J. Aloimonos, “‘Non’Rigid motion interpretation: A regularized approach”, TR-1860, Center for Automation Research, University of Maryland, College Park, June 1987.
89. J. Aloimonos and A. Basu, “Combining information in lowlevel vision”, TR-1947, Center for Automation Research, University of Maryland, College Park, Nov. 1987.
90. A. Waxman and J. Duncan, “Binocular image flows”, presented at the Workshop on Motion, Kiawah Island, SC, 1987.
91. W. Richards, “Structure from stereo and motion”, Al Memo 731, MIT, Cambridge, MA. Also in J. Opt. Soc. of America, vol. A2, pp. 343–349, 1986.
92. T. S. Huang and M. Blonstein, “Robust algorithms for computing three dimensional motion from image sequences”, presented at the IEEE Comp. Soc. Conf. on Computer Vision and Pattern Recognition, 1985.
93. R. Bajcsy, “Passive perception vs. active perception”, in Proc. IEEE Workshop on Computer Vision, Ann Arbor, Ml, 1986.
94. J. Aloimonos, I. Weiss, and A. Bandopadhyay, “Active vision”, Int. J. Computer Vision, vol. 2, no. 1, 1988.
95. I. Kuperman, Approximate Algebraic Linear Equations. London, England: Van Nostrand Reinhold Co., 1971.
96. R. Cole and C. K. Yap, “Shape from probing”, J. Algorithms, vol. 8, pp. 19–38, 1987.
97. I. Weiss, “Projective invariance”, TR-1965, Center for Automation Research, University of Maryland, College Park, Jan. 1988.
98. B. K. P. Horn and E. J. Weldon, “Computationally efficient methods of recovering translational motion”, in Proc. 1st ICCV, ‘London, England’, pp. 2-11, 1987.
99. G. Adiv, “Inherent ambiguities in recovering 3-D motion and structure from a noisy flow field”, in Proc. 1985 IEEE Comp. Soc. Conf. on Computer Vision and Pattern Recognition, pp. 70-77, 1985.
100. A. Neumaier, “New techniques for the analysis of linear interval equations”, Linear Algebra and Its Applications, vol. 58, pp. 273–325, 1984.
101. D. Gay, “Perturbation bounds for nonlinear equations”, SIAM J. Numer. Anal. vol. 18, Aug. 1981.
102. D. Gay, “Solving interval linear equations”, SIAM J. Numer. Anal., vol. 19, Aug. 1982.
103. R. Moore and T. Kioustelidis, “A simple test for accuracy of approximate solutions to ‘non’linear systems”, SIAM J. Numer. Anal., vol. 17, 1980.
104. J. Demmel, “The probability that a numerical analysis problem is unstable”, Tech. Rep., Courant Institute of Mathematical Sciences, 1984.
105. E. Gamble and T. Poggio, “Visual integration and detection of discontinuities: The key role of intensity edges”, Al Memo no. 970, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 1987.
106. T. Poggio and the staff, “MIT progress in understanding images”, in Proc. Image Understanding Workshop, Boston, MA, 1988.
107. C. O'Dunlaing, “Motion planning with inertial constraints”, Algorithmica, vol. 2, pp. 431–475, 1987.
108. M. Erdman and T. Lozano-Perez, “On multiple moving objects”, Algorithmica, vol. 2, pp. 477–521, 1987.
109. M. Intriligator and D. Brito, “Formal models of arms races”, J. Peace Science, pp. 77-96, Spring 1976.
110. S. Brams, M. Davis, and P. Straffin, Jr., “The geometry of arms races”, International Studies Quarterly, vol. 23, pp. 567–588, 1979.