OSA's Digital Library

Journal of the Optical Society of America A

Journal of the Optical Society of America A

| OPTICS, IMAGE SCIENCE, AND VISION

  • Vol. 20, Iss. 7 — Jul. 1, 2003
  • pp: 1304–1320

What makes viewpoint-invariant properties perceptually salient?

David W. Jacobs  »View Author Affiliations


JOSA A, Vol. 20, Issue 7, pp. 1304-1320 (2003)
http://dx.doi.org/10.1364/JOSAA.20.001304


View Full Text Article

Acrobat PDF (284 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

It has been noted that many of the perceptually salient image properties identified by the Gestalt psychologists, such as collinearity, parallelism, and good continuation, are invariant to changes in viewpoint. However, I show that viewpoint invariance is not sufficient to distinguish these Gestalt properties; one can define an infinite number of viewpoint-invariant properties that are not perceptually salient. I then show that generally, the perceptually salient viewpoint-invariant properties are minimal, in the sense that they can be derived by using less image information than for nonsalient properties. This finding provides support for the hypothesis that the biological relevance of an image property is determined both by the extent to which it provides information about the world and by the ease with which this property can be computed. [An abbreviated version of this work, including technical details that are avoided in this paper, is contained in K. Boyer and S. Sarker, eds., <i>Perceptual Organization for Artificial Vision Systems</i> (Kluwer Academic, Dordrecht, The Netherlands, 2000), pp. 121–138.]

© 2003 Optical Society of America

OCIS Codes
(260.1960) Physical optics : Diffraction theory

Citation
David W. Jacobs, "What makes viewpoint-invariant properties perceptually salient?," J. Opt. Soc. Am. A 20, 1304-1320 (2003)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-20-7-1304


Sort:  Author  |  Year  |  Journal  |  Reset

References

  1. D. Jacobs, “What makes viewpoint-invariant properties perceptually salient? A computational perspective,” in Perceptual Organization for Artificial Vision Systems, K. Boyer and S. Sarkar, eds. (Kluwer Academic, Dordrecht, The Netherlands, 2000), pp. 121–138.
  2. K. Koffka, Principles of Gestalt Psychology (Harcourt, Brace & World, New York, 1963).
  3. F. Attneave, “Some informational aspects of visual perception,” Psychol. Rev. 68, 183–193 (1954).
  4. W. Garner, Uncertainty and Structure As Psychological Concepts (Wiley, New York, 1962).
  5. E. Leeuwenberg, “A perceptual coding language for visual and auditory patterns,” Am. J. Psychol. 84, 307–349 (1971).
  6. A. Witkin and J. Tenenbaum, “On the role of structure in vision,” in Human and Machine Vision, J. Beck, B. Hope, and A. Rosenfeld, eds. (Academic, New York, 1983), pp. 481–543.
  7. D. Lowe, Perceptual Organization and Visual Recognition (Kluwer Academic, Dordrecht, The Netherlands, 1985).
  8. I. Rock, The Logic of Perception (MIT Press, Cambridge, Mass., 1983).
  9. J. Pomerantz and M. Kubovy, “Theoretical approaches to perceptual organization,” in Handbook of Perception and Human Performance: Vol. II. Cognitive Processes and Performance, K. Boff, L. Kaufmann, and J. Thomas, eds. (Wiley, New York, 1986), pp. (36.1–36.46).
  10. T. Binford, “Inferring surfaces from images,” Artif. Intell. 17, 205–244 (1981).
  11. T. Kanade, “Recovery of the three-dimensional shape of an object from a single view,” Artif. Intell. 17, 409–460 (1981).
  12. J. Cutting, “Observations: four assumptions about invariance in perception,” J. Exp. Psychol. Hum. Percept. Perform. 9, 310–317 (1983).
  13. L. Van Gool, T. Moons, E. Pauwels, and J. Wagemans, “Invariance from the Euclidean geometer’s persepective,” Perception 23, 547–561 (1994).
  14. I. Biederman, “Recognition-by-components: a theory of human image understanding,” Psychol. Rev. 94, 115–147 (1987).
  15. P. Jolicoeur and S. Kosslyn, “Coordinate systems in the long-term memory representation of three-dimensional shapes,” Cogn. Psychol. 15, 301–345 (1983).
  16. I. Rock and J. DiVita, “A case of viewer-centered object perception,” Cogn. Psychol. 19, 280–293 (1987).
  17. M. Corballis, “Recognition of disoriented shapes,” Psychol. Rev. 95, 115–123 (1988).
  18. I. Biederman and P. Gerhardstein, “Recognizing depth-rotated objects: evidence and conditions for three-dimensional viewpoint invariance,” J. Exp. Psychol. Hum. Percept. Perform. 19, 1162–1182 (1993).
  19. M. Tarr and H. Bülthoff, “Is human object recognition better described by geonstructural-descriptions or by multiple-views? Comment on Biederman and Gerhardstein 1993,” J. Exp. Psychol. Hum. Percept. Perform. 21, 1494–1505 (1995).
  20. I. Biederman and P. Gerhardstein, “Viewpoint-dependent mechanisms in visual object recognition: reply to Tarr and Bülthoff,” J. Exp. Psychol. Hum. Percept. Perform. 21, 1506–1514 (1995).
  21. M. Kurbat, “Structural description theories: Is RBC/JIM a general-purpose theory of human entry-level object recognition?” Perception 23, 1339–1368 (1994).
  22. W. Hayward and M. Tarr, “Testing conditions for viewpoint invariance in object recognition,” J. Exp. Psychol. Hum. Percept. Perform. 23, 1511–1521 (1997).
  23. I. Biederman and M. Bar, “One-shot viewpoint invariance in matching novel objects,” Vision Res. 39, 2885–2899 (1999).
  24. S. Palmer, “The psychology of perceptual organization: a transformational approach,” in Human and Machine Vision, J. Beck, B. Hope, and A. Rosenfeld, eds. (Academic, New York, 1983), pp. 269–339.
  25. R. Vogels, I. Biederman, M. Bar, and A. Lorincz, “Inferior temporal neurons show greater sensitivity to nonaccidental than metric differences,” J. Cogn Neurosci. 13, 444–453 (2001).
  26. G. Kayeart, I. Biederman, and R. Vogels, “Shape tuning in macaque inferior temporal cortex,” J. Neurosci. 23, 3016–3027 (2003).
  27. A. Jepson, W. Richards, and D. Knill, “Modal structure and reliable inference,” in Perception as Bayesian Inference, D. Knill and W. Richards, eds. (Cambridge U. Press, Cambridge, UK, 1996), pp. 63–92.
  28. D. Jacobs, “Matching 3-D models to 2-D images,” Int. J. Comput. Vis. 21 (1/2), 123–153 (1997).
  29. J. Wagemans, L. Van Gool, and G. d’Ydewalle, “Detection of symmetry in tachistoscopically presented dot patterns: effects of multiple axes and skewing.” Percept. Psychophys. 50, 413–427 (1991).
  30. J. Wagemans, L. Van Gool, V. Swinnen, and J. Van Horebeek, “Higher-order structure in regularity detection,” Vision Res. 33, 1067–1088 (1993).
  31. I. Weiss, “Geometric invariants and object recognition,” Int. J. Comput. Vision 10, 207–231 (1993).
  32. J. Burns, R. Weiss, and E. Riseman, “The non-existence of general-case view invariants,” in Geometric Invariance in Computer Vision, J. Mundy and A. Zisserman, eds. (MIT Press, Cambridge, Mass., 1992), pp. 120–131.
  33. D. Clemens and D. Jacobs, “Space and time bounds on model indexing,” IEEE Trans. Pattern Anal. Mach. Intell. 13, 1007–1018 (1991).
  34. Y. Moses and S. Ullman, “Limitations of non model-based recognition schemes,” in Proceedings of the Second European Conference on Computer Vision (Springer-Verlag, Heidelberg, Germany, 1992), pp. 820–828.
  35. D. Forsyth, J. Mundy, A. Zisserman, and C. Rothwell, “Recognising rotationally symmetric surfaces from their outlines,” in Proceedings of Second European Conference on Computer Vision (Springer-Verlag, Heidelberg, Germany, 1992), pp. 639–647.
  36. R. Basri and Y. Moses, “When is it possible to identify 3D objects from single images using class constraints?” Int. J. Comput. Vision 33, 1–22 (1999).
  37. P. Van der Helm and E. Leeuwenberg, “Goodness of visual regularities: a non-transformational approach,” Psychol. Rev. 103, 429–456 (1996).
  38. D. Field, A. Hayes, and R. Hess, “Contour integration by the human visual system: evidence for a local ‘association field’,” Vision Res. 33, 173–193 (1993).
  39. A. Tuller, A Modern Introduction to Geometries (D. Van Nostrand, Princeton, N.J., 1967).
  40. D. Jacobs, “The space requirements of indexing under perspective projection,” IEEE Trans. Pattern Anal. Mach. Intell. 18, 330–333 (1996b).
  41. A. Jepson and W. Richards, “What makes a good feature?” Memo No. 1356 (MIT Artificial Intelligence Laboratory, Cambridge, Mass., 1992).
  42. J. Feldman, “Curvilinearity, covariance, and regularity in perceptual groups,” Vision Res. 37, 2835–2848 (1997).
  43. D. Hoffman and W. Richards, “Parts of recognition,” in Visual Cognition, S. Pinker, ed. (MIT Press, Cambridge, Mass., 1984), pp. 65–96.
  44. D. Cyganski, J. Orr, T. Cott, and R. Dodson, “Development, implementation, testing, and application of an affine transform invariant curvature function,” in Proceedings of the First International Conference on Computer Vision (IEEE Computer Society Press, Los Alamitos Calif., 1987), pp. 496–500.
  45. E. Abravanel, “The figural simplicity of parallel lines,” Child Dev. 48, 708–710 (1977).
  46. R. Shepard, “Psychophysical complementarity,” in Perceptual Organization, M. Kobovy and J. Pomerantz, eds. (Erlbaum, Hillsdale, N.J., 1981), pp. 279–341.
  47. F. Tsai, “Robust affine invariant matching with applications to line features,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE Computer Society Press, Los Alamitos, Calif., 1993), pp. 393–399.
  48. J. Koenderink, Solid Shape (MIT Press, Cambridge, Mass., 1990).
  49. D. Jacobs, P. Belhumeur, and I. Jermyn, “Judging whether multiple silhouettes can come from the same object,” in Proceedings of the International Workshop on Visual Form (Springer-Verlag, Heidelberg, Germany, 2001), pp. 532–541.
  50. J. Koenderink and A. van Doorn, “The shape of smooth objects and the way contours end,” Perception 11, 129–137 (1982).
  51. J. Ponce, D. Chelberg, and W. Mann, “Invariant properties of straight homogeneous generalized cylinders and their contours,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 951–966 (1989).
  52. M. Zerroug and R. Nevatia, “Three-dimensional descriptions based on the analysis of the invariant and quasi-invariant properties of some curved-axis generalized cylinders,” IEEE Trans. Pattern Anal. Mach. Intell. 18, 237–966 (1996).
  53. J. Koenderink, “What does the occluding contour tell us about solid shape?” Perception 13, 321–330 (1984).
  54. D. Jacobs, P. Belhumeur, and R. Basri, “Comparing images under variable illumination,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE Computer Society Press, Los Alamitos, Calif., 1998), pp. 610–617.
  55. N. Chater, “Reconciling simplicity and likelihood principles in perceptual organization,” Psychol. Rev. 103, 566–581 (1996).
  56. M. Leyton, “A theory of information structure II. A theory of perceptual organization,” J. Math. Psychol. 30, 257–305 (1986).
  57. S. Ullman, The Interpretation of Visual Motion (MIT Press, Cambridge, Mass., 1979).
  58. S. Grossberg and E. Mingolla, “Neural dynamics of form perception: boundary completion, illusory figures, and neon color spreading,” Psychol. Rev. 92, 173–211 (1985).
  59. R. Heitger and R. Von der Heydt, “A computational model of neural contour processing, figure-ground segregation and illusory contours,” in Proceedings of the International Conference on Computer Vision (IEEE Computer Society Press, Los Alamitos, Calif., 1993), pp. 32–40.
  60. G. Guy and G. Medioni, “Inferring global perceptual contours from local features,” Int. J. Comput. Vision 20 (1/2), 113–133 (1996).
  61. L. Williams and D. Jacobs, “Stochastic completion fields: a neural model of illusory contour shape and salience,” Neural Comput. 9, 837–858 (1997).
  62. P. Parent and S. Zucker, “Trace inference, curvature consistency and curve detection,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 823–839 (1989).
  63. A. Sha’ashua and S. Ullman, “Structural saliency: the detection of globally salient structures using a locally connected network,” in Proceedings of the International Conference on Computer Vision (IEEE Computer Society Press, Los Alamitos, Calif., 1988), pp. 321–327.
  64. L. Williams and D. Jacobs, “Local parallel computation of stochastic completion fields,” Neural Comput. 9, 859–881 (1997).
  65. G. Krieger and C. Zetzsche, “Nonlinear image operators for the evaluation of local instrinsic dimensionality,” IEEE Trans. Image Process. 5, 1026–1042 (1996).
  66. S. Geman and D. Geman, “Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images,” IEEE Trans. Pattern Anal. Mach. Intell. 6, 721–741 (1984).
  67. D. Mumford, “Elastica and computer vision,” in Algebraic Geometry and Its Applications, C. Bajaj, ed. (Springer-Verlag, Heidelberg, Germany, 1994), pp. 491–506.
  68. K. Thornber and L. Williams, “Characterizing the distribution of completion shapes with corners using a mixture of random processes,” in Proceedings of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (Springer-Verlag, Heidelberg, Germany, 1997), pp. 19–34.
  69. S. Zhu, “Embedding Gestalt laws in the Markov random fields,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 1170–1187 (1999).
  70. J. Wagemans, L. Van Gool, and G. d’Ydewalle, “Orientational effects and component processes in symmetry detection,” Q. J. Exp. Psychol. A 44, 475–508 (1992).
  71. J. Wagemans, “Toward a better approach to goodness: comment on Van der Helm and Leeuwenberg 1996,” Psychol. Rev. 106, 610–621 (1999).
  72. J. Elder and S. Zucker, “A measure of closure,” Vision Res. 34, 3361–3369 (1994).
  73. I. Kovacs and B. Julesz, “A closed curve is much more than an incomplete one: effect of closure in figure-ground segmentation,” Proc. Natl. Acad. Sci. USA 90, 7495–7497 (1993).
  74. T. Binford and T. Levitt, “Quasi-invariants: theory and exploitation,” in Proceedings of the DARPA Image Understanding Workshop (Defense Advanced Research Projects Agency, Arlington, Va., 1993), pp. 819–829.
  75. J. Hummel and I. Biederman, “Dynamic binding in a neural network for shape recognition,” Psychol. Rev. 99, 480–517 (1992).
  76. D. Williams and B. Julesz, “Peceptual asymmetry in texture detection,” Proc. Natl. Acad. Sci. USA 89, 6531–6534 (1992).
  77. T. Poggio and S. Edelman, “A network that learns to recognize 3D objects,” Nature 343, 263–266 (1990).
  78. S. Ullman, “Aligning pictorial descriptions: an approach to object recognition,” Cognition 32, 193–254 (1989).

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited