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Psychophysics, Gestalts and Games

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Neuromathematics of Vision

Part of the book series: Lecture Notes in Morphogenesis ((LECTMORPH))

Abstract

Many psychophysical studies are dedicated to the evaluation of the human gestalt detection on dot or Gabor patterns, and to model its dependence on the pattern and background parameters. Nevertheless, even for these constrained percepts, psychophysics have not yet reached the challenging prediction stage, where human detection would be quantitatively predicted by a (generic) model. On the other hand, Computer Vision has attempted at defining automatic detection thresholds. This chapter sketches a procedure to confront these two methodologies inspired in gestaltism.

Using a computational quantitative version of the non-accidentalness principle, we raise the possibility that the psychophysical and the (older) gestaltist setups, both applicable on dot or Gabor patterns, find a useful complement in a Turing test. In our perceptual Turing test, human performance is compared by the scientist to the detection result given by a computer. This confrontation permits to revive the abandoned method of gestaltic games. We sketch the elaboration of such a game, where the subjects of the experiment are confronted to an alignment detection algorithm, and are invited to draw examples that will fool it. We show that in that way a more precise definition of the alignment gestalt and of its computational formulation seems to emerge.

Detection algorithms might also be relevant to more classic psychophysical setups, where they can again play the role of a Turing test. To a visual experiment where subjects were invited to detect alignments in Gabor patterns, we associated a single function measuring the alignment detectability in the form of a number of false alarms (NFA). The first results indicate that the values of the NFA, as a function of all simulation parameters, are highly correlated to the human detection. This fact, that we intend to support by further experiments, might end up confirming that human alignment detection is the result of a single mechanism.

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References

  1. Ahuja, N., Tuceryan, M.: Extraction of early perceptual structure in dot patterns: integrating region, boundary, and component gestalt. Comput. Vision Graph. Image Process. 48(3), 304–356 (1989)

    Article  Google Scholar 

  2. Attneave, F.: Some informational aspects of visual perception. Psychological Review 61(3), 183–193 (1954)

    Article  Google Scholar 

  3. Demeyer, M., Machilsen, B.: The construction of perceptual grouping displays using GERT. Behavior Research Methods, 1–8 (2011), online first

    Google Scholar 

  4. Desolneux, A., Moisan, L., Morel, J.: Meaningful alignments. International Journal of Computer Vision 40(1), 7–23 (2000)

    Article  MATH  Google Scholar 

  5. Desolneux, A., Moisan, L., Morel, J.: Computational gestalts and perception thresholds. Journal of Physiology – Paris 97, 311–324 (2003)

    Article  Google Scholar 

  6. Desolneux, A., Moisan, L., Morel, J.: A grouping principle and four applications. IEEE Transactions on Pattern Analysis and Machine Intelligence (2003)

    Google Scholar 

  7. Desolneux, A., Moisan, L., Morel, J.: From Gestalt Theory to Image Analysis, a Probabilistic Approach. Interdisciplinary Applied Mathematics, vol. 34. Springer (2008)

    Google Scholar 

  8. Ellis, W. (ed.): A Source Book of Gestalt Psychology. Humanities Press (1967 (originally 1938))

    Google Scholar 

  9. Feldman, J.: Regularity-based perceptual grouping. Computational Intelligence 13(4), 582–623 (1997)

    Article  MathSciNet  Google Scholar 

  10. Feldman, J.: Bayesian contour integration. Attention, Perception, & Psychophysics 63, 1171–1182 (2001)

    Article  Google Scholar 

  11. Feldman, J., Singh, M.: Information along contours and object boundaries. Psychological Review 112(1), 243–252 (2005)

    Article  Google Scholar 

  12. Field, D.J., Hayes, A., Hess, R.F.: Contour integration by the human visual system: Evidence for a local association field. Vision Research 33(2), 173–193 (1993)

    Article  Google Scholar 

  13. Fleuret, F., Li, T., Dubout, C., Wampler, E.K., Yantis, S., Geman, D.: Comparing machines and humans on a visual categorization test. Proceedings of the National Academy of Sciences 108(43), 17,621–17,625 (2011)

    Google Scholar 

  14. von Gioi, R.G., Jakubowicz, J.: On computational Gestalt detection thresholds. Journal of Physiology – Paris 103(1-2), 4–17 (2009)

    Article  Google Scholar 

  15. Grossberg, S., Mingolla, E.: Neural dynamics of perceptual grouping: Textures, boundaries, and emergent segmentations. Attention, Perception, & Psychophysics 38(2), 141–171 (1985)

    Article  Google Scholar 

  16. Han, F., Zhu, S.C.: Bottom-up/top-down image parsing with attribute grammar. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(1), 59–73 (2009)

    Article  MathSciNet  Google Scholar 

  17. Kanizsa, G.: Organization in vision: Essays on Gestalt perception. Praeger, New York (1979)

    Google Scholar 

  18. Kanizsa, G.: Grammatica del vedere. Il Mulino (1980)

    Google Scholar 

  19. Kanizsa, G.: Vedere e pensare. Il Mulino (1991)

    Google Scholar 

  20. Kersten, D., Mamassian, P., Yuille, A.: Object perception as bayesian inference. Annual Review of Psychology 55(1), 271–304 (2004)

    Article  Google Scholar 

  21. Köhler, W.: Gestalt Psychology. Liveright (1947)

    Google Scholar 

  22. Leclerc, Y.: Constructing simple stable descriptions for image partitioning. International Journal of Computer Vision 3(1), 73–102 (1989)

    Article  Google Scholar 

  23. Lowe, D.: Perceptual Organization and Visual Recognition. Kluwer Academic Publishers (1985)

    Google Scholar 

  24. Marr, D.: Vision. Freeman and co. (1982)

    Google Scholar 

  25. Metzger, W.: Gesetze des Sehens, 3rd edn. Verlag Waldemar Kramer, Frankfurt am Main (1975)

    Google Scholar 

  26. Metzger, W.: Laws of Seeing. The MIT Press (2006 (originally 1936)), English translation of the first edition of [25]

    Google Scholar 

  27. Mumford, D.: Pattern theory: the mathematics of perception. In: Proceedings of the International Congress of Mathematicians, Beijing, vol. I, pp. 401–422 (2002)

    Google Scholar 

  28. Nygård, G., Van Looy, T.: Wagemans: The influence of orientation jitter and motion on contour saliency and object identification. Vision Research 49, 2475–2484 (2009)

    Article  Google Scholar 

  29. Pinar Saygin, A., Cicekli, I., Akman, V.: Turing test: 50 years later. Minds and Machines 10(4), 463–518 (2000)

    Article  Google Scholar 

  30. Preiss, K.: A theoretical and computational investigation into aspects of human visual perception: Proximity and transformations in pattern detection and discrimination. Ph.D. thesis, University of Adelaide (2006)

    Google Scholar 

  31. Sarkar, S., Boyer, K.L.: Perceptual organization in computer vision: A review and a proposal for a classificatory structure. IEEE Transactions on Systems, Man, and Cybernetics 23(2), 382–399 (1993)

    Article  Google Scholar 

  32. Spelke, E.: Principles of object perception. Cognitive Science 14(1), 29–56 (1990)

    Article  Google Scholar 

  33. Stevens, S.: Psychophysics. Transaction Publishers (1986)

    Google Scholar 

  34. Tripathy, S.P., Mussap, A.J., Barlow, H.B.: Detecting collinear dots in noise. Vision Research 39(25), 4161–4171 (1999)

    Article  Google Scholar 

  35. Turing, A.: Computing machinery and intelligence. Mind 59, 433–460 (1950)

    Article  MathSciNet  Google Scholar 

  36. Uttal, W., Bunnell, L., Corwin, S.: On the detectability of straight lines in visual noise: An extension of frenchs paradigm into the millisecond domain. Perception and Psychophysics 8, 385–388 (1970)

    Article  Google Scholar 

  37. Uttal, W.R.: The effect of deviations from linearity on the detection of dotted line patterns. Vision Res. 13(11), 2155–2163 (1973)

    Article  Google Scholar 

  38. Vanegas, M.C., Bloch, I., Inglada, J.: Detection of aligned objects for high resolution image understanding. In: IGARSS, pp. 464–467 (2010)

    Google Scholar 

  39. Wagemans, J.: Perceptual use of nonaccidental properties. Canadian Journal of Psychology 46(2), 236–279 (1992)

    Article  Google Scholar 

  40. Wagemans, J., Elder, J.H., Kubovy, M., Palmer, S.E., Peterson, M.A., Singh, M., von der Heydt, R.: A century of gestalt psychology in visual perception: I. perceptual grouping and figureground organization. Psychological Bulletin (2012)

    Google Scholar 

  41. Wagemans, J., Feldman, J., Gepshtein, S., Kimchi, R., Pomerantz, J.R., van der Helm, P.A., van Leeuwen, C.: A century of gestalt psychology in visual perception: II. conceptual and theoretical foundations. Psychological Bulletin (2012)

    Google Scholar 

  42. Wertheimer, M.: Untersuchungen zur Lehre von der Gestalt. II. Psychologische Forschung 4(1), 301–350 (1923), An abridged translation to English is included in [8]

    Google Scholar 

  43. Witkin, A.P., Tenenbaum, J.M.: On the role of structure in vision. In: Beck, J., Hope, B., Rosenfeld, A. (eds.) Human and Machine Vision, pp. 481–543. Academic Press (1983)

    Google Scholar 

  44. Witkin, A.P., Tenenbaum, J.M.: What is perceptual organization for? IJCAI-83 2, 1023–1026 (1983)

    MATH  Google Scholar 

  45. Zhu, S., Yuille, A.: Region competition: Unifying snakes, region growing, and bayes/mdl for multiband image segmentation. Pattern Analysis and Machine Intelligence 18(9), 884–900 (1996)

    Article  Google Scholar 

  46. Zhu, S.C., Mumford, D.: A stochastic grammar of images. Foundations and Trends in Computer Graphics and Vision 2(4), 259–362 (2006)

    Article  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. École Normale Supérieure de Cachan, Cachan, France

    José Lezama & Jean-Michel Morel

  2. Centre de Mathématiques et de Leurs Applications - ENS de Cachan Paris, Paris, France

    Samy Blusseau

  3. Instituto de Ingegneria Eléctrica, Universidad de la República, Montevideo, Uruguay

    Gregory Randall & Rafael Grompone von Gioi

Authors
  1. José Lezama
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  2. Samy Blusseau
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  3. Jean-Michel Morel
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  4. Gregory Randall
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  5. Rafael Grompone von Gioi
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Correspondence to José Lezama .

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Editors and Affiliations

  1. Dipartimento di Matematica, Università di Bologna, Bologna, Italy

    Giovanna Citti

  2. CAMS Center for Mathematics, CNRS-EHESS, Paris, France

    Alessandro Sarti

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Lezama, J., Blusseau, S., Morel, JM., Randall, G., von Gioi, R.G. (2014). Psychophysics, Gestalts and Games. In: Citti, G., Sarti, A. (eds) Neuromathematics of Vision. Lecture Notes in Morphogenesis. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34444-2_6

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