A Proto-object Based Visual Attention Model

  • Francesco Orabona
  • Giorgio Metta
  • Giulio Sandini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4840)


One of the first steps of any visual system is that of locating suitable interest points, ‘salient regions’, in the scene, to detect events, and eventually to direct gaze toward these locations. In the last few years, object-based visual attention models have received an increasing interest in computational neuroscience and in computer vision, the problem, in this case, being that of creating a model of ‘objecthood’ that eventually guides a saliency mechanism. We present here an model of visual attention based on the definition of ‘proto-objects’ and show its instantiation on a humanoid robot. Moreover we propose a biological plausible way to learn certain Gestalt rules that can lead to proto-objects.


Visual Attention Humanoid Robot Central Pixel Perceptual Grouping Visual Attention Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Cave, K., Bichot, N.: Visuospatial attention: beyond a spotlight model. Psychonomic Bulletin & Review 6, 204–223 (1999)CrossRefGoogle Scholar
  2. 2.
    Kawato, M.: Internal models for motor control and trajectory planning. Current Opinion in Neurobiology 9, 718–727 (1999)CrossRefGoogle Scholar
  3. 3.
    O’Regan, J.: Solving the “real” mysteries of visual perception: the world as an outside memory. Canadian Journal of Psychology 46, 461–488 (1992)CrossRefGoogle Scholar
  4. 4.
    Maturana, R., Varela, F.: Autopoiesis and Cognition: The Realization of the Living. D.Reidel Publishing Co., Dordecht (1980)CrossRefGoogle Scholar
  5. 5.
    van Gelder, T., Port, R.: It’s about time: An overview of the dynamical approach to cognition. In: van Gelder, T., Port, R. (eds.) Mind as motion - Explorations in the Dynamics of Cognition, MIT Press, Cambridge, MA (1995)Google Scholar
  6. 6.
    Craighero, L., Fadiga, L., Rizzolatti, G., Umilta’, C.: Action for perception: a motor-visual attentional effect. J. Exp. Psychol. Hum. Percept. Perform. 25, 1673–1692 (1999)CrossRefGoogle Scholar
  7. 7.
    Fadiga, L., Fogassi, L., Gallese, V., Rizzolatti, G.: Visuomotor neurons: ambiguity of the discharge or ’motor’ perception? Int. J. Psychophysiol. 35, 165–177 (2000)CrossRefGoogle Scholar
  8. 8.
    Fischer, M.H., Hoellen, N.: Space- and object-based attention depend on motor intention. The Journal of General Psychology 131, 365–378 (2004)Google Scholar
  9. 9.
    Scholl, B.J.: Objects and attention: the state of the art. Cognition 80, 1–46 (2001)CrossRefGoogle Scholar
  10. 10.
    Rensink, R.A., O’Regan, J.K., Clark, J.J.: To see or not to see: The need for attention to perceive changes in scenes. Psychological Science 8(5), 368–373 (1997)CrossRefGoogle Scholar
  11. 11.
    Rensink, R.A.: Seeing, sensing, and scrutinizing. Vision Research 40(10–12), 1469–1487 (2000)CrossRefGoogle Scholar
  12. 12.
    Palmer, S., Rock, I.: Rethinking perceptual organization: the role of uniform connectedness. Psychonomic Bulletin & Review 1(1), 29–55 (1994)CrossRefGoogle Scholar
  13. 13.
    Treisman, A.M., Gelade, G.: A feature-integration theory of attention. Cognitive Psychology 12(1), 97–136 (1980)CrossRefGoogle Scholar
  14. 14.
    Milanese, R., Gil, S., Pun, T.: Attentive mechanisms for dynamic and static scene analysis. Optical Engineering 34, 2428–2434 (1995)CrossRefGoogle Scholar
  15. 15.
    Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20, 1254–1259 (1998)CrossRefGoogle Scholar
  16. 16.
    Itti, L., Koch, C.: Computational modeling of visual attention. Nature Reviews Neuroscience 2(3), 194–203 (2001)CrossRefGoogle Scholar
  17. 17.
    Sun, Y., Fisher, R.: Object-based visual attention for computer vision. Artificial Intelligence 146, 77–123 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Pylyshyn, Z.W.: Visual indexes, preconceptual objects, and situated vision. Cognition 80(1-2), 127–158 (2001)CrossRefGoogle Scholar
  19. 19.
    Metta, G., Fitzpatrick, P.: Early integration of vision and manipulation. Adaptive Behavior 11, 109–128 (2003)CrossRefGoogle Scholar
  20. 20.
    Orabona, F.: Learning and Adptation in Computer Vision. PhD thesis, University of Genoa (2007)Google Scholar
  21. 21.
    Sandini, G., Tagliasco, V.: An anthropomorphic retina-like structure for scene analysis. Computer Vision, Graphics and Image Processing 14, 365–372 (1980)CrossRefGoogle Scholar
  22. 22.
    Wolfe, J.M., Gancarz, G.: Guided search 3.0. In: Lakshminarayanan, V. (ed.) Basic and Clinical Applications of Vision Science, pp. 189–192. Kluwer Academic, Dordrecht, Netherlands (1996)Google Scholar
  23. 23.
    Smirnakis, S.M., Berry, M.J., Warland, D.K., Bialek, W., Meister, M.: Adaptation of retinal processing to image contrast and spatial scale. Nature 386, 69–73 (1997)CrossRefGoogle Scholar
  24. 24.
    Billock, V.A.: Cortical simple cells can extract achromatic information from the multiplexed chromatic and achromatic signals in the parvocellular pathway. Vision Research 35, 2359–2369 (1995)CrossRefGoogle Scholar
  25. 25.
    Mallot, H.A., von Seelen, W., Giannakopoulos, F.: Neural mapping and space-variant image processing. Neural Networks 3(3), 245–263 (1990)CrossRefGoogle Scholar
  26. 26.
    Li, X., Yuan, T., Yu, N., Yuan, Y.: Adaptive color quantization based on perceptive edge protection. Pattern Recognition Letters 24, 3165–3176 (2003)CrossRefGoogle Scholar
  27. 27.
    Eckhorn, R., Bauer, R., Jordan, W., Brosch, M., Kruse, M., Munk, W., Reitboeck, H.J.: Coherent oscillations: A mechanism of feature linking in the visual cortex? Biological Cybernetics 60, 121–130 (1988)CrossRefGoogle Scholar
  28. 28.
    Gray, C.M., König, P., Engel, A.K., Singer, W.: Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature 338, 334–336 (1989)CrossRefGoogle Scholar
  29. 29.
    De Smet, P., Pires, R.L.V.: Implementation and analysis of an optimized rainfalling watershed algorithm. In: Proc. of SPIE, VCIP’2000, vol. 3974, pp. 759–766 (2000)Google Scholar
  30. 30.
    Wan, S., Higgins, W.: Symmetric region growing. IEEE Trans. on Image Processing 12(9), 1007–1015 (2003)CrossRefGoogle Scholar
  31. 31.
    Viola, P., Jones, M.J.: Robust real-time face detection. International Journal of Computer Vision 57(2), 137–154 (2004)CrossRefGoogle Scholar
  32. 32.
    Melcher, D., Kowler, E.: Shapes, surfaces and saccades. Vision Research 39, 2929–2946 (1999)CrossRefGoogle Scholar
  33. 33.
    Tipper, S.P.: Object-centred inhibition of return of visual attention. Quarterly Journal of Experimental Psychology 43A, 289–298 (1991)CrossRefGoogle Scholar
  34. 34.
    Itti, L., Koch, C.: Feature combination strategies for saliency-based visual attention systems. Journal of Electronic Imaging 10(1), 161–169 (2001)CrossRefGoogle Scholar
  35. 35.
    Natale, L., Orabona, F., Berton, F., Metta, G., Sandini, G.: From sensorimotor development to object perception. In: Proc. of the 5th IEEE-RAS International Conference on Humanoid Robots, Tsukuba, Japan, pp. 226–231 (2005)Google Scholar
  36. 36.
    Field, D.J., Hayes, A., Hess, R.F.: Contour integration by the human visual system: evidence for local ”association field”. Vision Research 33(2), 173–193 (1993)CrossRefGoogle Scholar
  37. 37.
    Schmidt, K., Goebel, R., Löwel, S., Singer, W.: The perceptual grouping criterion of collinearity is reflected by anisotropies of connections in the primary visual cortex. European Journal of Neuroscience 5(9), 1083–1084 (1997)CrossRefGoogle Scholar
  38. 38.
    Grossberg, S., Mingolla, E.: Neural dynamics of perceptual grouping: textures, boundaries, and emergent segmentations. Percept. Psychophys. 38, 141–171 (1985)CrossRefGoogle Scholar
  39. 39.
    Guy, G., Medioni, G.: Inferring global perceptual contours from local features. Int. J. of Computer Vision 20, 113–133 (1996)CrossRefGoogle Scholar
  40. 40.
    Li, Z.: A neural model of contour integration in the primary visual cortex. Neural Computation 10, 903–940 (1998)CrossRefGoogle Scholar
  41. 41.
    Sigman, M., Cecchi, G.A., Gilbert, C.D., Magnasco, M.O.: On a common circle: Natural scenes and gestalt rules. PNAS 98(4), 1935–1940 (2001)CrossRefGoogle Scholar
  42. 42.
    Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proc. of ICCV 2001, vol. 2, pp. 416–423 (2001)Google Scholar
  43. 43.
    Morrone, M., Burr, D.: Feature detection in human vision: A phase dependent energy model. Proc. Royal Soc. of London B 235, 221–245 (1988)CrossRefGoogle Scholar
  44. 44.
    Knutsson, H.: Representing local structure using tensors. In: Proceedings 6th Scandinavian Conference on Image Analysis, Oulu, Finland, pp. 244–251 (1989)Google Scholar
  45. 45.
    Prodöhl, C., Würtz, R.P., von der Malsburg, C.: Learning the gestalt rule of collinearity from object motion. Neural Computation 15, 1865–1896 (2003)CrossRefzbMATHGoogle Scholar
  46. 46.
    Coppola, D.M., Purves, H.R., McCoy, A.N., Purves, D.: The distribution of oriented contours in the real world. PNAS 95, 4002–4006 (1998)CrossRefGoogle Scholar
  47. 47.
    Fitzpatrick, P., Metta, G.: Grounding vision through experimental manipulation. Philos. trans. - Royal Soc., Math. phys. eng. sci. 361(1811), 2185–2615 (2003)MathSciNetGoogle Scholar
  48. 48.
    Martin, D., Fowlkes, C., Malik, J.: Learning to detect natural image boundaries using local brightness, color and texture cues. IEEE Trans. Pattern Anal. Mach. Intell. 26(5), 530–549 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Francesco Orabona
    • 1
  • Giorgio Metta
    • 1
    • 2
  • Giulio Sandini
    • 2
  1. 1.DIST, University of Genoa, Viale Causa, 13 - Genoa 16145Italy
  2. 2.Italian Institute of Technology, Via Morego, 30 - Genoa 16163Italy

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