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Human and Machine Perception pp 271–288Cite as

Panel Summary: Knowledge Model Representations

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Abstract

Following the usual classifications of cognitive psychologists, we can say that the problem of representation spans three domains: the environment, the brain, and cognitive processes, which are usually studied by different scientists: the physicists, the neurobiologists and the psychologists. With the development of computer science and artificial intelligence new approaches have been introduced, which make possible simulation and implementation of cognitive processes through neural networks and symbolic systems. But the contribution of new methods is not limited to simulation, because they try to provide new models which consider cognitive process as information processing, not as reactions to stimuli.

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References

  1. J.J. Gibson, The Sense considered as Perceptual Systems, Houghton Mifflin, Boston (1966).

    Google Scholar 

  2. J.J. Gibson, The Ecological Approach to Visual Perception, Lawrence Erlbaum Ass., Hillsdale, New Jersay(1979).

    Google Scholar 

  3. U. Neisser, Cognitive Psychology, Appleton-Century-Crofts, New York (1967).

    Google Scholar 

  4. U. Neisser, Cognition and Reality: Principles and Implications of Cognitive Psychology, W. H. Freeman, New York (1976).

    Google Scholar 

  5. J.A. Fodor, The Modularity of Mind. An Essay on Faculty Psychology, MIT Press, Cambridge, MA (1983).

    Google Scholar 

  6. D. Marr, Vision: A Computational Investigation into the Human Representation and Processing of Visual Information, W.H. Freeman, New York (1982).

    Google Scholar 

  7. P.H. Lindsay and D.A. Norman, Human Information Processing, Academic Press, New York (1977).

    Google Scholar 

  8. I. Biederman, Recognition-by-components: A theory of human image understanding, Psychological Review, 94, 115:147(1987).

    Google Scholar 

  9. I. Biederman, T.W. Blicke, G. Ju, J.H. Hilton, and J.E. Hummel, Empirical analyses and connectionist modeling of real-time human image understanding, in: Program of the Tenth Annual Conference of the Cognitive Science Society, 251:256, Lawrence Erlbaum, Hillsdale, N.J. (1988).

    Google Scholar 

  10. A. Treisman and S. Gormican, Feature analysis in early vision: Evidence from search asymmetries, Psychological Review, 95, 15:48 (1988).

    Google Scholar 

  11. J. Piaget, La Construction du Réel chez l’enfant, Délacheaux et Niestlé, Neuchatel (1937).

    Google Scholar 

  12. A. Paivio, Imagery and Verbal Processes, Holt, Rinehart and Winston, New York (1971).

    Google Scholar 

  13. J.A. Barnden and J.B. Pollack, Advances in Connectionist and Neural Computation Theory. Vol. 1: High Level Connectionist Models, Ablex Publ. Corporation, Norwood, N.J. (1991).

    Google Scholar 

  14. M.H. Van Klerck and S.M. Kosslyn, Visual information processing: a perspective, in: Attention and Performance, D.E. Meyer and S. Kornblum eds., MIT Press, Cambridge, MA, vol. XIV, cap. 2, 37:58,(1992).

    Google Scholar 

  15. H. Wechsler, Computational Vision, Academic Press (1990).

    Google Scholar 

  16. A. Chella, M. Frixione, and S. Gaglio, A Cognitive Architecture for Artificial Vision, CS&AI Lab Tech. Rep, University of Palermo (1994).

    Google Scholar 

  17. D. Marr and K. Nishihara, Representation and recognition of the spatial organization of three-dimensional shapes, Proc. Roy. Soc. London B, 200, 269:294 (1978).

    Google Scholar 

  18. P. Gärdenfors, Three Levels of Inductive Interence, Lund University Cognitive Studies 9, Tech. Rep. LUHFDA/HFKO-5006-SE (1992).

    Google Scholar 

  19. A. A. G. Requicha, Representations for rigid solids: theory, methods and systems, ACM Comp. Surveys, 12, 4, 437:464(1980).

    Google Scholar 

  20. A. P. Pentland, Perceptual Organization and the Representation of Natural Form, Artificial Intelligence, 28,293:331 (1986).

    MathSciNet  Google Scholar 

  21. A. H. Barr, Superquadrics and Angle-Preserving Transformations, IEEE Computer Graphics and Applications, 1, 11:23,(1981).

    Google Scholar 

  22. E. Ardizzone, S. Gaglio, and F. Sorbello, Geometric and Conceptual Knowledge Representation within a Generative Model of Visual Perception, Journal of Intelligent and Robotic Systems, 2, 381:409 (1989).

    Google Scholar 

  23. F. Solina and R. Bajcsy, Recovery of Parametric Models from Range Images: The Case of Superquadrics with Global Deformations, IEEE Trans. on PAMI, 12, 131:147 (1990).

    Google Scholar 

  24. B. K. P. Horn, Robot Vision, The MIT Press (1986).

    Google Scholar 

  25. R. M. Bolle and B. C. Vemuri, On three-dimensional surface reconstruction methods, IEEE Trans. Patt. Anal. Mack Intell., vol.13, pp. 1:13 (1991).

    Google Scholar 

  26. I. Biederman, Human Image Understanding: Recent Research and a Theory, Computer Vision, Graphics and Image Processing, 32, 29:73 (1985).

    Google Scholar 

  27. B. Nebel, Reasoning and Revision in Hybrid Representation Systems, LNAI 422, Springer-Verlag, Berlin (1990).

    MATH  Google Scholar 

  28. R. Bajcsy and M. Campos, Active and Exploratory Perception, Computer Vision, Graphics and Image Processing: Image Understanding, 56, 1, 31:40 (1992).

    Google Scholar 

  29. D. Kleinfeld, Sequential State Generation by Model Neural Networks, Proc. Nat. Acad. Sci. USA, 83, 9469:9473 (1986).

    Google Scholar 

  30. D. Marr, Vision, W.H. Freeman and Co, S. Francisco, CA, (1982).

    Google Scholar 

  31. J.J. Clark and A.L. Yuille, Data Fusion for Sensory Information Processing Systems, Kluwer Academic Publishers, Boston, MA, (1990).

    Google Scholar 

  32. J. Hadamard, Lectures on the Cauchy Problem in Linear Partial Differential Equations, Yale Press, New Haven, CT, (1923).

    Google Scholar 

  33. B.K.P. Horn, Robot Vision, The MIT Press, Cambridge, MA, (1986).

    Google Scholar 

  34. Roberts, Machine Perception of Three-dimensional Solids, in: Optical and Electro-optical Information Processing, J.P. Tippet et al eds., The MIT Press, Cambridge, MA (1965).

    Google Scholar 

  35. W.T. Freeman, The Generic Viewpoint Assumption in a Bayesian framework, in: Bayesian Approaches to Perception, D. Knilland W. Richards eds., Cambridge University Press, (1996).

    Google Scholar 

  36. J.L. Marroquin, Probabilistic Solutions of Inverse Problems, MIT Technical Report 860, (1985).

    Google Scholar 

  37. A. Blake and A. Zisserman, Visual Reconstruction, MIT Press, Cambridge, MA, (1987).

    Google Scholar 

  38. L. Bedini, I. Gerace, and A. Tonazzini, A deterministic Algorithm for reconstructing images with interacting discontinuities, CGVIP:Graphical Models and Imge Processing, vol 56, 109:123, (1994).

    Google Scholar 

  39. S. Geman and D. Geman, Stochastic relaxation, Gibbs distribution and the Bayesian restoration of images, IEEE Transaction on Pattern Analysis and Machine Intelligence PAMI, vol 6, 721:741, (1984).

    Google Scholar 

  40. E. Aarts and J. Korst, Simulated Aneealing and Boltzmann Machines, J. Wiley and Sons, NY (1989).

    Google Scholar 

  41. W.F. Bischof and M. Ferraro, Curved Mondrian: shading analysis of patterned objects, Computational Intelligence, vol. 5, 121:126, (1989).

    Google Scholar 

  42. D. Geiger and F. Girosi, Parallel Deterministic Algorithms from MRF’s:Surface Reconstruction, IEEE Transaction on Pattern Analysis and Machine Intelligence PAMI, vol 13, 401:412, (1991).

    Google Scholar 

  43. A.L. Yuille and H.H. Bulthoff, Bayesian Decision theory and Psychophysics, in: Bayesian Approaches to Perception, D. Knilland W. Richards eds., Cambridge University Press, (1996).

    Google Scholar 

  44. A. Yuille, M. Ferraro, and T. Zhang, Surface shape from image warping, Harvard Technical Report, n. 95–6 (1995).

    Google Scholar 

  45. H.H. Bulthoff and H.A. Mallot, Integration of Depth Modules: Stereo and Shading, Journal of the Optical Society of America A, vol 5, 1749:1758, (1988).

    Google Scholar 

  46. H. Bulthoff, Shape from X: Psychophysics and Computations, in: Computational Methods of Visual Processing, M.S. Landy and J.A. Movshon eds., The MIT Press, Cambridge, MA.

    Google Scholar 

  47. Y. Leclerc and A. Bobick, The Direct Computation of Height from Shading, Proc. IEEE Conference on Computer Vision and Pattern Recognition, Maui, Haw, 552:558, (1991).

    Google Scholar 

  48. J. Cryer, P. Tsai, and M. Shah, Integration of Shape from X Modules: Combining Stereo and Shading, Proc. IEEE Conference on Computer Vision and Pattern Recognition, New York, 720:721, (1993).

    Google Scholar 

  49. S. Pankanti and A.K. Jain, Integrating Vision Modules Stereo, Shading, Grouping and Line Labelling, IEEE Transaction on Pattern Analysis and Machine Intelligence PAMI, vol 17, 831:842, (1995).

    Google Scholar 

  50. MJ. Young, M.S. Landy, and L.T. Maloney, A Perturbation Analysis of Depth Perception from Combinations of Texture and Motion Cues, Vision Res, vol 33, 2685:2696, (1993).

    Google Scholar 

  51. E.B. Johnston, B.G. Cumming, and A.J. Parker, Integration of depth modules: Stereopsis and texture, Vision Research, vol. 33, 813:826 (1993).

    Google Scholar 

  52. J.J. Koenderink and A. Van Doorn, Photometric invariant related to solid shape, Opt. Acta, vol 27, 981:996,(1980).

    Google Scholar 

  53. M. Ferraro, Local Geometry of surfaces from shading amalysis, Journal Optical Society of America A, vol 11, 1575:1579, (1994).

    MathSciNet  Google Scholar 

  54. J. Oliensis, Shape from shading as a partially well-constrained problem, CVGIP: Image processing, vol 54, 163:183,(1991).

    Google Scholar 

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Author information

Authors and Affiliations

  1. Istituto di Psicologia, Università di Pavia, Piazza Botta, 6, 27100, Pavia, Italy

    Ornella Andreani-Dentici (chairperson)

  2. Dipartimento di Fisica Sperimentale, Università di Torino, Via Giuria, 1, 10125, Torino, Italy

    Mario Ferraro

  3. Dipartimento di Ingegneria Elettrica, Università di Palermo, Viale delle Scienze, 90128, Palermo, Italy

    Salvatore Gaglio

Authors
  1. Ornella Andreani-Dentici
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  2. Mario Ferraro
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  3. Salvatore Gaglio
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Editor information

Editors and Affiliations

  1. University of Pavia, Pavia, Italy

    Virginio Cantoni  & Alessandra Setti  & 

  2. University of Palermo, Palermo, Italy

    Vito Di Gesù  & Domenico Tegolo  & 

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© 1997 Springer Science+Business Media New York

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Andreani-Dentici, O., Ferraro, M., Gaglio, S. (1997). Panel Summary: Knowledge Model Representations. In: Cantoni, V., Di Gesù, V., Setti, A., Tegolo, D. (eds) Human and Machine Perception. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5965-8_19

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  • DOI: https://doi.org/10.1007/978-1-4615-5965-8_19

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-7734-4

  • Online ISBN: 978-1-4615-5965-8

  • eBook Packages: Springer Book Archive

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