Inferring perceptual saliency fields from viewpoint-dependent recognition data

Neural Computation

Volumn 11, Issue 6, 1999, Pages 1331-1348

  Cutzu, Florin ^a   Tarr, Michael ^a  

^a BROWN UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; BIOLOGICAL MODEL; HUMAN; PATTERN RECOGNITION; PHYSIOLOGY; VISION;

ALGORITHMS; HUMANS; MODELS, BIOLOGICAL; PATTERN RECOGNITION, VISUAL; VISUAL PERCEPTION;

EID: 0033566440     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976699300016269     Document Type: Article

References (33)

1. Ashby, F. G. (Ed.). (1992). Multidimensional models of perception and cognition. Hillsdale, NJ: Erlbaum.
2. Biederman, I. (1987). Recognition by components: A theory of human image understanding. Psychol. Review, 94, 115-147.
3. Biederman, I., & Gerhardstein, P. C. (1993). Recognizing depth-rotated objects: Evidence and conditions for three-dimensional viewpoint invariance. Journal of Experimental Psychology: Human Perception and Performance, 19(6), 1162-1182.
4. Biederman, I., & Gerhardstein, P. C. (1995). Viewpoint-dependent mechanisms in visual object recognition. Journal of Experimental Psychology: Human Perception and Performance, 21(6), 1506-1514.
5. Bülthoff, H. H., & Edelman, S. (1992). Psychophysical support for a two-dimensional view interpolation theory of object recognition. Proc. Natl. Acad. Sci. USA, 89, 60-64.
6. Carroll, J. D., & Chang, J. J. (1970). Analysis of individual differences in multidi-mensional scaling via an N-way generalization of the Eckart-Young decomposition. Psychometrika, 35, 283-319.
7. Cutzu, F., & Edelman, S. (1998). Representation of object similarity in human vision: Psychophysics and a computational model. Vision Research, 38(15/16), 2229-2258.
8. Cutzu, F., & Tarr, M. (1997, February). The representation of three-dimensional object similarity in human vision. In Proc. SPIE Conf. on Electronic Imaging: science and technology, San Jose, CA.
9. Edelman, S. (1995). Representation of similarity in 3D object discrimination. Neural Computation, 7, 407-422.
10. Engl, H. W., Hanke, M., & Neubauer, A. (1996). Regularization of inverse problems. Dordrecht: Kluwer.
11. Geman, S., & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721-741.
12. Geman, D., & Reynolds, G. (1992). Constrained restoration and the recovery of discontinuities. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(3), 367-383.
13. Groetsch, C. W. (1993). Inverse problems in the mathematical sciences. Braunschweig: Vieweg & Sohn.
14. Hansen, P. C. (1998). Rank-deficient and discrete ill-posed problems: Numerical aspects of linear inversion. Philadelphia: Society for Industrial and Applied Mathematics.
15. Hayward, W. G., & Tarr, M. J. (1997). Testing conditions for viewpoint invariance in object recognition. Journal of Experimental Psychology: Human Perception and Performance, 23(5), 1511-1521.
16. Koenderink, J. J., & van Doorn, A. J. (1976). The singularities of the visual mapping. Biological Cybernetics, 24, 51-59.
17. Li, S. Z. (1995). Markov random field modeling in computer vision. Berlin: Springer-Verlag.
18. Marroquin, J. (1984). Surface reconstruction preserving discontinuities (A.I. Memo No. 792). Cambridge, MA: Artificial Intelligence Laboratory, Massachusetts Institute of Technology.
19. Morozov, V. A. (1993). Regularization methods for ill-posed problems. Boca Raton, FL: CRC Press.
20. Palmer, S. E., Rosch, E., & Chase, P. (1981). Canonical perspective and the perception of objects. In J. Long & A. Baddeley (Eds.), Attention and performance IX (pp. 135-151). Hillsdale, NJ: Erlbaum.
21. Poggio, T., Torre, V., & Koch, C. (1985). Computational vision and regularization theory. Nature, 317, 314-319.
22. Regińska, T. (1996). A regularization parameter in discrete ill-posed problems. SIAM J. Sci. Comput., 17(3), 740-749.
23. Rosch, E., Mervis, C. B., Gray, W. D., Johnson, D. M., & Boyes-Braem, P. (1976). Basic objects in natural categories. Cognitive Psychology, 8, 382-439.
24. Schwarz, H.-R. (1988). Finite element methods. Orlando, FL: Academic Press.
25. Schyns, P. G., Goldstone, R. L., & Thibaut, J.-P. (1998). The development of features in object concepts. Behavioral and Brain Sciences, in press.
26. Skilling, J. (Ed.). (1989). Maximum entropy and Bayesian methods. Dordrecht: Kluwer.
27. Tarr, M. J. (1995). Rotating objects to recognize them: A case study of the role of viewpoint dependency in the recognition of three-dimensional objects. Psychonomic Bulletin and Review, 2(1), 55-82.
28. Tarr, M. J., & Bülthoff, H. H. (1995). Is human object recognition better described by geon-structural-descriptions or by multiple-views? Journal of Experimental Psychology: Human Perception and Performance, 21(6), 1494-1505.
29. Tarr, M. J., Bülthoff, H. H., Zabinski, M., & Blanz, V. (1997). To what extent do unique parts influence recognition across changes in viewpoint? Psychological Science, 8(4), 282-289.
30. Tikhonov, A. N., & Arsenin, V. Y. (1977). Solutions of ill-posed problems. Washington, D.C.: W. H. Winston.
31. Torgerson, W. S. (1958). Theory and methods of scaling. New York: Wiley.
32. Tversky, B., & Hemenway, K. (1984). Objects, parts, and categories. Journal of Experimental Psychology: General, 113, 169-193.
33. Wahba, G. (1990). Spline models for observational data. Philadelphia: Society for Industrial and Applied Mathematics.