A fragment-based approach to object representation and classification

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2059, Issue , 2001, Pages 85-100

  Ullman, Shimon ^a   Sali, Erez ^a   Vidal Naquet, Michel ^a  

^a WEIZMANN INSTITUTE OF SCIENCE   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; COMPUTERS;

CLASSIFICATION ERROR RATE; COMPUTER VISION SYSTEM; FACE CLASSIFICATION; MUTUAL INFORMATIONS; OBJECT FRAGMENTS; OBJECT PERCEPTION; OBJECT REPRESENTATIONS; VISUAL CLASSIFICATION;

ECONOMIC AND SOCIAL EFFECTS;

EID: 84957588617     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45129-3_7     Document Type: Conference Paper

References (33)

1. Amit, Y., Geman, D., Wilder, K.: Joint Induction of Shape Features and Tree Classifiers. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 19, 11 (1997) 1300-1306.
2. Bhat, D., Nayar, K. S.: Ordinal Measures for Image Correspondence. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 20, 4 (1998) 415-423.
3. Biederman, I.: Human image understanding: recent research and theory. Computer Vision, Graphics and Image Processing 32 (1985) 29-73.
4. Binford, T. O.: Visual perception by computer. IEEE conf. on systems and control, Vol. 94, 2 (1971) 115-147.
5. Chow, C.K., Liu, C.N.: Approximating Discrete Probability Distributions with Dependence Trees. IEEE Transactions on Information Theory, Vol. 14, 3 (1968) 462-467.
6. Cover, T.M. & Thomas, J.A.: Elements of Information Theory. Wiley Series in Telecommunication, New York (1991).
7. Edelman, S.: Representing 3D objects by sets of activities of receptive fields. Biological cybernitics 70 (1993) 37-45.
8. Gallant, J.L., Braun, J., Van Essen, D.C.: Selectivity for polar, hyperbolic, and cartesian gratings in macaque visual cortex. Science, 259 (1993) 100-103.
9. Green, D. M., Swets, J. A.: Signal Detection Theory and Psychophysics. Wiley, Chichester New York Brisbane Toronto (1966). Reprinted by Krieger, Huntingdon, New York (1974).
10. Hubel, D. H., Wiesel, T. N.: Receptive fields and functional architecture of monkey striate cortex. Journal of physiology 195 (1968) 215-243.
11. Logothetis, N. K., Pauls J., Bülthoff H. H., Poggio T.: View-dependent object recognition in monkeys. Current biology, 4 (1994) 401-414.
12. Marr, D.: Vision. W.H. Freeman, San Francisco (1982).
13. Marr, D., Nishihara, H. K.: Representation and recognition of the spatial organization of three dimensional structure. Proceedings of the Royal Society of London B, 200 (1978) 269-294.
14. Mel, W. B.: SEEMORE: Combining color, shape and texture histogramming in a neurally inspired approach to visual object recognition. Neural computation 9 (1997) 777-804.
15. Minsky, M., Papert, S.: Perceptrons. The MIT Press, Cambridge, Massachusetts (1969).
16. Miyashita, Y., Chang, H.S.: Neuronal correlate of pictorial short-term memory in the primate temporal cortex. Nature, 331, (1988) 68-70.
17. Murase, H., Nayar, S.K.: Visual learning and recognition of 3-D objects from appearance. International J. of Com. Vision, 14 (1995) 5-24.
18. Nelson, C. R., Selinger A.: A Cubist approach to object recognition. International Conference on Computer Vision '98 (1998) 614-621.
19. Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufman Publishers, San Mateo, California (1988).
20. Perret, D. I., Rolls, E. T., Caan W.: Visual neurons responsive to faces in the monkey temporal cortex. Experimental brain research, 47 (1982) 329-342.
21. Poggio, T., Sung, K.: Finding human faces with a gaussian mixture distribution-base face model. Computer analysis of image and patterns (1995) 432-439.
22. Poggio, T., Edelman, S.: A network that learns to recognize three-dimensional objects. Nature, 343 (1990) 263-266.
23. Riesenhuber, M., Poggio, T.: Hierarchical models of object recognition in cortex. Nature Neuroscience, Vol. 2, 11 (1999) 1019-1025.
24. Rolls, E. T.: Neurons in the cortex of the temporal lobe and in the amygdala of the monkey with responses selective for faces. Human neurobiology, 3 (1984) 209-222.
25. Rosch, E. Mervis, C.B., Gray, W.D., Johnson, S.M., Boyes-Braem, P.: Basic objects in natural categories. Cognitive Psychology, 8 (1976) 382-439.
26. Schneiderman, H., Kanade. T.: Probabilistic modeling of local appearance and spatial relationships for object recognition. Proc. IEEE Comp. Soc. Conference on Computer Vision and Pattern Recognition, CVPR98, (1998) 45-51.
27. Tanaka, K.: Neural Mechanisms of Object Recognition. Science, Vol. 262 (1993) 685-688.
28. Turk M., Pentland A.: "Eigenfaces for recognition", Cognitive Neuroscience, 3 (1990) 71-86.
29. Ullman, S., Basri, R.: Recognition by Linear Combination of Models. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 13, 10 (1991) 992-1006.
30. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995).
31. von der Heydt, R., Peterhans, E., Baumgartner G.: Illusory Contours and Cortical Neuron Responses. Science, 224 (1984) 1260-1262.
32. Weber, M, Welling M. & Perona, P.: Towards Automatic Discovery of Object Categories. Proc. IEEE Comp. Soc. Conference on Computer Vision and Pattern Recognition, CVPR2000, 2, (2000) 101-108.
33. Wiskott, L., Fellous J. M., Krüger N., von der Malsburg, C.: Face Recognition by Elastic Bunch Graph Matching. Intelligent Biometric Techniques in Fingerprint and Face Recognition, 11 (1999) 355-396.