Machine learning paradigms for pattern recognition and image understanding

Spatial Vision

Volumn 10, Issue 1, 1996, Pages 87-103

  Caelli, Terry ^a   Bischof, Walter F ^b  

^a CURTIN UNIVERSITY   (Australia)

^b UNIVERSITY OF ALBERTA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL NEURAL NETWORK; DEPTH PERCEPTION; HUMAN; LEARNING; PATTERN RECOGNITION; PHYSIOLOGY; REVIEW;

FORM PERCEPTION; HUMANS; LEARNING; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, VISUAL; SPACE PERCEPTION;

PERCEPTUAL LEARNING ALGORITHMS; RECOGNITION BY PARTS PARADIGM;

COMPUTER VISION; FEATURE EXTRACTION; IMAGE CODING; IMAGE UNDERSTANDING; LEARNING ALGORITHMS; NEURAL NETWORKS; OBJECT RECOGNITION; PATTERN RECOGNITION;

LEARNING SYSTEMS;

EID: 0030153434     PISSN: 01691015     EISSN: None     Source Type: Journal    
DOI: 10.1163/156856896x00079     Document Type: Article

References (23)

1. Biederman, I. (1987). Recognition-by-components: A theory of human imageunderstanding, Psychol. Rev. 94, 115-147.
2. Bischof, W. F. and Caelli, T (1994). Learning structural descriptions of patterns: A new technique for conditional clustering and rule generation. Patt. Recognit. 27, 689-697.
3. Bischof, W. F. and Caelli, T. (1996). Scene understanding by rule evaluation, (submitted).
4. Caelli, T. and Dreier, A. (1994). Variations on the evidence-based object recognition theme. Patt. Recognit. 27, 185-204.
5. Caelli, T. and Pennington, A. (1993). An improved rule generation method for evidence-based classification systems. Patt. Recognit. 26, 733-740.
6. Cheeseman, P., Kelly, J., Self, M., Stutz, J., Taylor, W. and Freeman, D. (1990) In: Readings in Machine Learning. Shavlik and T. Dietterich (Eds). Morgan Kaufman, pp. 296-306.
7. Fan, T., Medioni, R. and Nevatia, R. (1989). Recognizing 3-D objects using surface descriptions. IEEE Trans. Patt. Anal. Machine Intell. 11, 1140-1156.
8. Flynn, P. and Jain, A. K. (1993). Three-dimensional object recognition. In: Handbook of Pattern Recognition and Image Processing, Volume 2: Computer Vision. Tzay Y. Young (Ed.). Academic Press, New York.
9. Grimson, W. E. L. (1990). Object Recognition by Computer. MIT Press, Cambridge.
10. Hoffmann, D. and Richards, W. (1986). Parts of recognition. In: From Pixels to Predicates. A. Pentland (Ed.). Ablex, New Jersey, pp. 268-294.
11. Jain, A. K. and Hoffman, D. (1988). Evidence-based recognition of objects. IEEE Trans. Patt. Anal. Machine Intell. 10, 783-802.
12. Julesz, B. (1984). Towards an axiomatic theory of preattentive vision. In: Dynamic Aspects of Neocortical Function. G. Edelman, W. Gall and M. Cowan (Eds). Neuroscience Research Foundation, New York, pp. 581-611
13. Lippman, R. P. (1987). An introduction to computing with neural nets. IEEE: ASSP Magazine, April 1987.
14. Milanese, R , Wechsler, H., Gil, S., Bost, J.-M. and Pun, T. (1994). Integration of bottom-up and top-down cues for visual attention using non-linear relaxation In: Proceedings of CVPR '94, Seattle, Washington, pp. 781-785.
15. Pearce, A., Caelli, T. and Bischof, W. F. (1994). Rulegraphs for graph matching in pattern recognition. Patt. Recognit. 27, 1231-1248.
16. Poggio, T. and Edelman, S. (1990) A network that learns to recognize three-dimensional objects. Nature 343, 263-266.
17. Poggio, T. and Girosi, F. (1990). Regularization algorithms for learning that are equivalent to multilayer networks. Science 247, 978-982.
18. Quinlan, J. R. (1993). C4.5 Programs for Machine Learning. Academic Press, New York.
19. Rentschler, I , Jüttner, M. and Caelli, T. (1994). Probabilistic analysis of human supervised learning and classification. Vision Res. 34, 669-687.
20. Shapiro, L. G. and Haralick, R. M. (1982). Organization of relational models for scene analysis. IEEE Trans. Patt. Recognit. Machine Intell. 4, 595-602.
21. Suetens, P., Fua, P. and Hanson, A. J. (1992). Computational strategies for object recognition. ACM Computing Surveys 24, 5-61.
22. Treisman, A. and Gelade, G. (1980). A feature integration theory of attention. Cognit. Psychol. 12, 97-136
23. Watt, R. and Morgan, M. (1985). A theory of the primary spatial code in human vision. Vision Res 25, 1661-1674.