Fuzzy set theory in computer vision: A prospectus

Fuzzy Sets and Systems

Volumn 90, Issue 2, 1997, Pages 177-182

  Keller, James M ^a  

^a UNIVERSITY OF MISSOURI   (United States)

Author keywords

Computer vision; Enhancement; Fuzzy sets; Recognition; Rule based approach; Segmentation

Indexed keywords

EID: 2042463149     PISSN: 01650114     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0165-0114(97)00084-5     Document Type: Article

References (10)

1. J.C. Bezdek, S.K. Pal (Eds.), Fuzzy Models for Pattern Recognition, IEEE Press, New York, 1992.
2. J. Keller, R. Krishnapuram, Fuzzy set methods in computer vision, in: R. Yager, L. Zadeh (Eds.), An Introduction to Fuzzy Logic Applications in Intelligent Systems, Kluwer, Boston, 1992, pp. 121-146.
3. J. Keller, R. Krishnapuram, Fuzzy decision models in computer vision, in: R. Yager, L. Zadeh (Eds.), Fuzzy Sets, Neural Networks, and Soft Computing, Van Nostrand, New York, 1994, pp. 213-232.
4. J. Keller, R. Krishnapuram, P. Gader, Y.-S. Choi, Fuzzy rule-based models in computer vision, in: W. Pedrycz (Ed.), Fuzzy Modelling: Paradigms and Practice, Kluwer Academic Publishers, Dordrecht, 1996, pp. 353-371.
5. J.M. Keller, X. Wang, Comparison of spatial relation definitions in computer vision, Proc. ISUMA/NAFIPS'95, College Park, MD, 1995, pp. 679-684.
6. R. Krishnapuram, J. Keller, Fuzzy and possibilistic clustering methods for computer vision, in: S. Mitra, M. Gupta, N. Kraske (Eds.), Neural and Fuzzy Systems, SPIE Press, 1994, pp. 133-159.
7. D. Marr, Vision, Freeman, San Francisco, CA, 1982.
8. S.K. Pal, Fuzziness, image information and scene analysis, in: R. Yager, L. Zadeh (Eds.), An Introduction to Fuzzy Logic Applications in Intelligent Systems, Kluwer, Boston, 1992, pp. 147-183.
9. Proc. 3rd IEEE Internat. Conf. on Fuzzy Systems, Or-lando, Florida, 1994.
10. Proc. 4th IEEE Internat. Conf. on Fuzzy Systems, Yokohama, Japan, 1995.