Fuzzy and rule-based image convolution

Mathematics and Computers in Simulation

Volumn 51, Issue 3-4, 2000, Pages 209-219

  Looney, Carl G ^a  

^a UNIVERSITY OF NEVADA   (United States)

Author keywords

Convolution; Edge detection; Images; Rule based convolution; Sharpening

Indexed keywords

EID: 0346107525     PISSN: 03784754     EISSN: None     Source Type: Journal    
DOI: 10.1016/s0378-4754(99)00118-4     Document Type: Article

References (9)

1. K.R. Castleman, Digital Image Processing, Prentice-Hall, Upper Saddle River, NJ, 1996.
2. J. Canny, A computational approach to edge detection, IEEE Trans. Pattern Analysis and Machine Intelligence 8(6) (1986) 679-698.
3. K. Etemad, R. Chellappa, A neural network based edge detector, in: Proc. IEEE 1993 Int. Conf. on Neural Networks, vol. 1, pp. 132-137.
4. S. Haykin, Analog and Digital Communications, Wiley, New York, 1989.
5. C.G. Looney, Squared Gaussian windowed sine functions for image enhancement, Technical Report UNR-CS-005, University of Nevada, Reno, 1998.
6. C.G. Looney, Pattern Recognition Using Neural Networks, Oxford University Press, New York, 1997.
7. F. Russo, A new class of fuzzy operators for image processing: design and implementation, in: Proc. IEEE 1993 Int. Conf. Neural Networks, vol. 1, pp. 815-820.
8. F. Russo, G. Ramponi, A fuzzy operator for the enhancement of blurred and noisy images, IEEE Trans. Image Processing 4(8) (1995) 1169-1174.
9. S. Yamamoto, Software representation of fuzzy sets and logic, in: S.G. Tzafestas, A.N. Venetsanopoulos (Eds.), Fuzzy Reasoning in Information, Decision and Control Systems, Kluwer Academic Publishers, Dordrecht, 1994, pp. 51-68.