Nonlinear fuzzy operators for image processing

Abstract The formal rules which describe the behavior of a ‘globally fuzzy’ technique for image processing are described in this paper. The analysis is then particularized, in order to get a deeper insight, to the case of a very simple operator of this family, putting to evidence the different sources of nonlinearity which are involved. Some examples briefly illustrating the achievable performances complete the presentation.

[1]  S. K. Pal,et al.  Fuzzy sets in image processing and recognition , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[2]  J. Keller,et al.  Fuzzy set theoretic approach to computer vision: An overview , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[3]  Giovanni Ramponi,et al.  Working on image data using fuzzy rules , 1992 .

[4]  S. Pal,et al.  Image enhancement using smoothing with fuzzy sets , 1981 .

[5]  O.K. AlShaykh,et al.  Fuzzy techniques for image enhancement and reconstruction , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[6]  Sankar K. Pal,et al.  Fuzzy models for pattern recognition , 1992 .

[7]  Sankar K. Pal,et al.  Image enhancement incorporating fuzzy fitness function in genetic algorithms , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[8]  F. Russo A new class of fuzzy operators for image processing: design and implementation , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[9]  Azriel Rosenfeld,et al.  Image enhancement and thresholding by optimization of fuzzy compactness , 1988, Pattern Recognit. Lett..

[10]  F. Russo,et al.  A user-friendly research tool for image processing with fuzzy rules , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[11]  Giovanni Ramponi,et al.  Fuzzy operator for sharpening of noisy images , 1992 .