By Michał Baczyński (auth.), Leszek Rutkowski, Rafał Scherer, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Zurada (eds.)
This quantity constitutes the complaints of the tenth foreign convention on synthetic Intelligence and delicate Computing, ICAISC’2010, held in Zakopane, Poland in June 13-17, 2010. The articles are geared up in topical sections on Fuzzy structures and Their purposes; information Mining, type and Forecasting; picture and Speech research; Bioinformatics and scientific purposes (Volume 6113) including Neural Networks and Their purposes; Evolutionary Algorithms and Their purposes; Agent method, Robotics and regulate; numerous difficulties aof synthetic Intelligence (Volume 6114).
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Extra info for Artificial Intelligence and Soft Computing: 10th International Conference, ICAISC 2010, Zakopane, Poland, June 13-17, 2010, Part I
Contrapositive symmetry of distributive fuzzy implications, Internat. J. Uncertain. Fuzziness Knowledge-Based Systems 10, 135–147 (2002) 3. : On the distributivity of fuzzy implications over continuous and Archimedean triangular conorms. Fuzzy Sets and Systems 161, 1406–1419 (2010) 4. : On the characterizations of (S,N)-implications. Fuzzy Sets and Systems 158, 1713–1727 (2007) 5. : On the distributivity of fuzzy implications over nilpotent or strict triangular conorms. IEEE Trans. Fuzzy Syst.
Fuzzy methods can also be applied in deciding the appropriate color like in  where the authors propose to model the hues of the perceptually distinct colors as fuzzy sets and then select as output the most plausible color among those in the ﬁltering window. Another fuzzy ﬁltering approach is proposed in where the authors deﬁne a new fuzzy metric that reduces the computational cost of the ﬁltering procedure. In , an adaptive method of fuzzy ﬁltering is proposed in respect to the ﬁltering window size, which is adapted to the local level of noise contamination (level judged by a fuzzy set theoretic approach).
Vlaicu Implementation and Results The performance of the proposed algorithm has been evaluated and compared with several other existing ﬁlters for impulse noise reduction. The algorithm of the proposed cascade of ﬁlters has been implemented in Matlab, using the Image Processing Toolbox and Fuzzy Logic Toolbox. In the experiment we use a ﬁltering window of 3 × 3 pixels. For pixel classiﬁcation based on its local chromaticity required in the adaptation of the fuzzy ﬁlters cascade - we use a neighborhood size of 17×17 pixels, which is large enough to be relevant to our local classiﬁcation goal.