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Instruments and Systems: Monitoring, Control, and Diagnostics

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Organization of Computer Vision Systems Based on Fuzzy Set Theory Techniques
P.I. KARASEV, A.A. AL-RAMMAHI, YU.YU.GROMOV, YU.V. MININ

This paper presents a model that uses fuzzy methods to achieve better image processing under uncertainty conditions. A rule base will be developed to create such models. The problem of data fuzziness is solved using probability density functions and probability distribution functions, while data analysis is presented with reference to each of the" analysis rules of the fuzzy set, which will be obtained by applying an aggregation function, which will be determined using the OWA operator(the Jager operator). The proposed design provides a solution to the problem of converting a discrete set of data values into a fuzzy set (fuzzification), which is a fairly well-solved problem for applied control methods, but has so far presented great difficulties for visual objects. Moreover, the proposed data analysis model provides a solution to non-trivial problems with visual objects.
Keywords: fuzzy methods, aggregation functions, computer vision, image processing, membership functions, OWA operators.


DOI: 10.25791/pribor.5.2021.1259

Pp. 22-26.

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