New-distinction measure for pattern recognition in fuzzy features space

Konstantin A. Zlotnikov; Boris F. Fyodorov

doi:10.1117/12.360320

26 August 1999 New-distinction measure for pattern recognition in fuzzy features space

Konstantin A. Zlotnikov, Boris F. Fyodorov

Proceedings Volume 3837, Intelligent Robots and Computer Vision XVIII: Algorithms, Techniques, and Active Vision; (1999) https://doi.org/10.1117/12.360320
Event: Photonics East '99, 1999, Boston, MA, United States

Abstract

Problem of pattern recognition can be interpreted as a problem of acceptance of optimal decision under conditions of uncertainty, caused by absence of the complete and authentic information about a recognized object and its features. The unique adequate method of solving of pattern recognition problem in the conditions of uncertainty is the decisions making by the whole set of available heterogeneous information, taking into account a significance and reliability of each of considered feature and their interrelation. Usually the solution of pattern recognition problem is reduced to the task of minimization of distance from an image of the object up to the standard image of the class of objects. In this paper we offer and review the possible approach to generalization of the Mahalnobis metrics, based on properties of fuzzy number in L-R form. The results of the experimental comparison of the effectiveness of pattern recognition using the considered set of fuzzy features and criteria are discussed.

Citation Download Citation

Konstantin A. Zlotnikov and Boris F. Fyodorov "New-distinction measure for pattern recognition in fuzzy features space", Proc. SPIE 3837, Intelligent Robots and Computer Vision XVIII: Algorithms, Techniques, and Active Vision, (26 August 1999); https://doi.org/10.1117/12.360320

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