N.Ja. Vozna, A.I. Sydor


Evaluation of extended Hamming distance that can be applied in various fields of knowledge is considered in the paper. Known methods of recognition in the Hamming space are not effective because of their using for images that are described by binary vectors, and do not address the applicability of different analytical expressions mutual correlation functions and capabilities of data encryption in various theoretical and numerical bases. In the course of the research the coefficient of structural complexity of components attributes of multifunctional data was determined; weight coefficients of informative estimation of their parameters were assigned as well. Structural complexity criteria of certain classes of images and methods for determining the Hamming distance based on difference distance of modular structural complexity units of images were developed. The analysis of the proposed methods was provided on the example of recognition of symbolism figures. The coding system symbols of playing cards are offered. The formulas for finding Hamming distance used in the proposed encoding systems were described and derived. Comparative diagrams of Hamming distance estimates for pairs of symbols used in the proposed methods of Hamming distance determination were given. The main advantages and disadvantages of each of the proposed methods for the Hamming distance determination were identified. The proposed definition of Hamming distance encoding attributes of the objects, taking into account the complexity of the structural components, when determining of the extended Hamming distance qualitative features that most accurately and unambiguously characterize the object are considered that is a theoretical contribution to the theory of structural pattern recognition methods. Thus, t proposed methods of coding attributes and calculating of Hamming distance are the basis for using of such Hamming distance determining technologies for road signs, symbols chemicals and other two-dimensional images recognition.


signal space; Euclidean distance; pattern recognition; Information Technology; data encryption

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