Article 3217

Title of the article



Fedotov Nikolay Gavrilovich, Doctor of engineering sciences, professor, head of sub-department of economic cybernetics, Penza State University (40 Krasnaya street, Penza, Russia),
Goldueva Dar'ya Alekseevna, Candidate of engineering sciences, associate professor, sub-department of economic cybernetics, Penza State University (40 Krasnaya street, Penza, Russia),
Mokshanina Mariya Alekseevna, Senior lecturer, sub-department of physics and mathematics, Penza State Agrarian University (30 Botanicheskaya street, Penza, Russia),

Index UDK

681.39; 007.001.362




Background. Most of the existing methods of half-tone or color object analysis generally presuppose prior simplification of an object to be analyzed involving image binarization. A side effect of image binarization is a loss of essential elements of useful information about the object. The paper suggests an alternative approach towards
half-tone image and colored texture analysis and recognition based on stochastic geometry and functional analysis.
Materials and methods. The proposed method for half-tone image and colored texture analysis and recognition makes it possible to form both the recognition features to describe geometric image particularities and the recognition features to reflect image color or brightness particularities.
Results. According to the suggested method recognition features can be created without analytical experts by means of automatic comuter generation followed feature space minimization, which is needed for the most reliable object recognition. The method allows to get recognition features invariant both to shift and rotation and to linear transformations of initial images, which is very important for the most of image analysis and recognition tasks.
Conclusions. The experimental results prove the effectiveness of the method suggested both for half-tone image and colored texture processing tasks.

Key words

image recognition, half-tone image, color texture, tracetransformation, triple feature, stochastic geometry

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Дата обновления: 07.11.2017 14:11