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- W3136557734 abstract "Constructive approaches, principles, and methods of identification, recognition, and classification of pollen grains have been developed based on dynamic models, neural networks (NN), and mechanisms for extracting statistical, dynamic, specific information characteristics, preliminary image processing, and setting variables. Pollen recognition models are proposed, which rely on mechanisms for checking the similarity of micro-objects, highlighting the outline, dividing them into segments (classes), using specific features that differ from other classes, extracting the desired properties, characteristics, knowledge, performing image identification based on a set of numerical characteristics, database and knowledge base rules [1, 2]. The traditional computational schemes of multilayer NN, Kohonen NN, radial - basic network, which is optimized on the basis of network learning mechanisms with the variable setting, control of identification error along the boundaries of permissible values, taking into account the stationary, quasi-stationary and nonstationary behavior of the dynamics of the image contour, are investigated. Generalized algorithms for identifying images of pollen grains based on combined models and mechanisms of NN are proposed. The efficiency of the algorithms was investigated according to the criteria of the mean square error and the speed of information processing with combined dynamic and neural network identification models, linear, nonlinear, and compositional connections of neurons between network layers [3]. A software package for visualization, recognition, and classification of images of pollen grains has been developed, the implementations of which have been tested taking into account the conditions of a priori insufficiency, parametric uncertainty, and nonstationarity of processes." @default.
- W3136557734 created "2021-03-29" @default.
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- W3136557734 date "2021-01-01" @default.
- W3136557734 modified "2023-09-26" @default.
- W3136557734 title "Recognition and Classification of Pollen Grains Based on the Use of Statistical, Dynamic Image Characteristics, and Unique Properties of Neural Networks" @default.
- W3136557734 cites W2130350311 @default.
- W3136557734 doi "https://doi.org/10.1007/978-3-030-68004-6_22" @default.
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