Error Control of Identification and Filtering of Micro-Object Images

Jumanov, I.I.; Safarov, R.A.; Djumanov, O.I.

doi:10.47813/nto.3.2022.6.109-124

Cited by 2 publications

(2 citation statements)

References 5 publications

(6 reference statements)

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“…Classifiers of fragments and features are built on various principles and models. The efficiency of computational schemes for optimizing training in NN was investigated based on the use of linear and nonlinear regression models, support vector machines, and a decision tree [20][21][22].…”

Section: Optimization Of Recognition Of Micro-objects Based On Comput...mentioning

confidence: 99%

Optimization of recognition of micro-objects based on reducing excessive information structures of images

Jumanov

Safarov

2022

J. Phys.: Conf. Ser.

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Scientific and methodological foundations of identification, recognition, classification of micro-objects using redundant information structures - morphometric, histological, fractal characteristics of images have been developed. Mechanisms for extracting statistical, dynamic, specific characteristics and rarefaction of redundant information structures are proposed. Dynamic models have been developed, combined with the capabilities of neural networks. Computational schemes for preliminary processing of images, texture, segmentation, filtering, approximation, regulation of variable values and optimization have been developed. Methods of recognition and classification of micro-objects with tools for obtaining images from a photo, video camera, digital microscope, interactive measurement, counting, structure determination, statistical analysis, isolation and segmentation, and the formation of informative (reference) points of image contours have been investigated. The traditional computational schemes of a multilayer neural network, a Kohonen neural network, and a radial-basic network are investigated. Modified algorithms for training a network with mechanisms for adjusting the values of variables, monitoring errors along the boundaries of permissible values, accounting for stationary, quasi-stationary and non-stationary behavior of image points during the formation of training samples have been developed. Generalized algorithms for the identification of images of pollen grains are proposed. The efficiency of the algorithms was investigated according to the criteria of the root mean square error and the speed of information processing. A software package for visualization, recognition, 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, uncertainty and nonstationarity of processes.

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Section: Optimization Of Recognition Of Micro-objects Based On Comput...mentioning

confidence: 99%

Optimization of recognition of micro-objects based on reducing excessive information structures of images

Jumanov

Safarov

2022

J. Phys.: Conf. Ser.

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“…Mechanisms for regulating variable identification models of RTS based on autoregressive, adaptive smoothing by R. Brown, Newton, Lagrange polynomials, orthogonal algebraic polynomials of 3, 5, 7 orders have been developed and implemented [20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Optimization of identification of micro-objects with error control and statistical image filtering mechanisms

Jumanov

Kholmonov

2022

J. Phys.: Conf. Ser.

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Scientific and methodological foundations have been developed for the optimal identification of non-stationary objects transmitted by random time series in control systems of industrial and technological complexes, monitoring of environmental protection and ecology, palynology, medicine based on the use of statistical, dynamic, neural network models and mechanisms for extracting statistical, dynamic specific characteristics of information. The mechanisms for controlling the error in the identification of RTS, based on the use of statistical criteria - the rules of the sequential analysis, Bayesian estimation, ±3σ, confidence interval, have been investigated. The problem of control of the error in the identification of RTS by the confidence interval, the mechanisms of threshold control of the values of the random time series elements, their increments, the error of prediction by statistical, trend, dynamic models, algebraic polynomials, parabolic, cubic interpolation, extrapolation spline functions. Mechanisms for regulating the variables of the random time series identification models based on autoregressive, adaptive smoothing by R. Brown, Newton, Lagrange polynomials, orthogonal algebraic polynomials of 3, 5, 7 orders have been developed. The methodology for assessing the quality of the identification of RTS by mathematical expectation and variance and the criterion of correspondence between the relations of mathematical expectation and variance of two consecutive elements have been implemented. The experimental study was carried out according to the real data of the power grid enterprise. The effectiveness of the implemented mechanisms was studied according to the criteria of the minimum root-mean-square error, labor intensity, cost of information processing.

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Error Control of Identification and Filtering of Micro-Object Images

Cited by 2 publications

References 5 publications

Optimization of recognition of micro-objects based on reducing excessive information structures of images

Optimization of recognition of micro-objects based on reducing excessive information structures of images

Optimization of identification of micro-objects with error control and statistical image filtering mechanisms

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