Optimization of Identification of Images of Micro-Objects Taking Into Account Systematic Error Based on Neural Networks

Jumanov, Isroil I.; Djumanov, Olimjon I.; Safarov, Rustam A.

doi:10.1109/rusautocon49822.2020.9208164

Cited by 11 publications

(3 citation statements)

References 4 publications

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“…This study is devoted to the development of mechanisms for optimizing the identification of micro-objects using morphometric, dynamic, specific characteristics of images [13,14,15]. Preliminary processing of images with mechanisms of texture, contour segmentation, detection, regulation of variables, identification of micro-objects based on the use of neural networks (NN) is assumed, as a popular technology for visualization, recognition, classification of micro-objects of various types [16][17][18][19].…”

Section: Intoductionmentioning

confidence: 99%

Optimization of recognition of micro-objects based on the use of morphometric characteristics of images

Jumanov¹,

Safarov²

2022

Proceedings of III All-Russian Scientific Conference With International Participation "Science, Technology, Society: Environmen

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Constructive approaches, principles and methods of identification, recognition and classification of micro-objects based on the use of neural networks and mechanisms for extracting morphometric characteristics of images have been developed. Information processing technology based on obtaining images of micro-objects from a photo, video camera, digital microscope is proposed. A technique has been developed for interactive measurement of the size of micro-objects, counting, determining the structure, conducting statistical analysis, isolating and segmenting fragments, selecting informative points, recognizing and classifying images. A computational scheme for preliminary processing of images is built, including mechanisms for texture, contour segmentation, detection, and regulation of variables. Algorithms for learning neural networks with setting variables within the limits of permissible values, taking into account the properties of nonstationarity of image points, have been built. The effectiveness of learning algorithms combined with neural network dynamic models with mechanisms for regulating linear, nonlinear, compositional connections of neurons between network layers has been investigated. The study was carried out according to the criterion of the percentage of correct recognition. A software package for visualization, recognition, classification of images of pollen grains has been developed and implemented, which has been tested under conditions of a priori insufficiency, uncertainty and nonstationarity.

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Section: Intoductionmentioning

confidence: 99%

Optimization of recognition of micro-objects based on the use of morphometric characteristics of images

Jumanov¹,

Safarov²

2022

Proceedings of III All-Russian Scientific Conference With International Participation "Science, Technology, Society: Environmen

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show abstract

“…A computational scheme for the identification, recognition, and classification of microobjects, in particular, pollen grains, medical objects, is implemented, which includes the following blocks: input image input, preprocessing, identification of objects of interest, highlighting features and image features, recognition, and classification of a micro-object, presentation of results. The effectiveness of the proposed computational scheme is based on real data from the chest X-ray processing (CXR) system [4,5,[13][14][15].…”

Section: Main Part 21 Optimization Of the Identification Of Micro-objects Using The Morphological Features Of Imagesmentioning

confidence: 99%

“…In this case, only significant values of conditional probabilities are used. The points are approximated by averaging multidimensional normal Gaussian kernels, forming the product of their own one-dimensional kernels, centering them in each sample according to the Gaussian distribution [5,6]…”

Section: Main Part 21 Optimization Of the Identification Of Micro-objects Using The Morphological Features Of Imagesmentioning

confidence: 99%

Improving the quality of identification and filtering of micro-object images based on neural networks

2021

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Constructive approaches, principles, and models for optimizing the identification of micro-objects have been developed based on the use of combined statistical, dynamic models and neural networks with mechanisms for filtering noise and foreign particles of images of medical objects and pollen grains. Algorithms for learning neural networks under conditions of a priori insufficiency, uncertainty of parameters, and low accuracy of data processing are investigated. The mechanisms of contour selection, segmentation, obtaining the boundaries of segments with hard and soft thresholds, filtering using the morphological features of the image have been developed [1]. Mechanisms for recognition and classification of images, adaptation of parameter values, tuning of the network structure, approximation and smoothing of random emissions, bursts in the image contour are proposed. A mechanism for suppressing impulse noise and noise is implemented based on various filtering methods, preserving the boundaries of objects and small-sized parts. Mathematical expressions are obtained for estimating the identification errors caused by nonstationarity, inadequacy of approximation, interpolation, and extrapolation of the image contour. A software package for the recognition and classification of micro-objects has been developed. The results were obtained for correct, incorrect recognition, as well as rejected pollen samples, which were synthesized with cubic, biquadratic, interpolation spline-functions and wavelet transforms.

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Optimization of Identification and Recognition of Micro-objects Based on the Use of Specific Image Characteristics

Jumanov

Safarov

2023

Lecture Notes on Data Engineering and Communications Technologies

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Optimization of Identification of Images of Micro-Objects Taking Into Account Systematic Error Based on Neural Networks

Cited by 11 publications

References 4 publications

Optimization of recognition of micro-objects based on the use of morphometric characteristics of images

Optimization of recognition of micro-objects based on the use of morphometric characteristics of images

Improving the quality of identification and filtering of micro-object images based on neural networks

Optimization of Identification and Recognition of Micro-objects Based on the Use of Specific Image Characteristics

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