Abstract
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.
Highlights
The development of scientific and methodological foundations for adaptive processing of information of continuous objects based on the use of neural networks (NN) contribute to solving a wide range of problems related to recognition, classification, approximation, forecasting in control systems of industrial and technological complexes, palynology, environmental protection and ecology, medicine, forensics
This study is devoted to the development of constructive approaches, principles, identification models aimed at using combined statistical, dynamic models and neural networks to optimize the identification of micro-objects with mechanisms for using statistical, dynamic, specific characteristics, as well as filtering non-stationary components of images
It has been determined that the developed mechanisms can improve the accuracy of segmentation, detection of boundaries of objects of interest by 1-2% compared to traditional technologies for determining the boundaries of objects in chest X-ray processing (CXR) images
Summary
The development of scientific and methodological foundations for adaptive processing of information of continuous objects based on the use of neural networks (NN) contribute to solving a wide range of problems related to recognition, classification, approximation, forecasting in control systems of industrial and technological complexes, palynology, environmental protection and ecology, medicine, forensics. The problem of identifying images of micro-objects, in particular, pollen grains, unicellular microorganisms, fingerprints, pictures of useful minerals in the composition of the rock mass, is of great importance [1]. This study is devoted to the development of constructive approaches, principles, identification models aimed at using combined statistical, dynamic models and neural networks to optimize the identification of micro-objects with mechanisms for using statistical, dynamic, specific characteristics, as well as filtering non-stationary components of images. It is proved that such methods of image identification have a number of undoubted advantages, simplicity of implementation, high speed of information processing [2]
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