Mechanisms For Using Image Properties And Neural Networks In Identification Of Micro-Objects

• Published in: International Conference on Application of Information and Communication Technologies pp. 1 - 6
• Main Authors: Jumanov, Isroil I, Safarov, Rustam A, Djumanov, Olimjon I
• Format: Conference Proceeding
• Language: English
• Published: IEEE 12.10.2022
• Subjects: Artificial neural networks; classification; Fingerprint recognition; identification; Image recognition; Interpolation; micro-object; pollen grains; recognition; Software algorithms; systematic error; Wavelet analysis; Wavelet transforms
• ISSN: 2472-8586
• DOI: 10.1109/AICT55583.2022.10013633

The problem of visualization, recognition, classification of images of micro-objects, in particular, pollen grains, unicellular organisms, fingerprints based on the definition of their variety, belonging to a class, the use of information of geometric shapes, morphology, dynamic, specific characteristics, unique features of neural networks has been investigated, in control systems of industrial and technological complexes, environmental monitoring, ecology, and medical diagnoses. Methods, learning algorithms, component computational schemes of neural networks have been developed, which provide the best quality of image identification in conditions of a priori insufficiency, uncertainty of parameters, and low accuracy of data processing. Mathematical expressions are obtained for estimating identification errors associated with information distortions at the measurement, input, and transmission stages due to nonstationarity, the inadequacy of approximation, interpolation, and extrapolation of the image contour. A software package for the recognition and classification of pollen grains has been built and implemented, which includes algorithms for a three-layer, loosely coupled neural network, Hopfield's network, bidirectional associative memory, Kohonen. Results are obtained for correct, incorrect recognition, and rejected pollen samples based on with-teacher and unsupervised learning algorithms, which are synthesized with cubic, biquadratic, and interpolation spline functions.