METHOD OF FINDING ENERGY CENTERS OF LASER PATHS IMAGES FRAGMENTS

Authors

DOI:

https://doi.org/10.32703/2617-9040-2022-39-23

Keywords:

laser paths, parallel-hierarchical transformations, image processing, laser spots

Abstract

Considered method of finding energy fragments of images of laser tracks in real time and its application for image recognition problems are considered. Given theoretical information, experimental research and software implementation of the image recognition system similar to natural structures.
Presented an analysis of recent work on neurobiology and work related to the modeling of neural mechanisms. The main problems in the existing work related to the modeling of information perception systems in a natural way have been identified.
The aim of the study is the software application of the method of finding the energy centers of images in real time to optimize these energy centers. The task of the research is to analyze the application of the method of finding the energy centers of fragments of images of laser paths. Carried out the description of the method of analysis of images of laser tracks on the basis of determination of the center of gravity on the basis of moment signs is carried out.
Described an example of work is given and the main functionality of the laser image processing program. Presented samples of reference images and individual fragments of long laser paths used in the experiments, as well as the curves of their energy centers. Shown computer simulation of laser path image processing was performed, as a result of which the adequacy of the calculated results.

References

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REFERENCES

Tymchenko L., Kokriatskaia, N., Gertsiy, A., Kotyra, A., Amirgaliyev, Y. (2019). Elaboration of pyramidal methods applying computation technique "rough-fine" image identification. Proceedings of SPIE - The International Society for Optical Engineering. DOI: 10.1117/12.2537179.

Nakonechna S. (2014). Obroblennya zobrazhen' plyam lazernih puchkіv іz zastosuvannyam paralel'no-ієrarhіchnih merezh [Image processing of laser beam spots using parallel-hierarchical networks] Extended abstract of candidate’s thesis. Lviv [in Ukrainian].

Tymchenko L., Tverdomed V., Petrovsky N., Kokryatska N., Maistrenko Y. (2019). Development of a method of processing images of laser beam bands with the use of parallel hierarchic networks. DOI: 10.15587/1729-4061.2019.188568

Romanyuk O. (20150). Method of anti-aliasing with the use of the new pixel model. Proceedings SPIE 9816.

Timchenko L. (2014). Organization of HighPerformance Parallel-Hierarchical Computing Processes for Classification of Laser Beam Images. Proceedings of the 12th International Conference on DAS-2014, Universitatea Stefan cel Mare Suceava, 192-197.

Romanyuk S., Pavlov S., Melnyk O. (2015). New method to control color intensity for antialiasing,” International “Siberian Conference Control and Communications. SIBCON. DOI: 10.1109/SIBCON.2015.7147194.

Tang Y. Li J. Zhou T. Schille J. (2018). Dynamic beam shaping with polarization control at the image plane for material processing. Liverpool. DOI: https://doi.org/10.1016/j.procir.2018.08.083

Yarovyj, A. A., Kokriats'ka, N. I., Nakonechna, S. V., Matejchuk, M. S., Pol'hul', T. D. (2014). Analiz obchysliuval'noi skladnosti GPU-oriientovanykh paralel'no-iierarkhichnykh obchysliuval'nykh system ta otsiniuvannia produktyvnosti ikh aparatnoho zabezpechennia [Analysis of computational complexity GPU-oriented hierarchical parallel computing and performance evaluation of hardware]. Optyko-elektronni informatsijno-enerhetychni tekhnolohii – Opto-electron Information technology the energy, 1 (27), 18–25 [in Ukrainian].

Tymchenko, L. I., Kokriats'ka, N. I., Mel'nikov, V. V., Nakonechna, S. V. (2012). Novyj metod prohnozuvannia iz zastosuvanniam paralel'no-iierarkhichnoi merezhi [A new forecasting method using the parallel-hierarchical network], materialy mizhnar. nauk.-tekhn. konf. Shtuchnyj intelekt. Intelektual'ni systemy – Artificial Intelligence. Intelligent Systems, 59–62 [in Ukrainian].

Timchenko L. (2017). Parallel-hierarchical networks for processing biomedical images and images of stains of laser beams. Experimental research. ASMI, Poltava.

Sawicki D. (2015). Using the GPU to determine the area the flame in the vision diagnostic system. Informatyka Automatyka Pomiary w Gospodarce i Ochronie Srodowiska.

Lawicki T., Zhirnova O. (2015). Application of curvelet transform for denoising of CT images. Proceedings SPIE 9662.

Orlov D. (2011). Determination of the position of the center of a laser beam when the dynamic range of the matrix receiver is exceeded. Measurement Techniques.

Aharon O. Laser Beam Profiling and Measurement. Retrieved from: http://www.novuslight.com/laser-beam-profiling-and-measurement_N678.html.

Roundy C. (2016). Current Technology of Laser Beam Profile Measurements. Ophir-Spiricon Inc.

Published

2022-06-28

Issue

Section

Information, telecommunication and resource saving technologies

How to Cite

METHOD OF FINDING ENERGY CENTERS OF LASER PATHS IMAGES FRAGMENTS. (2022). Transport Systems and Technologies, 39, 243-251. https://doi.org/10.32703/2617-9040-2022-39-23

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