EPJ Web Conf.
Volume 224, 2019IV International Conference “Modeling of Nonlinear Processes and Systems” (MNPS-2019)
|Number of page(s)||4|
|Section||Machine Learning, Artificial Intelligence and High-Performance Computing|
|Published online||09 December 2019|
Fusing Data Processing in the Construction of Machine Vision Systems in Robotic Complexes
Moscow State Technological University “STANKIN”, RU-127055, Moscow, Russia
2 Don State Technological University, RU-344000, Rostov-on-Don, Russia
3 City University of New York 2800 Victory Blvd, Staten Island, New York, USA
* e-mail: firstname.lastname@example.org
Published online: 9 December 2019
The development of machine vision systems is based on the analysis of visual information recorded by sensitive matrices. This information is most often distorted by the presence of interfering factors represented by a noise component. The common causes of the noise include imperfect sensors, dust and aerosols, used ADCs, electromagnetic interference, and others. The presence of these noise components reduces the quality of the subsequent analysis. To implement systems that allow operating in the presence of a noise, a new approach, which allows parallel processing of data obtained in various electromagnetic ranges, has been proposed. The primary area of application of the approach are machine vision systems used in complex robotic cells. The use of additional data obtained by a group of sensors allows the formation of arrays of usefull information that provide successfull optimization of operations. The set of test data shows the applicability of the proposed approach to combined images in machine vision systems.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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