The analysis of thermal images is an urgent task for many areas of modern technology and technology, since thermal images are used in many areas of life. Disjointed systems do not allow you to quickly form a general idea of the ongoing processes, since they do not allow an analysis of all parameters. The combination of data spaces obtained in different ranges can increase the productivity and speed of decision making by automated systems. Most often, images obtained with only a thermal imaging camera are a set of spots that characterize the gradient of temperature change in space. These images do not have clear boundaries, which is associated with the physics of thermal energy transfer processes in space. Combination of data obtained by thermal imaging and a camera recording data in the visible range, which are familiar to perception and more understandable to humans. Areas of application of combined images are: medicine, when making a diagnosis, identifying patients in a crowd (for example, a pandemic), searching for oncology or tissue death processes (as a result of hypothermia or burns); security systems and access control systems; chemistry, in the analysis of mixing processes and reactions; automated and autonomous control systems when building automatic driving systems; and etc. The development of machine vision systems is based on the analysis of information received by light-sensitive matrices in the form of two-dimensional signals (images). Formation of images is associated with the imposition of distortions on them, which can affect the final result. It is possible to reduce the amount of distortion in different ways or a combination of them. One of the possible methods of primary data processing can be the use of an algorithm for combining data by means of their joint calibration, which will make it possible to achieve a relationship between a 3D point in the real world and the corresponding 2D image, a projection (each pixel) in the image taken by a calibrated camera. In this article, we will consider a thermal camera calibration algorithm based on the analysis of a test pattern.

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..

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