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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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The paper deals with the issue of bone tissue restoration. To assist the specialist, we developed the SmartAssistantConstructor software package. The recovery process is based on finding a mirror copy of a piece of bone tissue. The article describes the algorithm for finding the missing part of the bone. The paper shows examples of this operation.

Infrared and thermal images have been used widely in the different forensics and security applications. Such images show the temperature difference between different objects and scene background. One of the drawbacks of such images is low contrast and noisy images which should be enhanced. This paper presents a new thermal image contour detection algorithm using the modified snake algorithm. The segmentation algorithm based on the image enhancement and the modified model of active contours based on regions, taking into account the calculation of the anisotropic gradient. Some presented experimental results illustrate the performance of the proposed cloud system on real thermal images in comparison with the traditional methods.