The article presents the results of a study of the contour analysis algorithm developed by the authors for automatic recognition of objects in an image with their subsequent modeling in virtual simulators for training human-computer systems operators. The main distinctive feature of the developed contour analysis algorithm is the use of image convolution in four directions and the tracing procedure, which makes it possible to more accurately search for the contour of objects compared to other algorithms of this type. The results of the study of the algorithm are given. Based on this study, recommendations on the choice of the parameters of the algorithm are given. As a result of the analysis of three-dimensional modeling methods, it was concluded that for the effective implementation of three-dimensional graphics in virtual simulators for training human-machine system operators, it is advisable to use an algorithm for contour image analysis of external objects.

In modern control systems and information processing, the recognition of objects in the image is complicated by the fact that the impact of negative factors introduces uncertainty into this process, leading to blurring of images. In this regard, it is necessary to develop models and algorithms that would reduce the degree of uncertainty in image processing. These models are necessary, for example, when monitoring environmentally hazardous objects, for search and detection of unauthorized burial of household waste, in the field of information security, in the analysis of x-rays and thermograms, in the actions of unmanned aerial vehicles of law enforcement agencies in autonomous mode. This article presents a description of information technology for recognition in the automated mode of objects in images. The basis of this technology is the algorithm of contour analysis of images. The main distinguishing feature of the algorithm is the use of convolution of the image in four directions, as well as the tracing procedure. The aim of the study was to develop algorithms for high-speed automated visualization of external objects. We present the results of the study of the algorithm of contour analysis in the processing of various images in the visible and infrared wavelengths. Recommendations are formulated for the choice of parameters of the contour analysis algorithm, such as the mean square deviation in image blur, minimum and maximum thresholds for filtering. The results of the study can be used in production management systems, life support of the city, technical vision, environmental conditions, monitoring of business processes, as well as in the creation of simulators for training operators of complex systems, etc. In addition, we show the expediency of applying the algorithm we developed in decision support systems.

In modern information systems, decision making based on image processing is hampered by the impact of negative external and internal factors leading to image blurring, which introduces uncertainty in this process. In this regard, algorithms and models are used to reduce the effect of uncertainty in image analysis. The article presents a new adaptive algorithm for image processing in different wave bands. The article also presents the results of research on the training of operators of image processing systems in conditions of uncertainty. It is proposed to train the operators of these systems on the basis of a competence-based approach using an information system that allows you to create individual training paths for the operators. The implementation of the training information system is proposed to be made on the basis of a web service.