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Smart infrastructure integrates cyberphysical systems, self-organizing networks of devices, fog computing to enhance the quality and convenience of manufacturing and social life, providing efficient resource management and increasing productivity and availability of traditional services. New technologies are characterized with a high degree of heterogeneity, structural complexity, and dynamic topology that result in a large number of new information and cyber security threats. Given a high degree of integration of digital cyber space into the processes of management, monitoring, aggregation, and data transferring, an assessment of its security level is a necessary requirement to maintain and control a safe and reliable environment for handling data and performing physical processes. Risk analysis and assessment is an extremely important task at all stages of the life cycle of subsystems and components of the smart environment. It allows us to assess a level of protection, identify vulnerable assets, and counteract the most likely directions of security threats. The known methods for security risk assessment do not consider the features of modern smart technologies, and this forces us to search for new approaches. This paper proposes a comprehensive method that includes a calculation of security indicators, risk assessment and selection of protective measures, based on attack graphs and thus allows us to consider the dynamics of changes in the components of the smart infrastructure. The automated system for assessing the security risks in the smart infrastructure and choosing the protective measures has been implemented.

Image-based object modeling has emerged as an important computer vision application. Typically, the process starts with the acquisition of the image views of an object. These views are registered within the global coordinate system using structure-and-motion techniques, while on the next step the geometric shape of an object is recovered using stereo and/or silhouette cues. This paper considers the final step, which creates the texture map for the recovered geometry model. The approach proposed in the paper naturally starts by backprojecting original views onto the obtained surface. A texture is then mosaiced from these back projections, whereas the quality of the mosaic is maximized within the process of Markov random field energy optimization. Finally, the residual seams between the mosaic components are removed via seam levelling procedure, which is similar to gradient-domain stitching techniques recently proposed for image editing. Unlike previous approaches to the same problem, intensity blending as well as image resampling are avoided on all stages of the process, which ensures that the resolution of the produced texture is essentially the same as that of the original views. Importantly, due to restriction to non-greedy energy optimization techniques, good results are produced even in the presence of significant errors on image registration and geometric estimation steps.