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This work addresses the automatic detection of complex situations in image sequences in the video surveillance context. There are difficulties when dealing with data from natural environments. This work expands the formalism of FMTHL and SGTs to deal with erroneous, missing, and noisy data and complexity, demonstrates the robustness of situational recognition in natural scenarios, and expands generic applicability beyond discourse boundaries.
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The aim of this work was the development of new image processing methods for the analysis and optimization of rotary kiln processes. It is shown how the newly developed methods can be employed to analyze certain areas and effects of rotary kiln processes. Thereby gained new process information can be used in the process control to optimize rotary kiln processes in terms of economic and environmental objectives.
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In machine vision, the illumination design has direct impact on the performance of a visual inspection system. In this work, a problem-specific illumination design is presented, which is motivated by optical feature extraction taking place during physical image acquisition. The approach is analyzed on the basis of a physically based camera and illumination model and is experimentally evaluated in different application scenarios.
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