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Anomaly detection is a general theory of detecting unusual patterns or events in data. This master thesis investigates the subject of anomaly detection in two different applications. The first application is product inspection using a camera and the second application is surveillance using a 2D laser scanner. The first part of the thesis presents a system for automatic visual defect inspection. The system is based on aligning the images of the product to a common template and doing pixel-wise comparisons. The system is trained using only images of products that are defined as normal, i.e. non-defective products. The visual properties of the inspected products are modelled using three different methods. The performance of the system and the different methods have been evaluated on four different datasets. The second part of the thesis presents a surveillance system based on a single laser range scanner. The system is able to detect certain anomalous events based on the time, position and velocities of individual objects in the scene. The practical usefulness of the system is made plausible by a qualitative evaluation using unlabelled data.

The diploma thesis deals with the problematics of machine vision. The research is devoted to description of the machine vision system hardware and software components. In practical part is described design and realization of the software, which allows several tasks of machine vision to be performed at the same time.

The aim of this master's thesis thesis is to enable evaluation of quality, or the type of product in industrial applications using artificial neural networks, especially in applications where the classical approach of machine vision is too complicated. The system thus designed is implemented onto a specific hardware platform and becomes a subject to the final optimalisation for the hardware platform for the best performance of the system.