Change detection is an ability that allows intelligent agents to react to unexpected situations. This mechanism is fundamental in providing more autonomy to robots. It has been used in many different fields including quality control and network intrusion. In the visual domain, however, most research has been confined to stationary cameras and only recently have researchers started to shift to mobile cameras. \ We propose a general framework for building internal spatial models of the visual experiences. These models are used to retrieve expectations about visual inputs which can be compared to the actual observation in order to identify the presence of changes. Our framework leverages the tolerance to small view changes of optic flow and color histogram representations and a self-organizing map to build a compact memory of camera observations. The effectiveness of the approach is demonstrated in a walking simulation, where spatial information and color histograms are combined to detect changes in a room. The location signal allows the algorithm to query the self-organizing map for the expected color histogram and compare it to the current input. Any deviations can be considered changes and are then localized on the input image. Furthermore, we show how detecting a vehicle entering or leaving the camera's lane can be reduced to a change detection problem. This simplifies the problem by removing the need to track or even know about other vehicles. Matching Pursuit is used to learn a compact dictionary to describe the observed experiences. Using this approach, changes are detected when the learned dictionary is unable to reconstruct the current input. The human experiments presented in this dissertation support the idea that humans build statistical models that evolve with experience. We provide evidence that not only does this experience improve people's behavior in 3D environments, but also enables them to detect chromatic changes. Mobile cameras are now part of our everyday lives, ranging from built-in laptop cameras to cell phone cameras. The vision of this research is to enable these devices with change detection mechanisms to solve a large class of problems. Beyond presenting a foundation that effectively detects changes in environments, we also show that the algorithms employed are computationally inexpensive. The practicality of this approach is demonstrated by a partial implementation of the algorithm on commodity hardware such as Android mobile devices. / text

Space weather is the field within the space sciences that studies how the Earths magnetosphere is influenced by the Sun. The Sun is constantly emitting dangerous radiation and plasma which in some cases can affect or damage the systems on Earth. Scientists have an interest in studying this interaction and therefore visualizations of space weather data are useful. OpenSpace is an interactive software that visualizes the entire known universe with real-time data. OpenSpace supports a range of different visualization methods and techniques, for this work, the relevant visualization tools are field lines and cut planes. GAMERA is a simulation model that simulates a wide range of situations where plasma is subjected to the influence of magnetic fields, the simulations are based on curvilinear grids. This project focuses on implementing data from GAMERA into OpenSpace. OpenSpace already supports a variety of different simulation models, although none that uses curvilinear grids for the data. The curvilinear grid can adapt to the specific shape and geometry of the data, allowing for more accurate data representation. The project aims to create a pipeline for reading data files from simulation runs and visualize it as field lines and cut planes. The files used in this project contain data suitable for volumes and field lines. The method was to first develop a reader to extract and manage desired data from HDF5 files in which the simulation data is stored. The data used to visualize field lines is rendered with an already existing component in OpenSpace. Secondly, a slice operation was developed to extract cut planes from the files containing data for volume visualization, these are then visualized with the help of a component for rendering cut planes which was developed during this work. The work led to a pipeline that reads and manages simulation data from GAMERA and the data is successfully visualized. However, there is room for improvement in color rendering, robustness and level of user interaction during runtime. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>