Foreground-background segmentation in videos is an important low-level task needed for many different applications in computer vision. Therefore, a great variety of different algorithms have been proposed to deal with this problem, however none can deliver satisfactory results in all circumstances. Our approach combines an efficent novel Background Substraction algorithm with a higher order Markov Random Field (MRF) which can model the spatial relations between the pixels of an image far better than a simple pairwise MRF used in most of the state of the art methods. Afterwards, a runtime optimized Belief Propagation algorithm is used to compute an enhanced segmentation based on this model. Lastly, a local between Class Variance method is combined with this to enrich the data from the Background Substraction. To evaluate the results the difficult Wallflower data set is used.
Computational simulation is an established method to gain insight into cellular processes. As the resulting data sets are usually large and complex, visualization can play a significant role in data analysis. In this paper, we focus on the visualization of simulation output from the next sub-volume method, a spatial simulation algorithm. In addition to the spatial context of the simulation output, its heterogeneous data types, multiple variables, and the temporal context make high demands on the visualization. To cope with these challenging characteristics, we systematically explore possible visualization concepts with respect to these characteristics. From these findings, we derive our specific solution to visualize the data from the next sub-volume method, using a framework of multiple coordinated views that emphasize the spatial context of the data. Combining these views with a highly interactive user interface, the user is able to adapt the visualization to his current analysis goals and explore the data in its complexity
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