Abstract Modern computer systems consist of a large number of dynamic hardware and software components that interact according to some specific rules. Quantitative models of such systems are important for performance engineering because... more
Abstract Modern computer systems consist of a large number of dynamic hardware and software components that interact according to some specific rules. Quantitative models of such systems are important for performance engineering because they allow for an earlier prediction of the quality of service.
Abstract In this paper we propose a simple and effective way to integrate structural information in random forests for semantic image labelling. By structural information we refer to the inherently available, topological distribution of... more
Abstract In this paper we propose a simple and effective way to integrate structural information in random forests for semantic image labelling. By structural information we refer to the inherently available, topological distribution of object classes in a given image. Different object class labels will not be randomly distributed over an image but usually form coherently labelled regions.
Abstract In this work we introduce a novel solution to the semantic image labelling problem, ie the task of assigning semantic object class labels to individual pixels in a test image. Conventional methods are typically relying on random... more
Abstract In this work we introduce a novel solution to the semantic image labelling problem, ie the task of assigning semantic object class labels to individual pixels in a test image. Conventional methods are typically relying on random fields for modelling interactions between neighboring pixels and obtaining smooth labelling results using unary and pairwise cost functions.