By definition, nominal data cannot be ranked. However, there are circumstances where it is essential to rank nominal data. Examples of such ranking include ranking hospitals and colleges, defining the “most livable cities”, and conference paper submissions. In this project, we consider ranking patient severity. The purpose is to determine how patient severity can be used to rank the quality of ...

In this paper, a pixel-based background modeling method, which uses nonparametric kernel density estimation, is proposed. To reduce the burden of image storage, we modify the original KDE method by using the first frame to initialize it and update it subsequently at every frame by controlling the learning rate according to the situations. We apply an adaptive threshold method based on image cha...

Previous research has indicated the significance of accurate classification of fluorescence in situ hybridisation (FISH) signals for the detection of genetic abnormalities. Based on well-discriminating features and a trainable neural network (NN) classifier, a previous system enabled highly-accurate classification of valid signals and artefacts of two fluorophores. However, since this system em...

With the adoption of Nepomuk as an organic part of KDE the semantic desktop became a reality to a great number of users and is employed by a growing number of applications. Thus, the amount of semantic data is constantly growing on the desktop. Therefore users need a way to access this data outside of the limiting use cases of the applications employing Nepomuk-KDE. We aim to assist users in bu...

The Markov Chain Monte Carlo approach is frequently used within a Bayesian framework to sample the target posterior distribution. Its efficiency strongly depends on proposal distribution build chain. best jump one that closely resembles unknown distribution; therefore, we suggest an adaptive based kernel density estimation (KDE). We group model's parameters according their correlation and KDE a...

Real-world visual data is often corrupted and requires the use of estimation techniques that are robust to noise and outliers. Robust methods are well studied for Euclidean spaces and their use has also been extended to Riemannian spaces. In this chapter, we present the necessary mathematical constructs for Grassmann manifolds, followed by two different algorithms that can perform robust estima...