Reinhard Kneser

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Tn stochastic language modeling, backing-off is a widely used method to cope with the sparse data problem. In case of unseen events this method backs off to a less spe­ cific distribution. In this paper we propose to use dis­ tributions which are especially optimized for the task of backing-off. Two different theoretical derivations lead to distributions(More)
A new method is presented to quickly adapt a given language model to local text characteristics. The basic approach is to choose the adaptive models as close as possible to the background estimates while constraining them to respect the locally estimated unigram probabilities. Several means are investigated to speed up the calculations. We measure both(More)
For many orthopaedic, neurological, and oncological applications, an exact segmentation of the vertebral column including an identification of each vertebra is essential. However, although bony structures show high contrast in CT images, the segmentation and labelling of individual vertebrae is challenging. In this paper, we present a comprehensive solution(More)
Segmentation of medical images can be achieved with the help of model-based algorithms. Reliable boundary detection is a crucial component to obtain robust and accurate segmentation results and to enable full automation. This is especially important if the anatomy being segmented is too variable to initialize a mean shape model such that all surface regions(More)
Recently, new techniques for minimally invasive aortic valve implantation have been developed generating a need for planning tools that assess valve anatomy and guidance tools that support implantation under x-ray guidance. Extracting the aortic valve anatomy from CT images is essential for such tools and we present a model-based method for that purpose. In(More)