Exploring Large Digital Library Collections Using a Map-Based Visualisation

In this paper we describe a novel approach for exploring large document collections using a map-based visualisation. We use hierarchically structured semantic concepts that are attached to the documents to create a visualisation of the semantic space that resembles a Google Map. The approach is novel in that we exploit the hierarchical structure to enable the approach to scale to large document collections and to create a map where the higher levels of spatial abstraction have semantic meaning. An informal evaluation is carried out to gather subjective feedback from users. Overall results are positive with users finding the visualisation enticing and easy to use

Некоторые вопросы моделирования центробежных насосов

This work presents a novel object tracking approach, where the motion model is learned from sets of frame-wise detections with unknown associations. We employ a higher-order Markov model on position space instead of a first-order Markov model on a high-dimensional state-space of object dynamics. Compared to the latter, our approach allows the use of marginal rather than joint distributions, which results in a significant reduction of computation complexity. Densities are represented using a grid-based approach, where the rectangular windows are replaced with estimated smooth Parzen windows sampled at the grid points. This method performs as accurately as particle filter methods with the additional advantage that the prediction and update steps can be learned from empirical data. Our method is compared against standard techniques on image sequences obtained from an RC car following scenario. We show that our approach performs best in most of the sequences. Other potential applications are surveillance from cheap or uncalibrated cameras and image sequence analysis.DIPLEC