Bogdan Vrusias

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Statistical pattern recognition techniques, supervised and unsupervised classification techniques being two good examples here, rely on the computations of similarity and distance metrics. The distances are computed in a multi-dimensional space. The axes of this space in principle relate to the features inherent in the input data. Usually such features are(More)
In general terms the evaluation of a summary depends on how close it is to the chief points in the source text. This begets the question as to what are the chief points in the source text and how is this information used in itself in identifying the source text. This is crucially important when we discuss automatic evaluation of summaries. So the question(More)
This paper deals with the problem of semantic transcoding of CCTV video footage. A framework is proposed that combines Computer Vision algorithms that extract visual semantics, together with Natural Language Processing that automatically builds the domain ontology from unstructured text annotations. The final aim is a system that will link the visual and(More)
A modular neural network-based system is presented where the component networks learn together to classify a set of complex input patterns. Each pattern comprises two vectors: a primary vector and a collateral vector. Examples of such patterns include annotated images and magnitudes with articulated numerical labels. Our modular system is trained using an(More)
This paper discusses the adaptation of the Scene of Crime Information System developed within an EPSRC-funded project, to the collection of data within the ImageCLEF track of the Cross Language Evaluation Forum 2003. The adaptations necessary to participate in this activity are detailed, and initial results are briefly presented. ImageCLEF is concerned with(More)