PREMI
2005
Springer
14 years 6 months ago
2005
Springer
This paper provides an overview of a number of techniques developed within our group to perform 3D reconstruction and image segmentation based of the application of energy minimisa...
PREMI
2005
Springer
14 years 6 months ago
2005
Springer
A hierarchical framework for document segmentation is proposed as an optimization problem. The model incorporates the dependencies between various levels of the hierarchy unlike tr...
PREMI
2005
Springer
14 years 6 months ago
2005
Springer
PREMI
2005
Springer
14 years 6 months ago
2005
Springer
Abstract. Technological advances in experimental and computational molecular biology have revolutionized the whole fields of biology and medicine. Large-scale sequencing, expressio...
PREMI
2005
Springer
14 years 6 months ago
2005
Springer
Frequent Pattern Mining (FPM) is a very powerful paradigm for mining informative and useful patterns in massive, complex datasets. In this paper we propose the Data Mining Template...
PREMI
2005
Springer
14 years 6 months ago
2005
Springer
Abstract. Segmentation of magnetic resonance imaging (MRI) data entails assigning tissue class labels to voxels. The primary source of segmentation error is the partial volume eff...
PREMI
2005
Springer
14 years 6 months ago
2005
Springer
Text mining techniques have been proposed for extracting protein names and their interactions. First, we have made improvements on existing methods for handling single word protein...
PREMI
2005
Springer
14 years 6 months ago
2005
Springer
The work proposes a hierarchical architecture for learning amic scenes at various levels of knowledge abstraction. The raw visual information is processed at different stages to g...
PREMI
2005
Springer
14 years 6 months ago
2005
Springer
The Semantic Web is not a single technology, but rather a collection of technologies designed to work together. As a result, research on the Semantic Web intends both to advance in...
PREMI
2005
Springer
14 years 6 months ago
2005
Springer
Abstract. Approximation spaces are fundamental for the rough set approach. We discuss their application in machine learning and pattern recognition.