Florian Morr

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We present a novel approach for analyzing two-dimensional (2D) flow field data based on the idea of invariant moments. Moment invariants have traditionally been used in computer vision applications, and we have adapted them for the purpose of interactive exploration of flow field data. The new class of moment invariants we have developed allows us to(More)
Flow vector fields contain a wealth of information that needs to be visualized. As an extension of the well-known streamline technique, we have developed a context-based method for visualizing steady flow vector fields in two and three dimensions. We call our method "Priority Streamlines". In our approach, the density of the streamlines is controlled by a(More)
Automatic classiication of transmission electron-microscopy images is an important step in the complex task of determining the structure of biologial macro-molecules. The process of 3D reconstruction from a set of such images implies their previous classiication into homogeneous image classes. In general, diierent classes may represent either distinct(More)
Motivated by RNA secondary-structure folding-landscapes based on computer algorithms we construct a mathematical framework which provides a comprehensive and coherent description of evolutionary dynamics. In this paper we study the evolutionary dynamics of a population of self-replicating RNA molecules with autocat-alytic interactions (hypercycles and(More)
In this paper we present a new procedure for the design and optimization of a kind of artiicial neural networks (ANN) based on genetic algorithms (GAs). The method proposed here, called g-lvq , uses a genetic algorithm with variable-length genome and a vectorial tness to optimize Learning Vector Quantization (lvq) neural networks. The procedure optimizes(More)
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