Visualization of irregular datasets using kernel density estimation function

Abstract

Visualization of empirical data is an important part of knowledge acquirement process. Numerous visualization techniques are thus employed including surface and volume rendering. Usually algorithms of visualization require data to be organized in a regular manner. Unfortunately data accumulated empirically often does not exhibit any internal regularity e.g. due to varying spatial density of samples resulting from natural constraints. In order to cope with this problem several preprocess procedures have been developed including distance-like methods. In the paper are discussed problems connected with data preprocessing for visual data analysis, hybrid data visualization styles using advanced texturing as part of data presentation. For fast prototyping of 3D visual styles and computation of visual examples POVRay with newest version of ScPovPlot3D toolkit has been used.