G. Klinker We Need Interactive Data Interpretation Rather Than interactive Data Visualization Position Paper for the Workshop on Scientific Visualization Environments, IEEE Visualization'91, Okt. 1991. (bib)
	General-purpose visualization systems, such as AVS, have recently received a lot of attention in the visualization community. However, many application programmers still prefer writing their own visualization code over using a standard visualization system. Why? What features are missing from current systems that prevent real applications from using them? In this position paper, we emphasize that certain applications areas, such as robotics or medical imaging need an interactive data interpretation component in addtion to straightforward data visualization. We use the term "interactive data interpretation" to describe the process of generating models from data measurements which are often provided in multi-dimensional, multi-modal arrays. The process is sometimes also referred to as "inverse modeling". Typical examples of data interpretation would be determining the outline of an object in a two-dimensional image or fitting a surface model to a three-dimensional volume. In either case, the process could be performed completely automatically (the typical case in computer vision) or partially interactively by the user, as done by Carlbom et al to outline a particular type of cell in electromicroscope data. We present a list of basic features that need to be included in today's visualization systems to make them useful for interactive data interpretation. We draw from our experience of having implemented a toolkit, VDI, for visualizaing computer vision applications. We are currently in the process of transferring the major concepts of VDI into AVS. We expect such extensions to AVS to be useful in many application areas, such as computer vision, medical imaging and seismic data analysis.
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