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Image Processing for Volume Graphics and Analysis.
This course presents tools and techniques for processing volume data as part of a visualization framework. The conventional volume rendering pipeline has been effectively used to visualize volume data that is often considered a sampled density map. However, more and more people are looking at data that has noise, occluding surfaces, density fluctuations, limited resolution, etc. These factors require users to do more "processing". Advanced volume processing is what enables people to do 1) linear and nonlinear filtering, 2) interpolation, 3) reconstruction, 4) feature extraction, and 5) model fitting. In the first half of the course, we describe the problem as a pipeline from the reconstruction of the continuous model from the sampled data, through the application of transfer functions for shading and classification, to the transformation sampling and projection of the reconstructed values for visualization. The goal is to extract or locate structures hidden within the data. A tacit requirement is to do so without masking detail with unwanted artifacts. Thus, the emphasis will be on factors which affect final image quality.
In the second half of the course, presenters will examine advanced image processing methods such as multiscale analysis, segmentation techniques, and level set algorithms. These techniques are gaining acceptance among both researchers and practitioners to gain extensive understanding of structures inherently present in datasets. Also, the use of complex data, not easily represented as a density map, require the use of these sophisticated analysis techniques. Case-studies and applications will be presented to illustrate and demonstrate the techniques.