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Institute
Line integral convolution (LIC) has become a well-known and popular method for visualizing vector fields. The method works by convolving a random input texture along the integral curves of the vector field. In order to accelerate image synthesis significantly, an efficient algorithm has been proposed that utilizes pixel coherence in field line direction. This algorithm, called ``fast LIC'', originally was restricted to simple box-type filter kernels. Here we describe a generalization of fast LIC for piecewise polynomial filter kernels. Expanding the filter kernels in terms of truncated power functions allows us to exploit a certain convolution theorem. The convolution integral is expressed as a linear combination of repeated integrals (or repeated sums in the discrete case). Compared to the original algorithm the additional expense for using higher order filter kernels, e.g.\ of B-spline type, is very low. Such filter kernels produce smoother, less noisier results than a box filter. This is evident from visual investigation, as well as from analysis of pixel correlations. Thus, our method represents a useful extension of the fast LIC algorithm for the creation of high-quality LIC images.
Polygonale Schädelmodelle bilden ein wichtiges Hilfsmittel für computergestützte Planungen im Bereich der plastischen Chirurgie. Wir beschreiben, wie derartige Modelle automatisch aus hochaufgelösten CT-Datensätzen erzeugt werden können. Durch einen lokal steuerbaren Simplifizierungsalgorithmus werden die Modelle so weit vereinfacht, daß auch auf kleineren Graphikcomputern interaktives Arbeiten möglich wird. Die Verwendung eines speziellen Transparenzmodells ermöglicht den ungehinderten Blick auf die bei der Planung relevanten Knochenstrukturen und läßt den Benutzer zugleich die Kopfumrisse des Patienten erkennen.
In this thesis we develop new methods for visualizing vector fields which specifically address three design goals: accuracy, performance, and cognition. Our methods will be general-purpose and can be applied to arbitrary vector fields in two- and three-dimensional space. The methodology behind our approach is {\em texture-based visualization}. Texture-based visualization methods imitate techniques known from experimental flow visualization, namely, the observation of randomly dispersed particles or dye injection patterns. Instead of depicting individual lines or symbols, a contiguous high-resolution image or texture is generated. This texture clearly reveals the directional structure of the field. In this way intuitive insight can be obtained and even small details of the field become visible. However, it remains unclear what kind of textures are best suited for our purpose. How do we generate them? Can we apply these methods in three-dimensional space? In particular, we focus on a technique known as {\em line integral convolution} or LIC. This method turns out to be quite versatile and well-suited for visualizing many interesting vector fields. LIC images display the integral curves or field lines of a vector field at high spatial resolution. Although conceptually quite simple, line integral convolution implies a number of interesting mathematical and algorithmic questions.