Konversatorium on Friday, October 20, 2006 - 10:00

In this talk we investigate the effects of function composition in the form g(f(x)) = h(x) by means of a spectral analysis of h. We decompose the spectral description of h(x) into a scalar product of the spectral description of g(x) and a term that solely depends on f(x) and that is independent of g(x). We then use the method of stationary phase to derive the essential maximum frequency of g(f(x)) bounding the main portion of the energy of its spectrum. This limit is the product of the maximum frequency of g(x) and the maximum derivative of f(x). This leads to a proper sampling of the composition h of the two functions g and f. We apply our theoretical results to a fundamental open problem in volume rendering -- the proper sampling of the rendering integral after the application of a transfer function.

Fully 3D datasets have become ubiquitous in a wide range of disciplines, such as science, engineering, medicine, and even entertainment. There is a vast demand to efficiently create these data, as well as fuse, relate, and visualize them. In this talk I will report on our efforts in all of these domains. First I will discuss techniques that utilize GPUs for rapid tomographic volume reconstruction and even direct volume visualization from X-ray projection data. Then I will describe our Magic Volume Lens framework which fuses and augments different types of volumetric data at different scales into one composite representation, providing a variety of zoom lenses for focus+context GPU-accelerated viewing with semantic context.