VISUALIZING FEATURES AND TRACKING THEIR EVOLUTION

Scientific visualization aims to devise algorithms and methods that transform massive scientific data sets into pictures and other graphic representations that facilitate comprehension and interpretation. Scientists want to study the essential dynamics of objects and their evolution; they want to describe them for controlled time periods, thus obtaining partial, and therefore simpler, mathematical solutions to the original problem. Unfortunately, even this is a daunting task because the amount of data generated is overwhelming. This article describes basic algorithms to extract coherent amorphous regions (features or objects) from two- and three-dimensional scalar and vector fields and then track them in a series of consecutive time steps. The authors use a combination of techniques from computer vision, image processing, computer graphics, and computational geometry and apply them to data sets from computational fluid dynamics. These techniques can reduce ''visual clutter'' and provide the first step to quantifying observable phenomena.