Generation Of Transfer Functions with Stochastic Search Techniques

Generation Of Transfer Functions with Stochastic Search Techniques

T. He, L. Hong, A. Kaufman, and H. Pfister.

(IEEE: Proceedings of the 7th conference on Visualization, 1996.)

This paper presents a novel approach to assist the user in exploring appropriate transfer functions for the visualization of volumetric datasets. The search for a transfer function is treated as a parameter optimization problem and addressed with stochastic search techniques. Starting from an initial population of (random or pre-defined) transfer functions, the evolution of the stochastic algorithms is controlled by either direct user selection of intermediate images or automatic fitness evaluation using user-specified objective functions. This approach essentially shields the user from the complex and tedious trial and error" approach, and demonstrates effective and convenient generation of transfer functions.