Generation of Transfer Functions with Stochastic Search Techniques

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.

• Type: Conference
• Year: 1996
• Where: VISUALIZATION
• Authors: Taosong He, Lichan Hong, Arie E. Kaufman, Hanspeter Pfister
• Keywords: Appropriate Transfer Functions, Transfer Functions, User-specified Objective Functions, Visualization, VISUALIZATION 1996