Generation of Transfer Functions with Stochastic Search Techniques

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Generation of Transfer Functions with Stochastic Search Techniques

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Title: Generation of Transfer Functions with Stochastic Search Techniques
Author: He, Taosong; Hong, Lichan; Kaufman, Arie; Pfister, Hanspeter

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Citation: He, Taosong, Lichan Hong, Arie Kaufman, and Hanspeter Pfister. 1996. Generation of transfer functions with stochastic search techniques. In Proceedings of the 7th conference on Visualization: October 28-29, 1996, San Francisco, California, ed. R. Yagel, and G. M. Nielson, 227-234. Los Alamitos, C.A.: IEEE Computer Society Press.
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Abstract: 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.
Published Version: http://portal.acm.org/citation.cfm?id=244979.245572
Other Sources: http://gvi.seas.harvard.edu/sites/all/files/VIS96_0.pdf
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:4141475
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