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Mapping textures on 3d terrains: a hybrid cellular automata approach

It is a time consuming task to generate textures for large 3D terrain surfaces in
computer games, flight simulations and computer animations. This work explores the
use of cellular automata in the automatic generation of textures for large surfaces. I
propose a method for generating textures for 3D terrains using various approaches - in
particular, a hybrid approach that integrates the concepts of cellular automata,
probabilistic distribution according to height and Wang tiles. I also look at other hybrid
combinations using cellular automata to generate textures for 3D terrains. Work for this
thesis includes development of a tool called "Texullar" that allows users to generate
textures for 3D terrain surfaces by configuring various input parameters and choosing
cellular automata rules.
I evaluate the effectiveness of the approach by conducting a user survey to
compare the results obtained by using different inputs and analyzing the results. The
findings show that incorporating concepts of cellular automata in texture generation for
terrains can lead to better results than random generation of textures. The analysis also
reveals that incorporating height information along with cellular automata yields better
results than using cellular automata alone. Results from the user survey indicate that a hybrid approach incorporating height information along with cellular automata and
Wang tiles is better than incorporating height information along with cellular automata
in the context of texture generation for 3D meshes.
The survey did not yield enough evidence to suggest whether the use of Wang
tiles in combination with cellular automata and probabilistic distribution according to
height results in a higher mean score than the use of only cellular automata and
probabilistic distribution. However, this outcome could have been influenced by the fact
that the survey respondents did not have information about the parameters used to
generate the final image - such as probabilistic distributions, the population
configurations and rules of the cellular automata.

Identiferoai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/4774
Date25 April 2007
CreatorsSinvhal, Swapnil
ContributorsKeyser, John
PublisherTexas A&M University
Source SetsTexas A and M University
Languageen_US
Detected LanguageEnglish
TypeBook, Thesis, Electronic Thesis, text
Format9877504 bytes, 2946974 bytes, 13277184 bytes, electronic, application/vnd.ms-powerpoint, application/pdf, application/vnd.ms-powerpoint, born digital

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