Models for Stochastic Texture Generation

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Publication Type technical report
School or College College of Engineering
Department Computing, School of
Creator Angerhofer, Norman Rae
Title Models for Stochastic Texture Generation
Date 1985-12
Description This work explores current models of textures for image synthesis and analysis, with an emphasis on generation of stochastic textures. Digital signal processing techniques are applied to create a general and robust model for the generation of stochastic textures. Martingale sequences are analyzed, and it is determined that the best way to develop martingale sequences is through the use of convolution. Methods of convolution with white noise are discussed. A number of ways to obtain the point spread function or texture filter for a target texture are developed. Important ways include the use of preexisting texture samples and the construction of ensemble averages. The theoretical framework behind the robustness of these methods is developed. Stochastic textures which have been generated with these methods are then used in the rendering process to give greater realism to objects in computer-generated images.
Type Text
Subject computer graphics; models of texture; image synthesis; image analysis; stochastic textures
Language eng
Bibliographic Citation Angerhofer, N. R. (1985). Models for stochastic texture generation.
Series University of Utah Computer Science Technical Report
Relation is Part of ARPANET
Format Medium application/pdf
Format Extent 41,252,641 bytes
File Name Angerhofer-Models_For.pdf
Conversion Specifications Original scanned with Kirtas 2400 and saved as 400 ppi uncompressed TIFF. PDF generated by Adobe Acrobat Pro X for CONTENTdm display
ARK ark:/87278/s6rz1d8x
Setname ir_computersa
Date Created 2016-04-27
Date Modified 2016-05-17
ID 96559
Reference URL https://collections.lib.utah.edu/ark:/87278/s6rz1d8x
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