Image texture generation using autoregressive integrated moving average (ARIMA)--models
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Authors
Rathmanner, Steven Clifford
Subjects
Autoregressive Integrated Moving Average (ARIMA)
Stationary
Separable
Autoregressive Integrated moving average (ARIMA)
Stationary
Separable
Stationary
Separable
Autoregressive Integrated moving average (ARIMA)
Stationary
Separable
Advisors
Therrien, Charles W.
Date of Issue
1987-03
Date
March 1987
Publisher
Language
en_US
Abstract
This thesis involves investigation of linear filtering models as a means of generating texture in images. Various autoregressive filter models are used to generate various textures, and the results are analyzed to determine relationships between filter parameters and texture characteristics. A two-dimensional counterpart to the autoregressive integrated moving average (ARIMA) model from one-dimensional time series analysis theory is developed and tested for texture modeling applications. All these models are driven by white noise, and to the extent that real images can be reproduced this way, advantages in image texture transmission could be realized. Results of this work indicate that the purely autoregressive models work well for some types of image textures, but that for the textures studied the ARIMA model is not particularly suitable.
Type
Thesis
Description
Series/Report No
Department
Electrical and Computer Engineering
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
Funder
Format
131 p.
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.