TEXTURE DISCRIMINATION BASED UPON AN ASSUMED STOCHASTIC TEXTURE MODEL.

J. W. Modestino; R. W. Fries; A. L. Vickers

doi:10.1109/cdc.1979.270141

TEXTURE DISCRIMINATION BASED UPON AN ASSUMED STOCHASTIC TEXTURE MODEL.

J. W. Modestino, R. W. Fries, A. L. Vickers

Electrical & Computer Engineering

Research output: Contribution to journal › Conference article › peer-review

2 Scopus citations

Abstract

A new approach to texture discrimination is described. This approach is based upon an assumed stochastic model for texture in imagery and is an approximation to the statistically optimum maximum likelihood classifier. The construction and properties of the stochastic texture model are described and a digital filtering implementation of the resulting maximum likelihood texture discriminant is provided. The efficacy of this approach is demonstrated through experimental results obtained with simulated texture data. A comparison is provided with more conventional texture discriminants under identical conditions. The implications to texture discrimination in real-world imagery are discussed.

Original language	English (US)
Pages (from-to)	79-84
Number of pages	6
Journal	Proceedings of the IEEE Conference on Decision and Control
Volume	1
DOIs	https://doi.org/10.1109/cdc.1979.270141
State	Published - Jan 1 1979
Event	Proc IEEE Conf Decis Control Incl Symp Adapt Processes 18th - Fort Lauderdale, FL, USA Duration: Dec 12 1979 → Dec 14 1979

ASJC Scopus subject areas

Control and Systems Engineering
Modeling and Simulation
Control and Optimization

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10.1109/cdc.1979.270141

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title = "TEXTURE DISCRIMINATION BASED UPON AN ASSUMED STOCHASTIC TEXTURE MODEL.",

abstract = "A new approach to texture discrimination is described. This approach is based upon an assumed stochastic model for texture in imagery and is an approximation to the statistically optimum maximum likelihood classifier. The construction and properties of the stochastic texture model are described and a digital filtering implementation of the resulting maximum likelihood texture discriminant is provided. The efficacy of this approach is demonstrated through experimental results obtained with simulated texture data. A comparison is provided with more conventional texture discriminants under identical conditions. The implications to texture discrimination in real-world imagery are discussed.",

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AU - Modestino, J. W.

AU - Fries, R. W.

AU - Vickers, A. L.

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N2 - A new approach to texture discrimination is described. This approach is based upon an assumed stochastic model for texture in imagery and is an approximation to the statistically optimum maximum likelihood classifier. The construction and properties of the stochastic texture model are described and a digital filtering implementation of the resulting maximum likelihood texture discriminant is provided. The efficacy of this approach is demonstrated through experimental results obtained with simulated texture data. A comparison is provided with more conventional texture discriminants under identical conditions. The implications to texture discrimination in real-world imagery are discussed.

AB - A new approach to texture discrimination is described. This approach is based upon an assumed stochastic model for texture in imagery and is an approximation to the statistically optimum maximum likelihood classifier. The construction and properties of the stochastic texture model are described and a digital filtering implementation of the resulting maximum likelihood texture discriminant is provided. The efficacy of this approach is demonstrated through experimental results obtained with simulated texture data. A comparison is provided with more conventional texture discriminants under identical conditions. The implications to texture discrimination in real-world imagery are discussed.

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