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) |
|---|---|
| Title of host publication | Proceedings of the IEEE Conference on Decision and Control |
| Publisher | IEEE |
| Pages | 79-84 |
| Number of pages | 6 |
| Volume | 1 |
| State | Published - 1979 |
| Event | Proc IEEE Conf Decis Control Incl Symp Adapt Processes 18th - Fort Lauderdale, FL, USA Duration: Dec 12 1979 → Dec 14 1979 |
Other
| Other | Proc IEEE Conf Decis Control Incl Symp Adapt Processes 18th |
|---|---|
| City | Fort Lauderdale, FL, USA |
| Period | 12/12/79 → 12/14/79 |
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ASJC Scopus subject areas
- Chemical Health and Safety
- Control and Systems Engineering
- Safety, Risk, Reliability and Quality
Cite this
TEXTURE DISCRIMINATION BASED UPON AN ASSUMED STOCHASTIC TEXTURE MODEL. / Modestino, J. W.; Fries, R. W.; Vickers, A. L.
Proceedings of the IEEE Conference on Decision and Control. Vol. 1 IEEE, 1979. p. 79-84.Research output: Chapter in Book/Report/Conference proceeding › Chapter
}
TY - CHAP
T1 - TEXTURE DISCRIMINATION BASED UPON AN ASSUMED STOCHASTIC TEXTURE MODEL.
AU - Modestino, J. W.
AU - Fries, R. W.
AU - Vickers, A. L.
PY - 1979
Y1 - 1979
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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UR - http://www.scopus.com/inward/citedby.url?scp=0018698969&partnerID=8YFLogxK
M3 - Chapter
AN - SCOPUS:0018698969
VL - 1
SP - 79
EP - 84
BT - Proceedings of the IEEE Conference on Decision and Control
PB - IEEE
ER -