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Texture Discrimination Based Upon an Assumed Stochastic Texture Model
May 1981 (vol. 3 no. 5)
pp. 557-580
James W. Modestino, SENIOR MEMBER, IEEE, Department of Electrical and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY 12181.
Robert W. Fries, MEMBER, IEEE, Department of Electrical and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY 12181; PAR Technology Corporation, Rome, NY 13440.
Acie L. Vickers, STUDENT MEMBER, IEEE, Department of Electrical and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY 12181.
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 realworld imagery are discussed.
Citation:
James W. Modestino, Robert W. Fries, Acie L. Vickers, "Texture Discrimination Based Upon an Assumed Stochastic Texture Model," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 3, no. 5, pp. 557-580, May 1981, doi:10.1109/TPAMI.1981.4767148
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