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M.K. Tsatsanis, G.B. Giannakis, "Object and Texture Classification Using Higher Order Statistics," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 7, pp. 733750, July, 1992.  
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@article{ 10.1109/34.142910, author = {M.K. Tsatsanis and G.B. Giannakis}, title = {Object and Texture Classification Using Higher Order Statistics}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {14}, number = {7}, issn = {01628828}, year = {1992}, pages = {733750}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.142910}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  Object and Texture Classification Using Higher Order Statistics IS  7 SN  01628828 SP733 EP750 EPD  733750 A1  M.K. Tsatsanis, A1  G.B. Giannakis, PY  1992 KW  texture detection; object scaling; pattern recognition; texture classification; noisy scene; energy detector; higher order statistics; matched filtering; minimum distance classifiers; object rotation; parameter estimates; filtering and prediction theory; parameter estimation; pattern recognition; statistical analysis VL  14 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
The problem of the detection and classification of deterministic objects and random textures in a noisy scene is discussed. An energy detector is developed in the cumulant domain by exploiting the noise insensitivity of higher order statistics. An efficient implementation of this detector is described, using matched filtering. Its performance is analyzed using asymptotic distributions in a binary hypothesistesting framework. The object and texture discriminant functions are minimum distance classifiers in the cumulant domain and can be efficiently implemented using a bank of matched filters. They are immune to additive Gaussian noise and insensitive to object shifts. Important extensions, which can handle object rotation and scaling, are also discussed. An alternative texture classifier is derived from a ML viewpoint and is statistically efficient at the expense of complexity. The application of these algorithms to the texturemodeling problem is indicated, and consistent parameter estimates are obtained.
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