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C. Bouman, B. Liu, "Multiple Resolution Segmentation of Textured Images," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 2, pp. 99113, February, 1991.  
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@article{ 10.1109/34.67641, author = {C. Bouman and B. Liu}, title = {Multiple Resolution Segmentation of Textured Images}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {13}, number = {2}, issn = {01628828}, year = {1991}, pages = {99113}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.67641}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  Multiple Resolution Segmentation of Textured Images IS  2 SN  01628828 SP99 EP113 EPD  99113 A1  C. Bouman, A1  B. Liu, PY  1991 KW  pattern recognition; picture processing; parameter estimation; textured images; statistical behavior; causal Gaussian autoregressive model; mean; variance; spatial correlation; unsupervised texture segmentation; multiple resolution segmentation; coarse resolution; Markov random field; posteriori probability; deterministic greedy algorithm; classification; AIC information criterion; parameter estimation; pattern recognition; picture processing; probability; statistics VL  13 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
A multiple resolution algorithm is presented for segmenting images into regions with differing statistical behavior. In addition, an algorithm is developed for determining the number of statistically distinct regions in an image and estimating the parameters of those regions. Both algorithms use a causal Gaussian autoregressive model to describe the mean, variance, and spatial correlation of the image textures. Together, the algorithms can be used to perform unsupervised texture segmentation. The multiple resolution segmentation algorithm first segments images at coarse resolution and then progresses to finer resolutions until individual pixels are classified. This method results in accurate segmentations and requires significantly less computation than some previously known methods. The field containing the classification of each pixel in the image is modeled as a Markov random field. Segmentation at each resolution is then performed by maximizing the a posteriori probability of this field subject to the resolution constraint. At each resolution, the a posteriori probability is maximized by a deterministic greedy algorithm which iteratively chooses the classification of individual pixels or pixel blocks. The unsupervised parameter estimation algorithm determines both the number of textures and their parameters by minimizing a global criterion based on the AIC information criterion. Clusters corresponding to the individual textures are formed by alternately estimating the cluster parameters and repartitioning the data into those clusters. Concurrently, the number of distinct textures is estimated by combining clusters until a minimum of the criterion is reached.
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