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A new method for recognizing 3D-textured surfaces is proposed. Textures are modeled with multiple histograms of micro-textons, instead of the more macroscopic textons used in earlier studies. The micro-textons are extracted with a recently proposed multiresolution local binary pattern operator. Our approach has many advantages compared to the earlier approaches and provides the leading performance in the classification of Columbia-Utrecht database (CUReT) textures imaged under different viewpoints and illumination directions. An approach for learning appearance models for view-based texture recognition using self-organization of feature distributions is also proposed.. It can be used for quickly selecting model histograms and rejecting outliers, thus providing an efficient tool for vision system training, even when the feature data has a large variability.