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A Rotation Invariant Pattern Operator for Texture Characterization


R Suguna, P. Anandhakumar


Vol. 10  No. 4  pp. 120-129


This paper proposes a novel Ternary Pattern Operator for texture characterization. The devised operator has rotation-invariant capability. The operator is used for extracting texture features from the images. From these features the texture present in the image are identified. The operator can be realized with few operations in local neighborhood and hence it is computationally simple. In the training phase the classifier is trained with samples with one particular angle and tested with samples of different angles. A detailed analysis is presented for rotation angles of misclassified samples. A distribution based classification approach is used for discriminating the textures. A probabilistic model is built for each texture class. A classification problem of different Brodatz texture and seven rotation angles is used in experiments. Experimental results prove that the performance of the proposed operator for feature extraction is appreciable.


Local Ternary Pattern Operator, Texture Characterization, Non parametric classification, Brodatz texture