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A 3D texture classification method based on support vector machines (SVM) and histogram model is proposed in this paper. We compute the bidirectional histogram of Lambertian, isotropic, randomly rough surfaces which are common in real-world scenes firstly, and final classification results are gained with SVM. Performance is evaluated by employing 600 texture images corresponding to 61 real-world surface samples extracted from the Columbia-Utrecht reflectance and texture (CUReT) database. Our experiments produce good classification results.
Date of Conference: Aug. 16 2007-July 18 2007