Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Classification by Multi-Predicate Local Binary Pattern Operators
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Fabric defect detection using modified local binary patterns
EURASIP Journal on Advances in Signal Processing
Attributes Reduction Applied to Leather Defects Classification
SIBGRAPI '10 Proceedings of the 2010 23rd SIBGRAPI Conference on Graphics, Patterns and Images
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Texture defect detection became one of the problems which has been paid much attention on by image processing scientists since late 90s. Since now many different methods have been proposed to analysis and classification textures. An approach which provides good features to classification is local binary patterns. In this paper an approach is proposed to detection porosity in stones by using the improved form of local binary patterns features. The proposed approach includes two stages. First of all, in train stage, by applying local binary pattern operator on absolutely porosity less images, the basic feature vector is calculated. After that, by image windowing and computing the non-similarity amount between these and basic vector, the porosityless threshold is computed. Finally, in test stage, by using the porosity-less threshold the porosities is detected on test images. In the result part, the accuracy rate of proposed approach is computed by applying on some captured images and compared with some previous methods. High detection rate, low time complexity, rotate invariant and noise insensitive are advantages of proposed approach. Also, the proposed approach can use for every case of defect detections or visual classification.