BibTeX:

@article{IJIRSTV3I2003,
     title={Texture Classification Using Local Binary Pattern for Noise Images},
     author={Sureshkumar and Sachin Veerashetty},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={3},
     number={2},
     pages={1--3},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV3I2003.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

The beneficial characteristics of Local Binary Pattern can be conserved by computing simple approach COMPLETE LBP (CLBP). The proposed technique called Binary Rotation Invariance and Noise Tolerant texture classification is mainly based this CLBP approach. This BRINT not just exhibits better execution than various late cutting edge LBP variations under ordinary conditions, but also performs significantly and consistently better in presence of noise due to its high distinctiveness and robustness. It likewise assessed to some kind of clamor levels (pepper and salt commotion, Gaussian) in common texture image.

Feature extraction, Local Binary Pattern (LBP), Turn Invariance, Texture analysis, Texture descriptors