BRINT: Binary Rotation Invariant and Noise Tolerant Texture Classification

@article{Liu2014BRINTBR,
  title={BRINT: Binary Rotation Invariant and Noise Tolerant Texture Classification},
  author={Li Liu and Yunli Long and Paul W. Fieguth and Songyang Lao and Guoying Zhao},
  journal={IEEE Transactions on Image Processing},
  year={2014},
  volume={23},
  pages={3071-3084}
}
In this paper, we propose a simple, efficient, yet robust multiresolution approach to texture classification-binary rotation invariant and noise tolerant (BRINT). The proposed approach is very fast to build, very compact while remaining robust to illumination variations, rotation changes, and noise. We develop a novel and simple strategy to compute a local binary descriptor based on the conventional local binary pattern (LBP) approach, preserving the advantageous characteristics of uniform LBP… CONTINUE READING
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Naghsh-Nilchi, “Noise tolerant local binary pattern operator for efficient texture analysis

  • A.R.A. Fathi
  • Pattern Recognit. Letters,
  • 2012
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