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Extended Gaussian-Filtered Local Binary Patterns for Colonoscopy Image Classification

Siyamalan Manivannan, Ruixuan Wang, Emanuele Trucco
ICCVW 2013 pp. 184-189
doi:10.1109/ICCVW.2013.31 /iccvw/2013/manivannan2013iccvw-extended/

Abstract

Local Binary Patterns (LBP) and its variants are widely used for texture classification. In this paper we propose a new variant of LBP descriptor called the extended Gaussian filtered Local Binary Patterns (GF-LBP) which is robust to illumination changes, noise and captures more informative edge-like features for classification. Experiments on a colonoscopy image dataset with 2100 images for binary (`normal' or `abnormal') classification show that the proposed xGF-LBP descriptor significantly outperforms the standard LBP descriptor and its considered variants.

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Cite

Text

Manivannan et al. "Extended Gaussian-Filtered Local Binary Patterns for Colonoscopy Image Classification." IEEE/CVF International Conference on Computer Vision Workshops, 2013. doi:10.1109/ICCVW.2013.31

Markdown

[Manivannan et al. "Extended Gaussian-Filtered Local Binary Patterns for Colonoscopy Image Classification." IEEE/CVF International Conference on Computer Vision Workshops, 2013.](https://mlanthology.org/iccvw/2013/manivannan2013iccvw-extended/) doi:10.1109/ICCVW.2013.31

BibTeX

@inproceedings{manivannan2013iccvw-extended,
  title     = {{Extended Gaussian-Filtered Local Binary Patterns for Colonoscopy Image Classification}},
  author    = {Manivannan, Siyamalan and Wang, Ruixuan and Trucco, Emanuele},
  booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
  year      = {2013},
  pages     = {184-189},
  doi       = {10.1109/ICCVW.2013.31},
  url       = {https://mlanthology.org/iccvw/2013/manivannan2013iccvw-extended/}
}