@InProceedings{8902765,
  author = {S. R. Barburiceanu and S. Meza and C. Germain and R. Terebes},
  booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},
  title = {An Improved Feature Extraction Method for Texture Classification with Increased Noise Robustness},
  year = {2019},
  pages = {1-5},
  abstract = {This paper presents an improved feature extraction method based on the use of state-of-the art filtering techniques and Local Binary Patterns-derived feature descriptors with applications in texture classification. The method is adaptive, being capable to determine the type of noise present in the input image and to apply the appropriate operator for the filtering step of the feature extraction technique. The improved approaches, labelled BM3DELBP (Block Matching and 3D filtering Extended Local Binary Pattern) and SARBM3DELBP (Synthetic Aperture Radar Block Matching and 3D filtering Extended Local Binary Pattern) bring significant improvements both in terms of robustness to Gaussian and speckle noise and in terms of classification accuracy, being invariant to different image transformations. We tested our approach both on synthetic textures from two standard Outex databases and on real polarimetric Synthetic Aperture Radar (SAR) images of pine forests. On all considered databases, the proposed approach proved to be above state-of-the-art LBP variants in terms of classification accuracy, even in the presence of high Gaussian and speckle noise levels.},
  keywords = {feature extraction;filtering theory;image classification;image denoising;image matching;image representation;image texture;radar computing;radar imaging;speckle;synthetic aperture radar;improved feature extraction method;texture classification;increased noise robustness;state-of-the art filtering techniques;Local Binary Patterns-derived feature descriptors;filtering step;feature extraction technique;improved approaches;labelled BM3DELBP;Extended Local Binary Pattern;SARBM3DELBP;Synthetic Aperture Radar Block Matching;classification accuracy;synthetic textures;polarimetric Synthetic Aperture Radar images;speckle noise levels;Feature extraction;Filtering;Three-dimensional displays;Histograms;Speckle;Training;Lighting;texture classification;Local Binary Patterns;Block Matching;3D filtering;noise robustness;feature extraction;SAR images;speckle noise;Gaussian noise},
  doi = {10.23919/EUSIPCO.2019.8902765},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570529541.pdf},
}

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R.","Meza, S.","Germain, C.","Terebes, R."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["S.","R."],"propositions":[],"lastnames":["Barburiceanu"],"suffixes":[]},{"firstnames":["S."],"propositions":[],"lastnames":["Meza"],"suffixes":[]},{"firstnames":["C."],"propositions":[],"lastnames":["Germain"],"suffixes":[]},{"firstnames":["R."],"propositions":[],"lastnames":["Terebes"],"suffixes":[]}],"booktitle":"2019 27th European Signal Processing Conference (EUSIPCO)","title":"An Improved Feature Extraction Method for Texture Classification with Increased Noise Robustness","year":"2019","pages":"1-5","abstract":"This paper presents an improved feature extraction method based on the use of state-of-the art filtering techniques and Local Binary Patterns-derived feature descriptors with applications in texture classification. The method is adaptive, being capable to determine the type of noise present in the input image and to apply the appropriate operator for the filtering step of the feature extraction technique. The improved approaches, labelled BM3DELBP (Block Matching and 3D filtering Extended Local Binary Pattern) and SARBM3DELBP (Synthetic Aperture Radar Block Matching and 3D filtering Extended Local Binary Pattern) bring significant improvements both in terms of robustness to Gaussian and speckle noise and in terms of classification accuracy, being invariant to different image transformations. We tested our approach both on synthetic textures from two standard Outex databases and on real polarimetric Synthetic Aperture Radar (SAR) images of pine forests. 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