Liu, L. ., Lao, S. ., Fieguth, P. W., Guo, Y. ., Wang, X. ., & Pietikäinen, M. . (2016). Median robust extended local binary pattern for texture classification. IEEE Transactions on Image Processing, 25, 1368-1381.
Reference author: Li Liu
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Li
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Liu
Liu, L. ., Wang, L. ., Zhao, L. ., & Fieguth, P. . (2016). Random projections and Single BoW for fast and Robust texture segmentation. Information Sciences, 370, 428-445.
Liu, L. ., Long, Y. ., Fieguth, P. W., Lao, S. ., & Zhao, G. . (2014). BRINT: binary rotation invariant and noise tolerant texture classification. IEEE Transactions on Image Processing, 23, 3071-3084.
Liu, L. ., Fieguth, P. ., Guo, Y. ., Wang, X. ., & Pietikäinen, M. . (2017). Local binary features for texture classification: Taxonomy and experimental study. Pattern Recognition, 62, 135-160.
Liu, L. ., Fieguth, P. ., Wang, X. ., Pietikäinen, M. ., & Hu, D. . (2016). Evaluation of LBP and deep texture descriptors with a new robustness benchmark. European Conference on Computer Vision, 69-86. Springer, Cham.
Liu, L. ., Fieguth, P. ., Zhao, G. ., Pietikäinen, M. ., & Hu, D. . (2016). Extended local binary patterns for face recognition. Information Sciences, 358, 56-72.
Liu, L. ., Ouyang, W. ., Wang, X. ., Fieguth, P. ., Chen, J. ., Liu, X. ., & Pietikäinen, M. . (2020). Deep learning for generic object detection: A survey. International Journal of Computer Vision, 128, 261-318.
Abdar, M. ., Pourpanah, F. ., Hussain, S. ., Rezazadegan, D. ., Liu, L. ., Ghavamzadeh, M. ., Fieguth, P. ., Khosravi, A. ., Acharya, R. ., Makarenkov, V. ., & . (2020). A review of uncertainty quantification in deep learning: Techniques, applications and challenges. ArXiv Preprint arXiv:2011.06225.
Liu, L. ., Chen, J. ., Fieguth, P. ., Zhao, G. ., Chellappa, R. ., & Pietikäinen, M. . (2019). From BoW to CNN: Two decades of texture representation for texture classification. International Journal of Computer Vision, 127, 74-109.
Liu, L. ., Chen, J. ., Zhao, G. ., Fieguth, P. ., Chen, X. ., & Pietikäinen, M. . (2019). Texture classification in extreme scale variations using GANet. IEEE Transactions on Image Processing, 28, 3910-3922.
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