Local binary pattern (LBP) is a multi-applicable texture descriptor applied in machine vision. Despite its outstanding abilities in revealing textural properties of image, it is sensitive to noise, due to its thresholding mechanism. To make LBP robust against noise, a directional thresholded LBP (DTLBP) is developed in this article which applies the directional neighboring pixels average values for thresholding. Applying this type of thresholding in addition to reducing noise, due to using the information of neighboring pixels with bigger radii, increases efficiency in extracting features. The DTLBP is able to be combined with other descriptors like completed LBP (CLBP) and local ternary pattern (LTP) which improves their functionality against noise. To evaluate the functionality of DTLBP, four known datasets including Outex (TC10), CUReT, UIUC and UMD are tested. Numerous and extensive experiments on these datasets with different kinds of noises indicate this newly developed descriptor’s efficiency, with or without incremental white Gaussian and Gaussian blur noises. The proposed descriptor is compared with its available state of the art counterparts. The results show that the combination of DTLBP with CLBP descriptors provide the best classification accuracy in the experiments, which confirms the efficiency and robustness of the proposed descriptor when extracting features from noisy and raw images.

中文翻译：

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通过定向阈值局部二值模式提取抗噪纹理特征

局部二值模式（LBP）是一种应用于机器视觉的多用途纹理描述符。尽管它在揭示图像的纹理特性方面具有出色的能力，但由于其阈值机制，它对噪声很敏感。为了使 LBP 对噪声具有鲁棒性，本文开发了一种方向阈值 LBP (DTLBP)，它应用方向相邻像素的平均值进行阈值处理。除了降低噪声之外，应用这种类型的阈值处理，由于使用了具有更大半径的相邻像素的信息，提高了提取特征的效率。DTLBP 能够与其他描述符相结合，如已完成的 LBP (CLBP) 和局部三元模式 (LTP)，从而提高了它们的抗噪声功能。为了评估 DTLBP 的功能，四个已知数据集包括 Outex (TC10)、CUReT、UIUC和UMD经过测试。在这些具有不同类型噪声的数据集上进行的大量大量实验表明，无论是否有增量高斯白噪声和高斯模糊噪声，这个新开发的描述符的效率。将提议的描述符与其可用的现有技术对应物进行比较。结果表明，DTLBP 与 CLBP 描述符的组合在实验中提供了最佳的分类精度，这证实了所提出的描述符在从噪声和原始图像中提取特征时的效率和鲁棒性。将提议的描述符与其可用的现有技术对应物进行比较。结果表明，DTLBP 与 CLBP 描述符的组合在实验中提供了最佳的分类精度，这证实了所提出的描述符在从噪声和原始图像中提取特征时的效率和鲁棒性。将提议的描述符与其可用的现有技术对应物进行比较。结果表明，DTLBP 与 CLBP 描述符的组合在实验中提供了最佳的分类精度，这证实了所提出的描述符在从噪声和原始图像中提取特征时的效率和鲁棒性。