ID	原文	译文
22095	针对传统局部二值模式(LBP)的特征鉴别力有限和噪声敏感性问题，该文提出一种基于金字塔分解和扇形局部均值二值模式的纹理特征提取方法。	The traditional Local Binary Pattern (LBP) has limited feature discrimination and is sensitive to the noise. In order to alleviate these problems, this paper proposes a method to extract texture features based on pyramid decomposition and sectored local mean binary pattern.
22096	首先，将原始图像进行金字塔分解，得到对应于不同分解级别的低频和高频(差分)图像。	First, the pyramid decomposition is performed on the original image to obtain low-frequency and high-frequency (difference) images with different decomposition levels.
22097	为提取兼具鉴别力和稳健性的特征，进一步采用阈值化处理技术将高频图像转化为正、负高频图。	To extract robust yet discriminative features, thresholding technique is further used to transform the high-frequency images into positive and negative high-frequency images.
22098	然后，基于局部均值操作提出一种扇形局部均值二值模式(SLMBP)，用于计算各级分解图像的纹理特征码。	Then, based on local averaging operations, Sectored Local Mean Binary Pattern (SLMBP) is proposed and used to compute texture feature codes at different decomposition levels.
22099	最后，对纹理特征码进行跨频带的联合编码和跨级别的直方图加权，从而获得最终的纹理特征。	Finally, the texture features are obtained by joint coding across frequency bands and by histogram weighting across decomposition levels.
22100	在公开的 3 个纹理数据库(Outex, Brodatz 和 UIUC)上进行分类实验，结果表明该文所提方法能够有效地提高纹理图像在无噪声环境和含高斯噪声环境下的分类精度。	Experiments on three publicly available texture databases (Outex, Brodatz and UIUC) demonstrate that the proposed method can effectively improve the classification accuracy of texture images both in noise-free conditions and in the presence of different levels of Gaussian noise.
22101	该文针对传统波达方向角(DOA)估计算法在非均匀噪声下角度估计精度差及分辨率低的问题，基于矩阵补全理论，提出一种二阶统计量域下加权 L1(MC-WLOSRSS)稀疏重构 DOA 估计算法。	Focusing on the problem of poor accuracy and low resolution of traditional Direction Of Arrival (DOA) estimation algorithm in the presence of non-uniform noise, based on the Matrix Complement theory, a Weighted L1 Sparse Reconstruction DOA estimation algorithm is developed under the Second-order Statistical domain (MC-WLOSRSS) in this paper.
22102	首先，基于矩阵补全方法，引入弹性正则化因子将接收信号协方差矩阵重构为无噪声协方差矩阵；	Following the matrix completion approach, the regularization factor is firstly introduced to reconstruct the signal covariance matrix reconstruction as a noise-free covariance matrix.
22103	而后在二阶统计量域下通过矩阵求和平均将无噪声协方差矩阵多矢量问题转化为单矢量问题；	After that, the multi-vector problem of the noise-free covariance matrix can be transformed into a single vector one by exploiting sum-average operation for matrix in the second-order statistical domain.
22104	最后利用稀疏重构加权 L1 范数实现 DOA 参数估计。	Finally, the DOA can be complemented by employing the sparse reconstruction weighted L1 norm.