ID 原文 译文
40226 该方法无需对输入图像进行任何预处理，仅仅在图像平面上直接计算轮廓差分，就可以将直线的探测问题演变成轮廓差分极大值的搜寻问题。 By use of the new difference, the process of line detection is converted to searching for local maximum in contour differences. The detection algorithm needs no any pre-processing on the input image beyond the calculation of contour difference.
40227 对复杂的田间灰度图像的实验表明，本文方法能准确地标记出植株的位置，并且对噪声和模糊等干扰因素也较为鲁棒。 Experiments on gray scale field images show that the proposed method can localize most plants accurately, even in the inference of noise and blur.
40228 相比于高分辨率（High resolution，HR）人脸图像，低分辨率（Low resolution，LR）人脸图像的识别效果较差。 Compared with high-resolution(HR) face image, the low-resolution(LR) face image recognition effect is poorer.
40229 针对此问题，已有研究者提出基于典型相关分析和核典型相关分析的LR人脸识别算法，但其并未考虑样本的类信息和视图间的一致性。 Researchers have put forward several LR face recognition algorithms based on the canonical correlation analysis(CCA)and kernel canonical correlation analysis(KCCA)to solve this problem, which ignored the supervised information and the consistency information between different views.
40230 本文同时利用数据的类信息和视图间的一致性信息，提出一致判别相关分析（Consistent discriminant correlation analysis，CDCA），进而得到基于CDCA的LR人脸识别算法。 In this paper, we put forward a novel dimensionality reduction algorithm—consistent discriminant correlation analysis(CDCA) by virtue of the class information and consistency information of different views.Furthermore, we design a LR face recognition algorithm based on CDCA.
40231 该算法先利用主成分分析从HR和LR人脸图像中提取主成分特征，然后利用CDCA学习HR和LR人脸的特征投影矩阵，进而实现LR人脸识别。 Concretely, we extract the principal component features from HR and LR face images respectively, use CDCA to learn the characteristic projection matrix of HR and LR face, and realize LR face recognition with the help of projection matrix.
40232 实验结果表明，相比现有的LR人脸识别算法，该算法具有较好的识别效果和鲁棒性。 The experimental results show the superiority of the proposed method on recognition effect and robustness compared with the existing LR face recognition algorithms.
40233 为提高双滤波器结构（Dual filter structure，DFS）一级滤波器W1(k）的收敛速度，本文提出一种改进的Haar子带变换（Partial Haar transform，PHT）算法。 An improved partial Haar transform(PHT)algorithm is proposed in this paper to improve the convergence of the first filter W1(k)in the dual filter structure(DFS).
40234 新算法先对W1(k）的输入信号进行PHT变换以压缩滤波器长度； In the new algorithm, the W1(k)adapts using a PHT version of the input signal to decrease its length.
40235 然后通过优化收敛步长使后验误差最小化以提高收敛速度； The convergence of W1(k)is further improved by optimizing the step size to minimize the a posteriori error.