ID 原文 译文
23635 因此，利用生物特征进行鉴定已成为时下热潮。 Therefore, the biometric recognition methods have become a compelling issue.
23636 其中的指纹识别更是因其方便性和可靠性受到普遍认同。 Among the methods, the fingerprint identification technique attracts much interest due to its excellent feasibility and reliability performance.
23637 传统的指纹识别方法基于特征点比对寻求相似性，此种方法特征点寻找容易出错，且随着指纹的模糊、破坏、污损或是其他问题，均会使识别率明显降低。 The traditional fingerprint recognition method is based on matching feature points. However, this method needs a long time to find the feature points, and suffering the blur, scaling, damage, and other problems, the recognition rate is decreased seriously.
23638 针对这些问题，该文提出基于深度卷积神经网络(CNN)的 CBF-FFPF(Central Block Fingerprint and Fuzzy Feature Points Fingerprint)算法对污损指纹图像进行分类识别。 To solve these problems, a fouling and damaged fingerprint recognition algorithm named CBF-FFPF (Central Block Fingerprint and Fuzzy Feature Points Fingerprint) is proposed, it is based on Convolution Neural Network (CNN) of deep learning.
23639 CBF-FFPF 算法提取指纹中心点分块图像及特征点模糊化图，合并后输入 CNN 网络，进行指纹深层特征识别。 Combining small sub block fingerprint, which takes the fingerprint core point as the center from the thinned image and fuzzy graph of fingerprint feature points, as original image input to obtain the recognition rate.
23640 将该算法与基于主成分分析(KPCA)，超限学习机(ELM)和 k 近邻分类器(KNN)的指纹识别算法进行比较，实验结果表明，所提出的 CBF-FFPF算法对污损指纹识别有更高的识别率和更好的鲁棒性。 The recognition rate based on CBF-FFPF is compared with the fingerprint identification algorithm based on Kernel Principal Component Analysis (KPCA), Extreme Learning Machine (ELM), and K-Nearest Neighbor (KNN). Experimental results show that fingerprint recognition algorithm CBF- FFPF has higher recognition rate and better robustness.
23641 为了对空间目标进行精确定位与跟踪，建立目标运动轨迹，该文对基于运动信息的星图暗弱空间运动点目标检测算法进行研究。 To accurately locate, track space targets, and establish targets trajectory, a study on moving target detection based on motion information for star maps is practiced.
23642 首先建立一种新的空间运动点目标描述模型，然后提出基于相关系数矩阵的运动点目标检测算法，最后提取目标运动轨迹，并给出了点目标运动速度的估计模型。 Firstly, a new model to characterize the space moving target is constructed, then an algorithm for moving point target detection is proposed based on correlation coefficient matrix statistical information. Based on the detection method, target motion trajectory is finally extracted and the velocity estimation model of the moving target is built.
23643 根据实测数据和硬件平台，提出了检测概率和虚警率相结合的评价方法对算法进行验证。 This paper also proposes an evaluation method, which combines detection probability and false alarm probability, to verify this method.
23644 试验结果表明，所提方法能够在保持较低的虚警概率下获得较高的检测概率，优于参与比较的其它目标检测方法。 The experimental results demonstrate that the proposed method outperforms the compared methods and can achieve high detection probability while keeping low false alarm probability.