本文假设各用户的签名特征空间具有一定相似性。
Assume the signature feature Spaces have the similar distributions for all users.
离线手写签名鉴别的主要困难在于签名特征的提取。
The main difficulty of off-line handwritten signature verification is feature extraction.
提出一种基于频道签名特征的P2P-TV流量精细识别方法。
This paper proposes a fine-grained identification method of P2P-TV traffic based on channel signatures.
介绍了手写签名特征抽取和选择的一般方法，并在此基础上，提出了一种实用的签名笔迹特殊点提取算法。
In this paper, the method for feature extraction and selection of handwriting signature is discussed. On this basis, a functional algorithm of special points extraction has been presented.
其一是图像预处理方法不当，在字符切分、大小归一化、倾斜校正以及签名图像的细线化时会丢失签名特征。
The first one is improper image pre-processing, such as segmentation, normalization and thinning, which lose the features of letters relationship and strokes.
利用它对签名样本的动态信息时间序列进行校正，可以提高签名特征向量在特征空间上分布的聚拢性，拉开真、伪签名特征向量在特征空间上的距离。
It can effectively increase the compactness of the feature vectors of genuine signatures, and therefore enlarge the distance from the feature vectors of forgery ones.
验证申请人的特征是否可由签名来证明。
Verify that the attributes of the claimant are proven by the signatures.
实验结果表明：动态特征能够作为认定书写人，鉴别摹仿签名的重要依据。
The results of experiments indicated that dynamic features were important basements which be used to identify writer, and also to distinguish imitated signatures.
通过将提取出的签名笔迹形状特征结合伪动态特征进行鉴别，可以有效降低识别错误率，达到较好的综合鉴别效果。
Through the combination of form and false dynamic characteristics, the rate of false recognition can be reduced effectively, preferable comprehensive identification effect can be achieved.
侧重签名能量特征提取方法的研究。
The study of signature energy feature extraction method is emphasized.
在线手写签名识别是通过人的生物特征进行身份验证的技术。
Online handwritten signature recognition is the technology by which the identity is verified via(person's) biological characteristic.
截至目前，已经用于身份识别的人体生物特征包括指纹、掌纹、虹膜、人脸、手形、签名等。
Up to now, many traits in human body have been applied, such as fingerprint, palmprint, iris, face, hand shape and signature, etc.
提出了一种基于DTW匹配的以签名能量为特征的在线手写签名验证算法。
The paper proposes an online handwriting signature verification algorithm with signature energy as feature based on dynamic time warping (DTW).
本文提出了一种将形状特征与伪动态特征相结合的采用2—阶鉴别方法的脱机中文签名鉴别系统。
In this article, an off-line Chinese signature verification system based on pseudo feature and shape feature extraction is introduced, furthermore, we adopted 2-pass verification method.
该方法具有成为一种全新的生育技术和测试方案的潜力，它并不只依靠形态特征，而且还利用化学签名。
The method has the potential for a novel fertility technology and a test scheme which does not only rely on morphological characteristics, but also utilizes chemical signatures.
在线手写签名验证是一种基于生物特征的身份识别技术，论文尝试将演化计算理论用于手写签名验证。
On-line handwriting signature verification is one kind of biometric identity recognition technology. This article tries to apply evolutionary computation theory to handwriting signature verification.
指出了已有的几个私有指定验证者签名方案的共有特征。
A common characteristic of some private designated verifier signature schemes is point out.
在特征的选择上，人工笔迹鉴定注重局部特征，而自动签名验证多用全局特征。
The differences between automatic signature verification and manual one are feature selecting and the way of judging.
在特征表示方面，用形状签名代替链码来描述轮廓，从而提高了计算速度，降低了由噪声干扰和镜头形变造成的影响。
The chain code was replaced by the shape signature and used to express the contour, improve the speed and decrease the effect of noise and transformation due to camera calibration.
在线手写签名认证是一种基于生物特征的身份认证技术。
On-line handwritten signature verification is one kind of biometric identity recognition technology.
最后提出二进制签名和排序签名算法改善了特征的匹配精度。
At last, the binary signature and the ordinal signature are proposed to improve the matching precision.
利用本文提出的基于特征签名的图像特征表示方法，本文进一步提出了基于多特征签名的图像检索系统，和基于多特征签名的重复图像检测方法。
By using signature based image feature, we stepped forward and proposed a multi-signature based image retrieval system and a multi-signature based duplicate image detecting method.
另外，多特征签名相对单特征签名，能进一步提高重复图像检测的召回率。
In addition, compared to single signature, multi-signature can improve the recall rate further in duplicate image detection.
作为特征签名在重复图像检测方面的应用，基于多特征签名的重复图像检测方法改进了矢量量化过程中的编码映射方式。
As another application of feature signature, multi-signature based duplicate image detecting method improved the mapping and encoding during vector quantization.
作为特征签名在重复图像检测方面的应用，基于多特征签名的重复图像检测方法改进了矢量量化过程中的编码映射方式。
As another application of feature signature, multi-signature based duplicate image detecting method improved the mapping and encoding during vector quantization.
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