脱机手写签名鉴别中的预处理技术研究

签名作为普遍接受的一种同意或授权的方式在社会生活中发挥了重要作用。论述了预处理在脱机手写签名鉴别中的作用,以实例阐述了脱机手写签名鉴别中预处理的过程。     

基于二阶系统的脱机中文签名鉴定

针对脱机手写签名鉴定的实现,提出了二阶签名鉴定系统,并进行了相关的实验。在第一阶段中使用了基于静态几何特征与伪动态特征进行粗分类识别,在第二阶段中使用了基于Zernike的特征进行精细分类识别,最终识别出待鉴定的手写签名,实验证明采该鉴定方法,可以在提高鉴定率的同时提高鉴定的效率。     

基于小波变换的汉字签名鉴别

汉字签名鉴别是通过对手写人的笔迹进行分析从而得出签名是否由某特定人书写的结论。不同人的签名特征是不相同的，可以从其签名信息中抽取出最有效的签名特征来区分真假签名。该文采用新的数学工具——Wavelet 变换方法，利用复信号小波变换的优良特性抽取了一些新的具有旋转、平移及尺度不变性的签名特征，经验证这种特征具有很好的稳定性与可靠性。     

基于字形信息和书写力信息的在线签名鉴别

针对目前签名采集设备只能获得书写压力信息或不能同时获得书写力信息与字形信息的不足,采用自行研制的F_Tablet手写平台同时采集签名书写过程中的二维字形信息和三维书写力信息,并提出一种综合利用字形信息与书写力信息进行在线签名鉴别的方法.该方法先对签名进行预处理和笔画分割,并基于签名笔画数目建立多个签名模板,然后采用概率统计和迭代实验确定书写力内部以及书写力与字形之间的权重比和个性化的判别阈值,实现对签名的分类.基于利用F_Tablet手写平台构建的签名样本库的签名鉴别实验结果证明了本文提出的基于字形信息和书写力信息的在线签名鉴别方法的有效性.     

在线手写签名认证研究

手写签名认证是人工智能领域的一个重要课题,对它的深入研究具有重要的现实意义。文章采用基于概率统计的方法表示手写签名的速度特征,用vague集相似度表示书写压力特征,并通过细分的拐点表示笔画特征进行签名认证,最后,构造了一个在线手写签名认证系统。实验证明了该方法的有效性。     

伪Zernike矩的不变性分析及其应用

本文从几何矩的概念出发,详细介绍了Zernike和伪Zernike矩。然后重点分析了伪Zernike矩的旋转、位移及尺度不变性。本文利用脱机手写签名图像对此进行了验证,实验结果证明了其不变性特征。故本文提出利用伪Zernike矩来进行脱机手写签名图像的特征提取。     

基于图像处理技术的离线手写签名鉴别方法

由于手写签名具有高度的唯一性、不遗忘性、自然性与可靠性已成为身份识别的研究热点。文中对手写签名识别技术主要是离线手写签名识别技术进行了详细分析,在研究手写签名的特点和在身份认证中实用性的基础上,提出了一种基于图像处理技术的手写签名鉴别方案,该方案利用了图像的缩放和膨胀细化技术对图像进行处理,然后利用均方差和峰值信噪比来评价签名图像的相似度。试验数据表明该鉴别方法简单可行且高效,能获得较好的验证率和鉴别率。     

基于图像处理技术的离线手写签名鉴别方法

由于手写签名具有高度的唯一性、不遗忘性、自然性与可靠性已成为身份识别的研究热点。文中对手写签名识别技术主要是离线手写签名识别技术进行了详细分析,在研究手写签名的特点和在身份认证中实用性的基础上,提出了一种基于图像处理技术的手写签名鉴别方案,该方案利用了图像的缩放和膨胀细化技术对图像进行处理,然后利用均方差和峰值信噪比来评价签名图像的相似度。试验数据表明该鉴别方法简单可行且高效,能获得较好的验证率和鉴别率。     

基于集成神经网络的离线手写签名鉴别方法

离线手写签名鉴别是一种基于生物特征的身份识别技术,利用集成神经网络对手写签名进行自动鉴别。该集成神经网络由特征分配网络、神经网络认证主体和决策融合识别网络三部分构成。通过为每个签名者构造独立的分类器,并分别为每个分类器构造认证、识别训练集,解决了离线签名的认证和识别问题。基于此方法的签名鉴别实验获得了比较满意的结果,可以用来辅助人类专家进行签名鉴别。     

一个实用脱机手写汉字笔迹库的建立

建立一个实用的脱机手写汉字笔迹库是研究笔迹鉴别技术的基础,论文结合笔迹图像与书写者信息设计了一个脱机手写汉字笔迹库系统,详细介绍了笔迹样本采集方案及系统的主要功能,阐述了部分关键问题的解决方案。     

Efficient generic on-line/off-line (threshold) signatures without key exposure

The “hash–sign–switch” paradigm was firstly proposed by Shamir and Tauman with the aim to design an efficient on-line/off-line signature scheme. Nonetheless, all existing on-line/off-line signature schemes based on this paradigm suffer from the key exposure problem of chameleon hashing. To avoid this problem, the signer should pre-compute and store a plenty of different chameleon hash values and the corresponding signatures on the hash values in the off-line phase, and send the collision and the signature for a certain hash value in the on-line phase. Hence, the computation and storage cost for the off-line phase and the communication cost for the on-line phase in Shamir–Tauman’s signature scheme are still a little more overload. In this paper, we first introduce a special double-trapdoor hash family based on the discrete logarithm assumption and then incorporate it to construct a more efficient generic on-line/off-line signature scheme without key exposure. Furthermore, we also present the first key-exposure-free generic on-line/off-line threshold signature scheme without a trusted dealer. Additionally, we prove that the proposed schemes have achieved the desired security requirements.     

Automatic gender detection using on-line and off-line information

In this paper, the problem of classifying handwritten data with respect to gender is addressed. A classification method based on Gaussian Mixture Models is applied to distinguish between male and female handwriting. Two sets of features using on-line and off-line information have been used for the classification. Furthermore, we combined both feature sets and investigated several combination strategies. In our experiments, the on-line features produced a higher classification rate than the off-line features. However, the best results were obtained with the combination. The final gender detection rate on the test set is 67.57%, which is significantly higher than the performance of the on-line and off-line system with about 64.25 and 55.39%, respectively. The combined system also shows an improved performance over human-based classification. To the best of the authors’ knowledge, the system presented in this paper is the first completely automatic gender detection system which works on on-line data. Furthermore, the combination of on-line and off-line features for gender detection is investigated for the first time in the literature.     

A Controlled Experiment to Evaluate On-Line Process Guidance

Process-centered software engineering environments are expected to improve an individual's comprehension of work activities, as well as improve communication and reduce conflicts among teams of software developers. We chose to investigate individual responses when using such an environment before looking for a group response. A controlled experiment compared off-line and on-line implementations of measurement-based process guidance to test three hypotheses: first, individuals accomplish their work more efficiently when using on-line process guidance as compared to off-line guidance; second, individuals are willing to use an on-line system for guidance; and third, individuals adjust their behavior based on quantitative quality models. The subjects worked alone on two testing exercises. Subjects used either an off-line or an on-line guidance technique during the first exercise, and the other technique during the second exercise. The results did not confirm the hypotheses. On average, subjects worked more efficiently when using off-line guidance, preferred off-line guidance, and ignored quantitative quality models. Post hoc analysis identified a strong correlation between subject experience level and preference for the type of guidance, a result that deserves further investigation.     

Diagnosability of discrete event systems and its applications

As man-made systems become more and more complex, diagnostics of component failures is no longer an easy task that can be performed based on experience and intuition. Therefore, it is important to develop a systematic approach to diagnostic problems. Diagnostics can be done either on-line or off-line. By on-line diagnostics, we mean diagnostics performed while the system to be diagnosed is in normal operation. On the other hand, in off-line diagnostics, the system is not in normal operation. We will study both on-line and off-line diagnostics in this paper and identify main features and differences of these two types of diagnostics. We will also introduce the concept of diagnosability and study its properties, all in the framework of discrete event systems. This study is motivated by diagnostic problems in the automotive industry and we will emphasize its applications.     

高效的无证书的在线/离线签密方案

在线/离线签密方案具有资源耗费小的特点,但已有的在线/离线签密方案大多是是基于证书的密码体制或者基于身份的密码体制提出的,都存在证书的管理问题或密钥的托管问题。基于无证书密码体制的优点,提出了一种高效的无证书的在线/离线签密方案。分析表明,该方案不需要证书的管理,也没有密钥的托管问题,满足签密所要求的所有性质,且在效率上优于已有的基于身份的在线/离线签密方案以及无证书的签密方案。     

Hybrid active learning for reducing the annotation effort of operators in classification systems

Active learning is understood as any form of learning in which the learning algorithm has some control over the input samples due to a specific sample selection process based on which it builds up the model. In this paper, we propose a novel active learning strategy for data-driven classifiers, which is based on unsupervised criterion during off-line training phase, followed by a supervised certainty-based criterion during incremental on-line training. In this sense, we call the new strategy hybrid active learning. Sample selection in the first phase is conducted from scratch (i.e. no initial labels/learners are needed) based on purely unsupervised criteria obtained from clusters: samples lying near cluster centers and near the borders of clusters are expected to represent the most informative ones regarding the distribution characteristics of the classes. In the second phase, the task is to update already trained classifiers during on-line mode with the most important samples in order to dynamically guide the classifier to more predictive power. Both strategies are essential for reducing the annotation and supervision effort of operators in off-line and on-line classification systems, as operators only have to label an exquisite subset of the off-line training data resp. give feedback only on specific occasions during on-line phase. The new active learning strategy is evaluated based on real-world data sets from UCI repository and collected at on-line quality control systems. The results show that an active learning based selection of training samples (1) does not weaken the classification accuracies compared to when using all samples in the training process and (2) can out-perform classifiers which are built on randomly selected data samples.     

Optimal Time-Critical Scheduling via Resource Augmentation

We consider two fundamental problems in dynamic scheduling: scheduling to meet deadlines in a preemptive multiprocessor setting, and scheduling to provide good response time in a number of scheduling environments. When viewed from the perspective of traditional worst-case analysis, no good on-line algorithms exist for these problems, and for some variants no good off-line algorithms exist unless P = NP . We study these problems using a relaxed notion of competitive analysis, introduced by Kalyanasundaram and Pruhs, in which the on-line algorithm is allowed more resources than the optimal off-line algorithm to which it is compared. Using this approach, we establish that several well-known on-line algorithms, that have poor performance from an absolute worst-case perspective, are optimal for the problems in question when allowed moderately more resources. For optimization of average flow time, these are the first results of any sort, for any NP -hard version of the problem, that indicate that it might be possible to design good approximation algorithms.     

A knowledge-based task-level programming and execution environment for robots

A knowledge-based framework to support task-level programming and operational control of robots is described. Our bask intention is to enhance the intelligence of a robot control system so that it may carefully coordinate the interactions among discrete, asynchronous and concurrent events under the constraints of action precedence and resource allocation. We do this by integrating both off-line and on-line planning capabilities in a single framework. The off-line phase is equipped with proper languages for describing workbenches, specifying tasks, and soliciting knowledge from the user to support the execution of robot tasks. A static planner is included in the phase to conduct static planning, which develops local plans for various specific tasks. The on-line phase is designed as a dynamic control loop for the robot system. It employs a dynamic planner to tackle any contingent situations during the robot operations. It is responsible for developing proper working paths and motion plans to achieve the task goals within designated temporal and resource constraints. It is implemented in a distributed and cooperative blackboard system, which facilitates the integration of various types of knowledge. Finally, any failures from the on-line phase are fed back to the off-line phase. This forms the interaction between the off-line and on-line phases and introduces an extra closed loop opportunistically to tune the dynamic planner to adapt to the variation of the working environment in a long-term manner.     

基于标称性能指标的离线鲁棒预测控制器综合

离线鲁棒预测控制综合算法离线确定一个控制律序列,对应一组吸引域,在线根据当前状态的位置选择相应的控制律,该类控制器在线计算量非常小,而可行性和最优性与其它综合算法相比或多或少要差一些,为此,采用标称性能指标而不是“最坏情况”性能指标来改进离线综合算法的可行性和最优性,改进的控制器保持了原有控制器的稳定性以及控制律关于系统状态的连续性.仿真结果说明了采用标称性能指标的优越性。