几种脱机手写女书特征提取法的比较研究

目前,OCR技术已取得了多项研究成果,但是几乎没有人从事女书文字识别的研究。女书的特征提取是女书文字识别中的一个重点和难点。文章通过比较几种特征提取算法,提出一种基于G-DCD改进的特征提取算法,并用该算法正确提取手写体女书文字特征。     

汉语定义语句的抽取方法

该文介绍了在汉语文本中抽取定义语句的方法。方法的主要特点是：给定被定义的词汇（字符串）,应用Bo-yer-Moore算法查找该串在文本中的位置,继而在该句子中查找符合定义特征的谓词。在这个工作基础上,根据谓词字符串的特征排除谓词歧义,并按照句法分析的结果对定义语句修饰谓词的不同语法单元进行识别,从而实现了基于字符串和语法特征的识别的定义语句抽取。     

基于带有惩罚因子的阴性选择算法的恶意程序检测模型

提出了一个基于带有惩罚因子的阴性选择算法的恶意程序检测模型.该模型从指令频率和包含相应指令的文件频率两个角度出发,对指令进行了深入的趋向性分析,提取出了趋向于代表恶意程序的恶意程序指令库.利用这些指令,有序切分程序比特串,模型提取得到恶意程序候选特征库和合法程序类恶意程序特征库.在此基础上,文中提出了一种带有惩罚因子的阴性选择算法(negative selection algorithm with penalty factor,NSAPF),根据异体和自体的匹配情况,采用惩罚的方式,对恶意程序候选特征进行划分,组成了恶意程序检测特征库1(malware detection signature library 1,MDSL1)和恶意程序检测特征库2(MDSL2),以此作为检测可疑程序的二维参照物.综合可疑程序和MDSL1,MDSL2的匹配值,文中模型将可疑程序分类到合法程序和恶意程序.通过在阴性选择算法中引入惩罚因子C,摆脱了传统阴性选择算法中对自体和异体有害性定义的缺陷,继而关注程序代码本身的危险性,充分挖掘和调节了特征的表征性,既提高了模型的检测效果,又使模型可以满足用户对识别率和虚警率的不同要求.综合实验...     

一种基于GTD模型和状态空间处理的运动目标超宽带散射中心提取方法

超宽带条件下散射中心的不同运动将对参数提取造成影响.基于GTD模型和状态空间处理,本文提出了一种针对运动目标的超宽带散射中心提取方法.该方法首先将超宽带条件下的目标GTD散射模型转化为状态空间方程,通过奇异值分解提取散射中心的径向距离和径向速度信息;然后由标准正交向量基降维表示散射中心在整个带宽上的类型参数信息,采用遍历方法和最小二范数准则求解出散射中心的类型参数信息;最后基于最小二乘法求解出散射中心的散射强度.文中同时给出了各参数估计的CR界.仿真结果验证了文中方法的有效性,该方法可同时提取散射中心径向距离、径向速度、散射强度和类型参数信息,从而有利于各散射中心的跟踪和整体目标的有效识别.     

On-column solvent exchange for purified preparative fractions

On-column solvent exchange, using many of the principles of solid-phase extraction, has been implemented to significantly reduce evaporation cycle time following reverse-phase preparative HPLC. Additional benefits, such as a reduced potential for salt formation, thermal decomposition, and residual solvent, are also described. Fractions obtained from preparative separations, typically in a large volume of acetonitrile:water, are injected into the preparative HPLC and then eluted in acetonitrile, creating a new fraction in a volatile organic solvent. Minimal modification to the instrument was required, and unattended operation is possible. Acetonitrile evaporation is achieved within 3 h, compared with 17 h for aqueous-based fractions; lower temperatures can be used during the evaporation step; mobile-phase additives, likely to form salts with the target compound if concentrated in the fraction, are removed before evaporation; sample recovery and purity are unaffected.     

Cascade Markov random fields for stroke extraction of Chinese characters

Extracting perceptually meaningful strokes plays an essential role in modeling structures of handwritten Chinese characters for accurate character recognition. This paper proposes a cascade Markov random field (MRF) model that combines both bottom-up (BU) and top-down (TD) processes for stroke extraction. In the low-level stroke segmentation process, we use a BU MRF model with smoothness prior to segment the character skeleton into directional substrokes based on self-organization of pixel-based directional features. In the high-level stroke extraction process, the segmented substrokes are sent to a TD MRF-based character model that, in turn, feeds back to guide the merging of corresponding substrokes to produce reliable candidate strokes for character recognition. The merit of the cascade MRF model is due to its ability to encode the local statistical dependencies of neighboring stroke components as well as prior knowledge of Chinese character structures. Encouraging stroke extraction and character recognition results confirm the effectiveness of our method, which integrates both BU/TD vision processing streams within the unified MRF framework.     

LPP solution schemes for use with face recognition

Locality preserving projection (LPP) is a manifold learning method widely used in pattern recognition and computer vision. The face recognition application of LPP is known to suffer from a number of problems including the small sample size (SSS) problem, the fact that it might produce statistically identical transform results for neighboring samples, and that its classification performance seems to be heavily influenced by its parameters. In this paper, we propose three novel solution schemes for LPP. Experimental results also show that the proposed LPP solution scheme is able to classify much more accurately than conventional LPP and to obtain a classification performance that is only little influenced by the definition of neighbor samples.     

A review of vision-based systems for soccer video analysis

This paper presents a survey of soccer video analysis systems for different applications: video summarization, provision of augmented information, high-level analysis. Computer vision techniques have been adapted to be applicable in the challenging soccer context. Different semantic levels of interpretation are required according to the complexity of the corresponding applications. For each application area we analyze the computer vision methodologies, their strengths and weaknesses and we investigate whether these approaches can be applied to extensive and real time soccer video analysis.     

Invariant pattern recognition using contourlets and AdaBoost

In this paper, we propose new methods for palmprint classification and handwritten numeral recognition by using the contourlet features. The contourlet transform is a new two dimensional extension of the wavelet transform using multiscale and directional filter banks. It can effectively capture smooth contours that are the dominant features in palmprint images and handwritten numeral images. AdaBoost is used as a classifier in the experiments. Experimental results show that the contourlet features are very stable features for invariant palmprint classification and handwritten numeral recognition, and better classification rates are reported when compared with other existing classification methods.     

A feature extraction method for use with bimodal biometrics

Bimodal biometrics has been found to outperform single biometrics and are usually implemented using the matching score level or decision level fusion, though this fusion will enable less information of bimodal biometric traits to be exploited for personal authentication than fusion at the feature level. This paper proposes matrix-based complex PCA (MCPCA), a feature level fusion method for bimodal biometrics that uses a complex matrix to denote two biometric traits from one subject. The method respectively takes the two images from two biometric traits of a subject as the real part and imaginary part of a complex matrix. MCPCA applies a novel and mathematically tractable algorithm for extracting features directly from complex matrices. We also show that MCPCA has a sound theoretical foundation and the previous matrix-based PCA technique, two-dimensional PCA (2DPCA), is only one special form of the proposed method. On the other hand, the features extracted by the developed method may have a large number of data items (each real number in the obtained features is called one data item). In order to obtain features with a small number of data items, we have devised a two-step feature extraction scheme. Our experiments show that the proposed two-step feature extraction scheme can achieve a higher classification accuracy than the 2DPCA and PCA techniques.