一种光学测量中椭圆边缘精确提取算法

提出了一种椭圆边缘精确提取算法。对图像灰度分布函数按二次泰勒展开式展开,利用高斯核函数和图像卷积的方式进行微分求导,在过零点附近求取二阶导数绝对值的极小值点,然后,通过计算Hessian矩阵得到该边缘点的法向方向,在法向方向进行多项式曲线拟合和直线拟合得到子像素边缘点。实验结果表明,算法对子像素边缘提取具有较好的精度,在光学测量椭圆形标识检测中有参考价值。

等距圆锥螺旋线数理性质的初探

等距圆锥螺旋线是一种重要的空间曲线,从数学和物理两个角度研究其性质,并进行了数值模拟.

跳帧转换编码中的运动矢量修正

在跳帧转换编码中,利用前向向量法进行运动矢量合成可以得到较好的图像信噪比。本文提出了用HAVS快速搜索算法对合成的运动矢量进行修正的方法,同时给出了一个最佳搜索窗口。实验结果表明,在±2像素内,用HAVS算法对合成的运动矢量修正,既能明显提高图像的信噪比,同时又能最大限度的降低计算量。

A Novel Feature-based Method for Sentiment Analysis of Chinese Product Reviews

Sentiment analysis of online reviews and other user generated content is an important research problem for its wide range of applications.In this paper,we propose a feature-based vector model and a novel weighting algorithm for sentiment analysis of Chinese product reviews.Specifically,an opinionated document is modeled by a set of feature-based vectors and corresponding weights.Different from previous work,our model considers modifying relationships between words and contains rich sentiment strength descriptions which are represented by adverbs of degree and punctuations.Dependency parsing is applied to construct the feature vectors.A novel feature weighting algorithm is proposed for supervised sentiment classification based on rich sentiment strength related information.The experimental results demonstrate the effectiveness of the proposed method compared with a state of the art method using term level weighting algorithms.

A Group Oriented Cryptosystem for the Vector Space Access Structure

A group oriented cryptosystem for the vector space access structure was proposed. This cryptosystem adopts self-certified public keys. It allows the participants of an authorized subset to cooperatively access an encrypted message. All data delivered in the cryptosystem are public. Therefore it does not need a partial decrypting results combiner and any secure communication channel. The security of the group oriented cryptosystem is based on the intractability of the discrete log problem and difficulty of factoring large integers. The suspected attacks can not break it.

A REAL-TIME C-V CLUSTERING ALGORITHM FOR WEB-MINING

In this letter, a real-time C-V (Characteristic-Vector) clustering algorithm is put forth to treat with vast action data which are dynamically collected from web site. The algorithm cites the concept of C-V to denote characteristic, synchronously it adopts two-value [0,1]input and self-definition vigilance parameter to design clustering-architecture. Vector Degree of Matching (VDM) plays a key role in the clustering algorithm, which determines the magnitude of typical characteristic. Making use of stability analysis, the classifications are confirmed to have reliably hierarchical structure when vigilance parameter shifts from 0.1 to 0.99. This non-linear relation between vigilance parameter and classification upper limit helps mining out representative classifications from net-users according to the actual web resource, then administering system can map them to web resource space to implement the intelligent configuration effectually and rapidly.

Face Orientation Normalization Using Eye Positions

Despite the fact that progress in face recognition algorithms over the last decades has been made, changing lighting conditions and different face orientation still remain as a challenging problem. A standard face recognition system identifies the person by comparing the input picture against pictures of all faces in a database and finding the best match. Usually face matching is carried out in two steps： during the first step detection of a face is done by finding exact position of it in a complex background （various lightning condition）, and in the second step face identification is performed using gathered databases. In reality detected faces can appear in different position and they can be rotated, so these disturbances reduce quality of the recognition algorithms dramatically. In this paper to increase the identification accuracy we propose original geometric normalization of the face, based on extracted facial feature position such as eyes. For the eyes localization lbllowing methods has been used： color based method, mean eye template and SVM （Support Vector Machine） technique. Experimental investigation has shown that the best results for eye center detection can be achieved using SVM technique. The recognition rate increases statistically by 28% using face orientation normalization based on the eyes position.

On-line least squares support vector machine algorithm in gas prediction

Traditional coal mine safety prediction methods are off-line and do not have dynamic prediction functions.The Support Vector Machine(SVM) is a new machine learning algorithm that has excellent properties.The least squares support vector machine(LS-SVM) algorithm is an improved algorithm of SVM.But the common LS-SVM algorithm,used directly in safety predictions,has some problems.We have first studied gas prediction problems and the basic theory of LS-SVM.Given these problems,we have investigated the affect of the time factor about safety prediction and present an on-line prediction algorithm,based on LS-SVM.Finally,given our observed data,we used the on-line algorithm to predict gas emissions and used other related algorithm to compare its performance.The simulation results have verified the validity of the new algorithm.

Prediction of Transverse Permeability for Unidirectional Fiber Tows Based on the Homogenization Theory

The transverse permeability of unidirectional fiber tows is calculated using homogenization method.Each fiber tow consisting of 21 filaments is arranged in uniform square packing.Stokes governing equation is analogized with Lame equation used in the linear elasticity problem and is solved by the finite element code ANSYS.The prediction for transverse permeability of unidirectional fiber obtained by the homogenization approach is compared with other analytical methods.The result shows a good agreement with Kozeny-Carman equation and Gebart square packing model.A model for nonuniform fiber distribution and measurement technology are proposed.It can be found that the experimental result is in excellent agreement with predicted permeability in the nonuniform distribution model.

No-reference image quality assessment based on nonsubsample shearlet transform and natural scene statistics

A novel no-reference(NR) image quality assessment(IQA) method is proposed for assessing image quality across multifarious distortion categories. The new method transforms distorted images into the shearlet domain using a non-subsample shearlet transform(NSST), and designs the image quality feature vector to describe images utilizing natural scenes statistical features: coefficient distribution, energy distribution and structural correlation(SC) across orientations and scales. The final image quality is achieved from distortion classification and regression models trained by a support vector machine(SVM). The experimental results on the LIVE2 IQA database indicate that the method can assess image quality effectively, and the extracted features are susceptive to the category and severity of distortion. Furthermore, our proposed method is database independent and has a higher correlation rate and lower root mean squared error(RMSE) with human perception than other high performance NR IQA methods.