关于可解群的特征标图

从群的不可约特征标维数的若干信息来推知群的结构一直是群表示论的重要课题 .ManzO在文献 [2～ 6 ]中研究了可解群G的特征标图Γ(G)图论性质 ,并且当Γ(G)不连通时 ,给出了G的纯群理论性质 (引理 1) .本文将讨论若Γ(G)为连通图 ,Γ(G)有怎样的图论性质以及它们如何影响可解群G的结构 ,从而推广了文献 [2～ 6

关于可解群的一类特征标图

在可解群Ｇ的特征标图的中心ＺΓ（Ｇ）满足一定条件时，给出了Ｇ的特征标子图Γ（Ｇ）－ＺΓ（Ｇ）的连通分支数，同时给出了一类拟ＰＰＣ（χ０，ｐ。

关于可解群的特征标图

从群的不可约特征标维数的若干信息来推知群的结构一直是群表示论的重要课题，Ｍａｎｚ，０．在（２）－（６）中研究了可解群Ｇ的特征标图Г（Ｇ）图论性质，并且当Г（Ｇ）不连通时，给出Ｇ的纯群理论性质（引理１）．本文将讨论若Г（Ｇ）为连通图，Г（Ｇ）有怎样的图论性质以及它们如何影响可解群的Ｇ的结构，从而推广了（２）－（６）的若干结果。

Product image sentence annotation based on kernel descriptors and tag-rank

Dealing with issues such as too simple image features and word noise inference in product image sentence anmotation, a product image sentence annotation model focusing on image feature learning and key words summarization is described. Three kernel descriptors such as gradient, shape, and color are extracted, respectively. Feature late-fusion is executed in turn by the multiple kernel learning model to obtain more discriminant image features. Absolute rank and relative rank of the tag-rank model are used to boost the key words＇ weights. A new word integration algorithm named word sequence blocks building （WSBB） is designed to create N-gram word sequences. Sentences are generated according to the N-gram word sequences and predefined templates. Experimental results show that both the BLEU-1 scores and BLEU-2 scores of the sentences are superior to those of the state-of-art baselines.

Feature extraction for target identification and image classification of OMIS hyperspectral image

In order to combine feature extraction operations with specific hyperspectral remote sensing information processing objectives,two aspects of feature extraction were explored. Based on clustering and decision tree algorithm,spectral absorption index (SAI),continuum-removal and derivative spectral analysis were employed to discover characterized spectral features of different targets,and decision trees for identifying a specific class and discriminating different classes were generated. By combining support vector machine (SVM) classifier with different feature extraction strategies including principal component analysis (PCA),minimum noise fraction (MNF),grouping PCA,and derivate spectral analysis,the performance of feature extraction approaches in classification was evaluated. The results show that feature extraction by PCA and derivate spectral analysis are effective to OMIS (operational modular imaging spectrometer) image classification using SVM,and SVM outperforms traditional SAM and MLC classifiers for OMIS data.

EVALUATION OF THE DIAGNOSTIC CRITERIA FOR THE LOCALIZATION OF ACQUIRED ARTERIOVENOUS FISTULAS BY COLOR DOPPLER FLOW IMAGING

Objective. To evaluate the diagnostic criteria for the localization of acquired arteriovenous fistulas (AVFs) by color Doppler flow imaging (CDFI) based on the features of their hemodynamic changes. Methods. The shape and hemodynamic changes of involved vessels which could be helpful to localize the sites of fistulas were studied according to the observation of 10 cases of acquired AVFs. Results. The sites of the fistulas could be shown by two dimensional ultrasonography and color flow imaging in 40%and 80%cases, respectively. In all cases, turbulent high velocity flow was present at the sites of the fistulas, low resistant flow was present in the arteries proximal to the fistulas, and artery like flow was detected in the veins. Conclusion. CDFI was accurate for the localization of acquired AVFs, which were mainly localized by their hemodynamic changes shown by pulse Doppler ultrasound.

Semi-reference image quality metric for virtual view images in free-viewpoint television

A semi-reference image quality assessment metric based on similarity measurement for synthesized virtual viewpoint image （VVI） in free-viewpoint television system （FFV） is proposed in this paper. The key point of the proposed metric is taking resemblant information between VVI and its neighbor view images for quality assessment to make our metric to be extended to multi-semi-reference image quality assessment easily. The proposed metric first extracts impact factors from image features, then combines an image synthesis technique and similarity functions, in which, disparity information are taken into account for registering the resemblant regions. Experiments are divided into three phases. Phase I is to verify the validation of the proposed metric by taking impaired images and original reference into account. The experimental results show the agreement between evaluation scores and bio-characteristic of human visual system. Phase II shows the accordance with Phase I by taking neighbor view as reference. The proposed metric can be taken as a full reference one to evaluate the image quality even though the original reference is absent. Phase III is then performed to evaluate the quality of WI. Evaluation scores in the experimental results are able to evaluate the quality of VVI.

STATISTICAL PROBABILITY BASED ALGORITHM FOR EXTRACTING FEATURE POINTS IN 2-DIMENSIONAL IMAGE

An algorithm for automatically extracting feature points is developed after the area of feature points in 2-dimensional (2D) imagebeing located by probability theory, correlated methods and criterion for abnormity. Feature points in 2D image can be extracted only by calculating standard deviation of gray within sampled pixels area in our approach statically. While extracting feature points, the limitation to confirm threshold by tentative method according to some a priori information on processing image can be avoided. It is proved that the proposed algorithm is valid and reliable by extracting feature points on actual natural images with abundant and weak texture, including multi-object with complex background, respectively. It can meet the demand of extracting feature points of 2D image automatically in machine vision system.

Support vector machine with mixture of kernels for automatic image annotation

Automatic image annotation(AIA)has become an important and challenging problem in computer vision due to the existence of semantic gap.In this paper,a novel support vector machine with mixture of kernels(SVM-MK)for automatic image annotation is proposed.On one hand,the combined global and local block-based image features are extracted in order to reflect the intrinsic content of images as complete as possible.On the other hand,SVM-MK is constructed to shoot for better annotating performance.Experimental results on Corel dataset show that the proposed image feature representation method as well as automatic image annotation classifier,SVM-MK,can achieve higher annotating accuracy than SVM with any single kernel and mi-SVM for semantic image annotation.

Hypercube and Tetrahedron Algebra

Let D be an integer at least 3 and let H(D, 2) denote the hypercube. It is known that H(D, 2) is a Q-polynomial distance-regular graph with diameter D, and its eigenvalue sequence and its dual eigenvalue sequence are all {D-2i}D i=0. Suppose that denotes the tetrahedron algebra. In this paper, the authors display an action of ■ on the standard module V of H(D, 2). To describe this action, the authors define six matrices in Mat X(C), called A, A*, B, B*, K, K*.Moreover, for each matrix above, the authors compute the transpose and then compute the transpose of each generator of ■ on V.