Aiming at the problems of long registration time and low registration accuracy of current image registration algorithms,an image registration algorithm based on Grid-based Motion Statistics(GMS),Vector Coefficient Similarity (VCS)and Graph-Cut RANdom SAmple Consensus(GC-RANSAC)was proposed. Firstly,the feature points of the image were extracted through the ORB(Oriented FAST and Rotated BRIEF)algorithm,and Brute-Force matching of the feature points was performed. Then,the coarse matching feature points in the image were meshed by the GMS algorithm,and the coarse matching pairs were filtered based on the principle that high feature support exists in the neighborhood of the correct matching points in the grid. And the part elimination was performed to the matching pairs by introducing the principle that the image matching pair has VCS not exceed a set threshold during vector operation,which is beneficial to the fast convergence of the algorithm in the later stage. Finally,the local optimal model fitting was performed by using the GC-RANSAC algorithm to obtain the fine matching feature point set and achieve image registration and stitching with high precision. Compared with algorithms such as ASIFT+RANSAC,GMS,AKAZE+RANSAC,GMS+GC-RANSAC,the results show that the proposed algorithm improves the average matching accuracy by 30. 34% and reduces the average matching time by 0. 54 s.

In view of the bending properties of cloth, an approximate model of nonlinear bending was proposed based on the analysis of the fabric characteristics and internal structure of cloth. Firstly, the parameters of bending properties were obtained through the measurement of bending properties of real cloth. Then, the bending model based on mean curvature was put forward to calculate the bending force. Secondly, the surface mean curvature and Gauss curvature were used to segment the triangular mesh model of cloth in the dynamic simulation. Finally, the bending force was updated according to the change of the mean curvature. In the comparison experiments with the Volino's bending model, the key frame speed of the proposed model increased by an average of 2.7% in the process of bending and 4.1% in the process of lifting arms without affecting the quality of cloth simulation. The experimental results show that the proposed model is simple and accurate, and it can fully show the details of clothing folds in a natural way.

According to the difference between normal users visiting patterns and abnormal ones, a new method to detect applicationlayer Distributed Denial of Service (DDoS) attack was proposed based on IP Service Request Entropy (SRE) time series. By approximating the Adaptive AutoRegressive (AAR) model, the SRE time series was transformed into a multidimensional vector series regarded as a description of current users visiting patterns. Furthermore, a Support Vector Machine (SVM) classifier was applied to classify vector series and identify the attacks. The simulation results show that this approach not only can distinguish between normal traffic and DDoS attack traffic, but also is suitable to detect DDoS attack against the large scale network traffic, which does not arouse the sharp changes of the network traffic.