In order to quickly calculate the optimal fitting position of the orthopedic plate on the surface of broken bone to reduce the repeated adjustment times of the orthopedic plate during the surgical operation, a rapid calculation method of orthopedic plate fit based on improved Iterative Closest Point (ICP) algorithm was proposed. Firstly, under the guidance of the doctor, the fitting area was selected on the surface of the broken bone, and the point cloud of the inner surface for the orthopedic plate was extracted by using the angle between the normal vectors of the surface points for the orthopedic plate. Then, the two groups of point cloud models were smoothed, and the grid sampling method was adopted to simplify the point cloud models, after these operations, the characteristic relationship between the point clouds was used for the initial registration. Finally, the boundary and internal feature key points of the inner surface point cloud model of the orthopedic plate were extracted, K-Dimensional Tree (KD-Tree) was used to search the adjacent points, so that the feature key points of the orthopedic plate and the selected area of the broken bone surface were accurately registered by ICP. Taking tibia as the example to carry out experiments, and the results show that the proposed method can improve the registration efficiency while maintaining relatively high registration degree compared with other registration algorithms proposed in recent years. The proposed algorithm can realize the rapid registration between different damage types of tibia and orthopedic plate, and it is universal to other damaged bones.

In order to overcome the difficulty in discovering multi-stage attack from multi-source alerts, an algorithm was proposed to mine the attack sequence pattern. The multi-source alerts were normalized into a unified format by matching them with regular expressions. The redundant information of alerts was compressed, and the alerts of the same stage were clustered according to the association rule set trained by strong association rules, efficiently removing the redundant alerts, so that the number of alerts was reduced. Then, the clustered alerts were divided to obtain candidate attack event dataset by sliding-window, and the attack pattern mining algorithm PrefixSpan was used to find out the attack sequence patterns of multi-stage attack events. The experimental results show that the proposed algorithm can lead to an accurate and efficient analysis of alert correlation and extract the attack steps of attack events without expert knowledge. Compared with the traditional algorithm PrefixSpan, the algorithm has an increase in attack pattern mining efficiency of 48.05%.

Considering the influence of clothing recognition on clothing shopping image search, the characteristics of online clothing shopping images were analyzed and a novel clothing extraction algorithm was proposed on the basis of pose estimation and salient object detection, which combined pose estimation and salient object detection. Implementing pose estimation on images, the presented method realized adaptability to poses, and introduced that integrating pose estimation into the region detection part of the salient object detection to obtain the salient detection map combining pose estimation, which took two complementary advantages. The clothing region was automatically located. Clothing was extracted by adopting the graph cut principle iteratively. The experimental results demonstrate that the proposed algorithm can accurately extract the clothing in complex background, illustrating the effectiveness of the introduction of pose estimation and saliency detection to clothing extraction. Besides, it can be applied to most clothing shopping images, and has good universality.

An fast image stitching algorithm based on improved Speeded Up Robust Feature (SURF) was proposed to overcome the real-time and robustness problems of the original SURF based stitching algorithms. The machine learning method was adopted to build a binary classifier, which identified the critical feature points obtained by SURF and removed the non-critical feature points. In addition, the Relief-F algorithm was used to reduce the dimension of the improved SURF descriptor to accomplish image registration. The weighted threshold fusion algorithm was adopted to achieve seamless image stitching. Several experiments were conducted to verify the real-time performance and robustness of the improved algorithm. Furthermore, the efficiency of image registration and the speed of image stitching were improved.

As the fabric defect type of diversity and traditional artificial detection methods inefficient ,in order to detect the fabric defect more effective, A new approach, SGE, based on saliency region feature for fabric defect detection is studied. In this approach, the original image is divided into two parts, one extracts the saliency region feature of fabric defect by improved FSR roughly, another employing the gabor filter and taking the amplitude as an output characteristics, and extracts the saliency region feature of fabric defect by PSR accurately, then by using maximum entropy to segment the saliency region respectively and fused the sub-images. The result is get got by calculating perimeter and area of the contours to removal the isolated points. The experiment selects four types of typical fabric defect images and OpenCV library is used. The experiment result shows that the algorithm, without prior learning，meet the real-time.

As the session controlling protocol of application-layer, SIP has the features of simple, expansibility and dilatancibility. At the basis of simple introduction of SIP protocol, the JAIN SIP exploring construction for the fulfillment of SIP communication of SUN Co was discussed in detail. To use Java language and take JAIN SIP as the core, all kinds of communication entity basic method in the fulfillment of SIP communication were described and simple model for SIP communication was built.