An epidemic model gives an efficient approach for transaction processing of replication systems in weakly connected environments. The approach has the advantages of high adaptation, support for low-bandwidth network, and committing updates in an entirely decentralized control fashion. But the previous implementing protocols, like ROWA protocol, quorum protocol, and voting protocol, have a common shortcoming that they are pessimistic in conict reconciliation, therefore bring high transaction abort rate and reduce system perfor-mance dramatically when the workload scales up. In this paper, an optimistic voting protocol, which introduces condition vote and order vote in the voting process of transactions, is pro-posed. The condition vote and order vote postpone the nal decision on con icting transactions and avoid transaction aborts that are incurred by read-write and write-write concts. Exper-imental results indicate thatthe optimistic voting protocol decreases abort rate and improvesaverage response time of transactions markedly when compared to other protocols.

Real-time face tracking and 3D reconstruction from video is a challenge in computer graphics and computer vision. We develop a system to track face by integrating image pyramid algorithm and Kalman filter. First, Facial features of the first frame are acquired automatically by Plessey corner detector according to FDP in MPEG-4 and FACS based on anatomical knowledge and general 3D model. And then corner disparity is obtained by image pyramid algorithm and Kalman filter. Kalman filter is employed to resolve the tracking speed and occlusion problems. Finally, 3D model is reconstructed by SfM (Structure from motion). Experimental results show the high prospect of this algorithm, and the two issues of real-time face tracking and modeling are resolved in our algorithm.

With the increasing number of people who can afford to make videos to record their lives, home videos play more and more important role in multimedia. Video abstraction is an efficient way to help review such a huge amount of home videos. In this paper, a home video abstraction technique combining audio and video features is presented. The audio contents are firstly classified as silence, pure speech, non-pure speech, music and background sound using SVMs. Then non-pure speech is further classified into song and other non-pure speechusing SVM. and background sound is classified into laughter, applause, scream and others using Hidden Markov Models (HMMs). For video contents, motion level and blur degree are acquired. Finally, video segments containing special effects, such as speech, laughter, song, applause, scream, and specified motion level and blur degree, are extracted as the main parts of the abstract. The remaining parts of the abstract are generated using key frame infonllation The experimental results show that the proposed algorithm can extract desired parts of home video to generate satisfactory video abstracts.

An approach to user-assisted video object extraction at pixellevel framework that aims at reducing users' workload while making full use of user interaction. The framework takes over multi-pass scan on a video sequence and bidirectional tracking on the sequence's sub-shots during each pass. Systems based on this framework can not only free users from laborious work, but also be allowed to embed various complex but effective tracking algorithms. To fulfill a system, we propose an extended Intelligent Scissors for machine to precisely track VOs. The approach to tracking is a multi-level process, locating VO boundaries from a coarse level to a fine one and yielding pixelaccurate results of extraction. Experiments on MPEG test sequences show that this approach together with the proposed framework performs well in practice.

A new method is proposed for real-time video object segmentation from static background under shadows and varying lighting. The method firstly learns a statistical model and a type model for a background scene in HSV space. The latter one represents the reflection characteristic of object surface in background and helps decide whether hue or saturation component should be used to detect changes in the statistical model. In the stage of change detection, multi-criteria decision is taken in order to deal with various lightness conditions such as shadows. At last a speedup morphologic filter and a connected components labeling algorithm will refine segmentation results. The method makes full and better use of color information in images. The capability of accurate segmentation and real-time execution is demonstrated for several test and practical video sequences.