No abstract

Data security has consistently been a major issue in information technology. In the cloud computing environment, it becomes particularly serious because the data is located in different places even in all the globe. Data security and privacy protection are the two main factors of user's concerns about the cloud technology. Though many techniques on the topics in cloud computing have been investigated in both academics and industries, data security and privacy protection are becoming more important for the future development of cloud computing technology in government, industry, and business. Data security and privacy protection issues are relevant to both hardware and software in the cloud architecture. This study is to review different security techniques and challenges from both software and hardware aspects for protecting data in the cloud and aims at enhancing the data security and privacy protection for the trustworthy cloud environment. In this paper, we make a comparative research analysis of the existing research work regarding the data security and privacy protection techniques used in the cloud computing.

No abstract

Most of existing approaches on event detection in sports video are general audience oriented. The extracted events are then presented to the audience without further analysis. However, professionals, such as soccer coaches, are more interested in the tactics used in the events. In this paper, we present a novel approach to extract tactic information from the goal event in broadcast soccer video and present the goal event in a tactic mode to the coaches and sports professionals. We first extract goal events with far-view shots based on analysis and alignment of web-casting text and broadcast video. For a detected goal event, we employ a multiobject detection and tracking algorithm to obtain the players and ball trajectories in the shot. Compared with existing work, we proposed an effective tactic representation called aggregate trajectory which is constructed based on multiple trajectories using a novel analysis of temporal-spatial interaction among the players and the ball. The interactive relationship with play region information and hypothesis testing for trajectory temporal-spatial distribution are exploited to analyze the tactic patterns in a hierarchical coarse-to-fine framework. The experimental results on the data of FIFA World Cup 2006 are promising and demonstrate our approach is effective.

No abstract

Envelope methodology can provide substantial efficiency gains in multivariate statistical problems, but in some applications the estimation of the envelope dimension can induce selection volatility that may mitigate those gains. Current envelope methodology does not account for the added variance that can result from this selection. In this article, we circumvent dimension selection volatility through the development of a weighted envelope estimator. Theoretical justification is given for our estimator and validity of the residual bootstrap for estimating its asymptotic variance is established. A simulation study and an analysis on a real data set illustrate the utility of our weighted envelope estimator.

Graphics detection and recognition are fundamental research problems in document image analysis and retrieval. As one of the most pervasive graphical elements in business and government documents, logos may enable immediate identification of organizational entities and serve extensively as a declaration of a document's source and ownership. In this work, we developed an automatic logo-based document image retrieval system that handles: 1) Logo detection and segmentation by boosting a cascade of classifiers across multiple image scales; and 2) Logo matching using translation, scale, and rotation invariant shape descriptors and matching algorithms. Our approach is segmentation free and layout independent and we address logo retrieval in an unconstrained setting of 2-D feature point matching. Finally, we quantitatively evaluate the effectiveness of our approach using large collections of real-world complex document images.

In this paper, a novel multiple objects detection and tracking approach based on support vector machine and particle filter is proposed to track players in broadcast sports video. Compared with previous work, the contributions of this paper are focused on three aspects. First, an improved particle filter called SVR particle filter is proposed as the player tracker by integrating support vector regression (SVR) into sequential Monte Carlo framework. SVR particle filter enhances the performance of classical particle filter with small sample set and improves the efficiency of tracking system. Second, support vector classification combined with playfield segmentation is employed to automatically detect the players in sports video as the initialization of tracker. Third, a unified framework for automatic object detection and tracking is proposed based on support vector machine and particle filter. The experimental results are encouraging and demonstrate that our approach is effective.