Traditional video surveillance takes a huge amount of space storage. Recording everything captured by a surveillance camera consumes the storage devices used by the system. Extracting useful and meaningful information from surveillance videos is a time consuming process due to the long time of the recorded videos. These drawbacks limit the effectiveness of traditional video surveillance systems. In this paper, we propose and elaborate on a compression method which investigates the fact that surveillance videos may last for a long time with no changes in the scene it monitors. Using this fact, a new compression technique that reduces the size of the videos dramatically was developed. We also present a dataset for low quality surveillance videos which can be used by researchers for applying different algorithms and techniques in the field of surveillance videos.

Traditional video surveillance takes a huge amount of space storage. Recording everything captured by a surveillance camera consumes the storage devices used by the system. Extracting useful and meaningful information from surveillance videos is a time consuming process due to the long time of the recorded videos. These drawbacks limit the effectiveness of traditional video surveillance systems. In this paper, we propose and elaborate on a compression method which investigates the fact that surveillance videos may last for a long time with no changes in the scene it monitors. Using this fact, a new compression technique that reduces the size of the videos dramatically was developed. We also present a dataset for low quality surveillance videos which can be used by researchers for applying different algorithms and techniques in the field of surveillance videos.

Traditional video surveillance takes a huge amount of space storage. Recording everything captured by a surveillance camera consumes the storage devices used by the system. Extracting useful and meaningful information from surveillance videos is a time consuming process due to the long time of the recorded videos. These drawbacks limit the effectiveness of traditional video surveillance systems. In this paper, we propose and elaborate on a compression method which investigates the fact that surveillance videos may last for a long time with no changes in the scene it monitors. Using this fact, a new compression technique that reduces the size of the videos dramatically was developed. We also present a dataset for low quality surveillance videos which can be used by researchers for applying different algorithms and techniques in the field of surveillance videos.

Different types of e-assessment systems that are recognized at universities and based on the campus wireless have been developed. These systems help the students to use their Mobile Phones as learning media to access the information more easily from anywhere and at any time. Seppala and Alamaki developed a mobile learning project for teacher training. Their study compared the effectiveness of internet, face-to-face and mobile based instructions. Al Masri has proposed a study to compare the effective strategy in paper-based assessment with mobile-based assessment for assessing university students in English literature. It has been found that students gained better scores in mobile phone-based test than in paper-based test. This paper aims to determine and measure the effects of mobile-based assessments on the perception, achievement levels and performance of the students in internet-assisted courses. The main functionalities and features of this paper are: Knowledge evaluation, automatic generation of exams, exam grading, communication, course management, and questions-bank database.

This paper examines the problem of white-balance correction when a scene contains two illuminations. This is a two step process: 1) estimate the two illuminants; and 2) correct the image. Existing methods attempt to estimate a spatially varying illumination map, however, results are error prone and the resulting illumination maps are too low-resolution to be used for proper spatially varying white-balance correction. In addition, the spatially varying nature of these methods make them computationally intensive. We show that this problem can be effectively addressed by not attempting to obtain a spatially varying illumination map, but instead by performing illumination estimation on large sub-regions of the image. Our approach is able to detect when distinct illuminations are present in the image and accurately measure these illuminants. Since our proposed strategy is not suitable for spatially varying image correction, a user study is performed to see if there is a preference for how the image should be corrected when two illuminants are present, but only a global correction can be applied. The user study shows that when the illuminations are distinct, there is a preference for the outdoor illumination to be corrected resulting in warmer final result. We use these collective findings to demonstrate an effective two illuminant estimation scheme that produces corrected images that users prefer.

NULL

NULL

NULL

NULL

This paper try to show that Von Neumann model is a more robust, useful and flexible tool than many have been realized. It tries to show that the tools of Von Neumann model are useful enough to understand the present behavior of the economy of computer industry. The paper elaborates over and explains the Von Neumann mathematical model. It shows when and how Von Neumann stationary equilibrium state will happen. It drew useful conclusions. It opens the door to further investigations on the same direction.