Abstract— A common method for real-time segmentation of moving regions in image sequences involves “background subtraction,” or thresholding the error between an estimate of the image without moving objects and the current image. The numerous approaches to this problem differ in the type of background model used and the procedure used to update the model. The latest being the one using fuzzy color histogram. Traditional background modelling and subtraction methods have a strong assumption that the scenes are of static structures with limited perturbation. Background subtraction is a very popular approach for detecting moving objects from a still scene. For this, most of previous methods depend on the assumption that the background is static over short time periods. However, structured motion patterns of the background (e.g., waving leaves, spouting fountain, rippling water, etc.), which are distinctive from variations due to noise, are hardly tolerated in this assumption and thus still lead to high-level false positive rates when using previous models. In this paper five different methods is being described.

Abstract— In this paper we will prove two problems : 1) Beal Conjecture 2) Fermat’s Last Theorem First we will show Beal Conjecture is true. Then we will show Fermat’s Last Theorem is true.

Abstract— In this paper we will show how natural number series can be formed by prime numbers when 1,2 is given and operator multiplication is given. We will then prove Legendre and Grimm’s Conjecture.

Abstract— E-learning is the new means which has the potential to support the traditional education system. E-learning is gaining popularity and is considered as a viable and efficient means of content delivery and student engagement for many courses in higher education institutions. Due to the affordability and availability of Internet enabled computing devices and developments in ICT, every learner has the opportunity to improve her understanding and enhance her educational qualifications. In this paper we propose a conceptual system architecture that can take the advantage of Cloud-based computing facilities and provide the online learning aids in the form of audio, video, animation, and simulation applications. We also discuss some of the factors that will have an impact of such an initiative with respect to the higher education in India.

Abstract— Several classes of denoising algorithms such as total variation (TV) , wavelets and nonlocal means have all achieved much success. These algorithms are based on different theories, and all show good performance in denoising. When denoising an image, the TV method makes use of the geometric features of the image, the wavelet method makes use of the statistical features of the coefficients, and the nonlocal means method makes use of the redundancy in the image texture features. However, the features that have been used by these methods all come from the noisy image itself. In fact, the image features acquired by other sensors from the same scene can also be used as priors in denoising. In many situations involving multicomponent remotesensing images, a single-component image with a higher SNR or higher spatial resolution is often available. In the past, such an auxiliary image was applied for fusion with a multispectral image to improve its spatial resolution. In fact, the auxiliary noise-free image is a more suitable aid to denoising. In an auxiliary image as a prior is used to assist in denoising or deblurring the image, but this is not suitable for a remote-sensing image, and the auxiliary noisy image must come from the same sensor. There has been some research on denoising based on such an auxiliary image in the remotesensing field. The goal of our approach is denoising. In this letter, following the ideas and, the correlation between the different bands of multicomponent images is used. The auxiliary image as the prior is introduced into the TV or partial differential equation (PDE) denoising method. Moreover, the auxiliary image is applied in the form of a “noise-free” single-component image (no image is completely noise-free and by “noise-free” mean “with a high SNR”).