Background modeling of video frame sequences is a prerequisite for computer vision applications. Robust principal component analysis(RPCA), which aims to recover low rank matrix in applications data mining and machine learning, has shown improved background performance. Unfortunately, The traditional RPCA method considers the batch recovery all samples, leads higher storage cost. This paper pro...

We develop two novel approaches to solving for the Laplace transform of a time-changed stochastic process. We discard the standard assumption that the background process (Xt) is Lévy. Maintaining the assumption that the business clock (Tt) and the background process are independent, we develop two different series solutions for the Laplace transform of the time-changed process X̃t = X(Tt). In fa...

Background Process models offer opportunities to explore the effectiveness of different program and policy alternatives by varying input behaviors and model parameters to reflect programmatic/policy effects. Methods Traditional theoretical HIV models set modeling parameters a priori and a small change in model parameters often gives widely varying results for the same behavioral inputs. The Asi...

This paper presents a new technique to extract objects from a real complex background so that a video sequence can be decomposed into a set of objects as required for object oriented video compression techniques. The proposed method is based on a background subtraction technique. However, instead of using a fixed background, the system relies on predicting one from a previously constructed virt...

Spatial and spatio-temporal processes are often described with a Gaussian process model. This model can be represented as a convolution of a white noise process and a smoothing kernel. We expand upon this model by considering convolutions of non-iid background processes. We highlight two particular models based on convolutions of Markov random fields and of time-varying processes. These models ...

The rapid increase of multidimensional data (a.k.a. tensor) like videos brings new challenges for low-rank data modeling approaches such as dynamic data size, complex high-order relations, and multiplicity of low-rank structures. Resolving these challenges require a new tensor analysis method that can perform tensor data analysis online, which however is still absent. In this paper, we propose ...