Subspace learning methods are widely used in background modeling to tackle illumination changes. Their main advantage is that it doesn’t need to label data during the training and running phase. Recently, White et al. [1] have shown that a supervised approach can improved significantly the robustness in background modeling. Following this idea, we propose to model the background via a supervise...

In this paper we propose a methodology for minimizing the variance of a cube (mosaicked) representation of a scene imaged by a pan-tilt camera. The minimization is based on the estimation of the vignetting image distortion, using the pan and tilt degrees of freedom instead of color calibrating patterns. Experiments with real images show that variance minimization is effective for improving even...

Background modeling and subtraction is a core component in motion analysis. The central idea behind such module is to create a probabilistic representation of the static scene that is compared with the current input to perform subtraction. Such approach is efficient when the scene to be modeled refers to a static structure with limited perturbation. In this paper, we address the problem of mode...

Modeling background and segmenting moving objects are significant techniques for video surveillance and other video processing applications. In this paper, we proposed a novel adaptive approach for modeling background and segmenting moving objects with a non-parametric kernel density estimation. Unlike previous approaches to object detection that detect objects by global thresholds, we used a l...

Background modeling is an important issue in video surveillance. A sophisticated and adaptive background model can be used to detect moving objects which are segregated from the scene in each image frame of the video via the background subtraction process. Many background subtraction methods are proposed for video acquired by a stationary camera, assuming that the background exhibits stationary...

Xinyi Cui Facebook 1.

Keywords: Generative models Face model Face analysis Morphable Model Bayesian model Implicit background models a b s t r a c t Face image interpretation with generative models is done by reconstructing the input image as well as possible. A comparison between the target and the model-generated image is complicated by the fact that faces are surrounded by background. The standard likelihood form...