We present a model-based method for accurate extraction of pedestrian silhouettes from video sequences. Our approach is based on two assumptions, 1) there is a common appearance to all pedestrians, and 2) each individual looks like him/herself over a short amount of time. These assumptions allow us to learn pedestrian models that encompass both a pedestrian population appearance and the individ...

Background modeling is a common component in video surveillance systems and is used to quickly identify regions of interest. To increase the robustness of background subtraction techniques, researchers have developed techniques to update the background model and also developed probabilistic/statistical approaches for thresholding the difference. This paper presents an error analysis of this typ...

With polarization of the cosmic microwave background (CMB) now detected, and confirmed by several independent experiments, the next goal is to characterise accurately its statistical properties. In these lecture notes we review the physical motivation for pursuing CMB polarization, and the basic statistical properties of the polarization fields. We then discuss some of the key aspects of the an...

Factor analysis (FA) is a statistical technique similar to principal component analysis (PCA) for explaining the variance in a data set in terms of underlying linear factors. Unlike PCA, however, FA has not been widely exploited for face or object recognition. This paper explains the differences between PCA and FA, and confirms that PCA outperforms FA in a standard face recognition task. Howeve...