The problem of protecting computer systems can be viewed in part as the problem of distinguishing self from other. We describe a method for accomplishing this which is based on the way natural immune systems generate T-cells. The two-phase algorithm rst generates a set of detectors that do not match self. In the second phase, these detectors are used to monitor self and if a match is ever found...

A decentralized formulation of the quickest change detection problem is studied, where the distributions of the observations at all of the sensors in the system change at the time of disruption, and the sensors communicate with a common fusion center. A Bayesian setting is considered in which a priori knowledge of the change time distribution is available. The observations are assumed to be ind...

The problem of detecting changes in the statistical properties of a stochastic system and time series arises in various branches of science and engineering. It has a wide spectrum of important applications ranging from machine monitoring to biomedical signal processing. In all of these applications the observations being monitored undergo a change in distribution in response to a change or anom...

Judgmental detection of changes in time series is an ubiquitous task. Previous research has shown that human observers are often relatively poor at detecting change, especially when the series are serially dependent (autocorrelated). We present two experiments in which participants were asked to judge the occurrence of changes in time series with varying levels of autocorrelation. Results show ...

Moving objects in image sequences acquired by a static camera can be detected by analyzing the grey–level difference between successive frames. Direct motion detection, however, will also detect fast variations of scene illumination. This paper describes a method for motion detection that is considerably less sensitive to time–varying illumination. It is based on combining a motion detection al...

In sequential change detection, existing performance measures differ significantly in the way they treat the time of change. By modeling this quantity as a random time, we introduce a general framework capable of capturing and better understanding most well-known criteria and also propose new ones. For a specific new criterion that constitutes an extension to Lorden’s performance measure, we of...

The increased presence of digital multimedia in numerous applications, such as security, surveillance, the semantic web, has rendered the automated characterization of video content necessary. The localization of different activities/events in video content is of particular interest, however it is quite challenging to achieve in a principled manner and with no prior knowledge or training. This ...

In the present paper, we develop a new efficient approach to detection of abrupt changes in a sequence of recognized digital images, which can be applied to a wide variety of practical problems of digital image processing. The principal idea of this approach consists in transforming an original sample of the observed data to a set of onedimensional statistics and measuring a distance from homog...

We present a method for quantifying and localising changes in two facial scans of the same person taken at two different time instants. The method is based on rigid registration and semantic feature extraction, followed by discrepancy computation. The proposed method combines the Landmark Transform (LT) method, which is applied on semantic feature points, and the Iterative Closest Point (ICP) a...

We tackle the change-point problem with data belonging to a general set. We propose a penalty for choosing the number of change-points in the kernel algorithm of Harchaoui and Cappé (2007). This penalty generalizes the one proposed for one dimensional signals by Lebarbier (2005). We prove it satisfies a non-asymptotic oracle inequality by showing new concentration results in Hilbert spaces. Exp...