We investigate the contribution of local spatio-temporal variation of image intensity to saliency. To measure different types of variation, we use the geometrical invariants of the structure tensor. With a video represented in spatial axes x and y and temporal axis t, the ndimensional structure tensor can be evaluated for different combinations of axes (2D and 3D) and also for the (degenerate) ...

The AEGISS (Ascertainment and Enhancement of Disease Surveillance and Statistics) project uses spatio-temporal statistical methods to identify anomalies in the incidence of gastrointestinal infections in the UK. The focus of this paper is the modelling of temporal variation in incidence using data from the Southampton area in southern England. We identified and fitted a hierarchical stochastic ...

Many studies in the last few years have been dedicated to increasing temperatures and extreme heat Europe since second half of 20th century because their adverse effects on ecosystems resilience, human health, quality life. The present research aims analyze spatio-temporal variations events Southeastern using daily temperature data from 70 selected meteorological stations applying methodology d...

Random encounter models can be used to estimate population abundance from indirect data collected by non-invasive sampling methods, such as track counts or camera-trap data. The classical Formozov-Malyshev-Pereleshin (FMP) estimator converts track counts into an estimate of mean population density, assuming that data on the daily movement distances of the animals are available. We utilize gener...

Natural-abundance stable isotope ratios provide a wealth of ecological information relating to food web structure, trophic level, and location. The correct interpretation of stable isotope data requires an understanding of spatial and temporal variation in the isotopic compositions at the base of the food web. In marine pelagic environments, accurate interpretation of stable isotope data is ham...

According to recent works, introduced by Y.Meyer [1] the decomposition models based on Total Variation (TV) appear as a very good way to extract texture from image sequences. Indeed, videos show up characteristic variations along the temporal dimension which can be catched in the decomposition framework. However, there are very few works in literature which deal with spatio-temporal decompositi...

OBJECTIVE To illustrate the ability of hierarchical Bayesian spatio-temporal models in capturing different geo-temporal structures in order to explain hospital risk variations using three different conditions: Percutaneous Coronary Intervention (PCI), Colectomy in Colorectal Cancer (CCC) and Chronic Obstructive Pulmonary Disease (COPD). RESEARCH DESIGN This is an observational population-base...