This paper proposes a general Kernel-Bayesian framework for object tracking. In this framework, the kernel based method—mean shift algorithm is embedded into the Bayesian framework seamlessly to provide a heuristic prior information to the state transition model, aiming at effectively alleviating the heavy computational load and avoiding sample degeneracy suffered by the conventional Bayesian t...

A significant amount of academic research in criminology focuses on spatial and temporal event analysis. Although several efforts have integrated spatial and temporal analyses, most previous work focuses on the space-time interaction and space-time clustering of criminal events. This research expands previous work in geostatistics and disease clustering by using a Bayesian hierarchical framewor...

This paper studies two types of spatial relationships that can be learned from training examples for object recognition. The first one employs deformable relationships between object parts with a Gaussian model, while the second one describes pairwise relationships between pixel intensity values using Bayesian networks. We perform experiments on a human face dataset and a horse dataset, imposin...

We propose a two-way Bayesian vector spatial procedure incorporating dimension reparameterization with a variable selection option to determine the dimensionality and simultaneously identify the significant covariates that help interpret the derived dimensions in the joint space map. We discuss how we solve identifiability problems in a Bayesian context that are associated with the two-way vect...

Statistical approaches for inferring the spatial distribution of taxa (Species Distribution Models, SDMs) commonly rely on available occurrence data, which is often clumped and geographically restricted. Although available SDM methods address some of these factors, they could be more directly and accurately modelled using a spatially-explicit approach. Software to fit models with spatial autoco...

The human visual system seems to be capable of temporally integrating information in a video sequence in such a way that the perceived spatial resolution of a sequence appears much higher than the spatial resolution of an individual frame. This paper addresses how to utilize both the spatial and temporal information present in an image sequence to create a high-resolution video still. A novel o...

interest rate ad valorem tariffs are considered as the most prominent trade tools extensively used in the framework of spatial equilibrium models (sems) to analyze agricultural and food trade policies around the world. however the results obtained from such models have been criticized because of their inadequacy in producing any observed data within the base period. hence a positive spatial and...

In this paper we present a general formalism to model several levels of meta information on spatial content. In particular, we expand on a two-level procedure of Bayesian inference: model fitting and model selection. We study five cases of extraction of spatial information from remote sensing images using this scheme. In this way, we put together pieces of information obtained from different le...

Extreme value theory focuses on the study of rare events and uses asymptotic properties to estimate their associated probabilities. Easy availability of georeferenced data has prompted a growing interest in the analysis of spatial extremes. Most of the work so far has focused on models that can handle block maxima, with few examples of spatial models for exceedances over a threshold. Using a hi...