We present a parametric framework for the joint processing of heterogeneous data,
specifically for a binary classification problem. Processing such a data set is not …

We consider the problem of fusing probability scores from a set of classifiers to estimate a
final fused probability score. Our interest is in scenarios where the classifiers are statistically …

Hidden Markov chain (HMC) models have been widely used in unsupervised image
segmentation. In these models, there is a double process; a hidden one noted X and an …

This paper deals with the statistical restoration of hidden discrete signals, extending the
classical methodology based on hidden Markov chains. The aim is to take into account the …

This chapter introduces copula functions and the use of the Gaussian copula function to
model probabilistic dependencies in supervised classification tasks. A copula is a …

Parametric modeling and estimation of non-Gaussian multidimensional probability density
function is a difficult problem whose solution is required by many applications in signal and …

There has been a long and lively debate on whether rate covariance and temporal
coordination of spikes, regarded as potential origins for correlations in cortical spike signals …

Typical testors are a useful tool for both feature selection and for determining feature
relevance in supervised classification problems. Nowadays, generating all typical testors of …

The paper focuses on a flexible model of multidimensional probability density function (pdf)
dedicated to describe amplitude distribution of polarimetric SAR data. The model is based …

This paper deals with the joint modeling of color textures in the context of Content Based
Image Retrieval (CBIR). We propose a generic multivariate model based on the Generalized …