There are many unavoidable noise interferences in image acquisition and transmission. To make it better for subsequent processing, the noise in the image should be removed in advance. There are many kinds of image noises, mainly including salt and pepper noise and Gaussian noise. This paper focuses on the research of the Gaussian noise removal. It introduces many wavelet threshold denoising alg...

The motivating application for this research is the problem of recognizing a planar object consisting of points from a noisy observation of that object. Given is a planar Gaussian mixture model ρT (x) representing an object along with a noise model for the observation process (the template). Also given are points representing the observation of the object (the query). We propose a method to det...

We introduce a flexible class of mixture models for clustering and density estimation. Our model allows clustering of non-linearly-separable data, produces a potentially low-dimensional latent representation, automatically infers the number of clusters, and produces a density estimate. Our approach makes use of two tools from Bayesian nonparametrics: a Dirichlet process mixture model to allow a...

This paper describes a flexible non-Gaussian statistical method used to model polarimetric synthetic aperture radar (POLSAR) data. We outline the theoretical basis of the well-know product model as described by the class of Scale Mixture models and discuss their appropriateness for modelling radar data. The statistical distributions of several Scale mixture models are then described, including ...

This paper proposes the use of Gaussian Mixture Models to estimate conditional probability density functions in an environmental risk mapping context. A conditional Gaussian Mixture Model has been compared to the geostatistical method of Sequential Gaussian Simulations and shows good performances in reconstructing local PDF. The data sets used for this comparison are parts of the digital elevat...

Estimators derived from a divergence criterion such as φ−divergences are generally more robust than the maximum likelihood ones. We are interested in particular in the so-called minimum dual φ–divergence estimator (MDφDE), an estimator built using a dual representation of φ–divergences. We present in this paper an iterative proximal point algorithm that permits the calculation of such an estima...

A problem of using mixture-of-Gaussian models for unsupervised texture segmentation is that “multimodal” textures (such as can often be encountered in natural images) cannot be well represented by a single Gaussian cluster. We propose a divide-andconquer method that groups together Gaussian clusters (estimated via Expectation Maximization) into homogeneous texture classes. This method allows to...

In this paper we present a method for performing discriminative human pose estimation using a mixture of Gaussian Processes appearance model to map directly from the image features to the multi-model pose distribution. In order to obtain a pose estimate for a sequence of frames, we introduce a dynamic programming algorithm for inferring a smooth pose sequence from the multi-model distribution g...

This paper concerns the problem of estimating linear functionals of the mixing distribution from Poisson mixture observations. In particular, linear functionals for which parametric rate of convergence cannot be achieved are studied. It appears that Gaussian functionals are rather easy to estimate. Estimation of the distibution functions is then considered by approximating this functional by Ga...