This paper presents a new framework for segmentation of textured visual imagery. The proposed method consists of a Bayesian formulation for labeling similar regions. Similarity is defined via texture features obtained by Gabor Wavelets. Multivariate Gaussian distributions are employed to model the feature class-conditional densities, while the Markov process is used to characterize the distribu...

This paper introduces Bayesian supervised and unsupervised segmentation algorithms aimed at oceanic segmentation of SAR images. The data term, i.e., the density of the observed backscattered signal given the region, is modeled by a finite mixture of Gamma densities with a given predefined number of components. To estimate the parameters of the class conditional densities, a new expectation maxi...

In this paper, we firstly give a brief introduction of expectation maximization (EM) algorithm, and then discuss the initial value sensitivity of expectation maximization algorithm. Subsequently, we give a short proof of EM's convergence. Then, we implement experiments with the expectation maximization algorithm (We implement all the experiments on Gaussion mixture model (GMM) ). Our experiment...

The existing maximum likelihood theory and its computer software in structural equation modeling are established on the basis of linear relationships among latent variables with fully observed data. However, in social and behavioral sciences, nonlinear relationships among the latent variables are important for establishing more meaningful models and it is very common to encounter missing data. ...

We study the problem of incorporating risk while making combinatorial decisions under uncertainty. formulate a discrete submodular maximization for selecting set using conditional value at (CVaR), metric commonly used in financial analysis. While CVaR has recently been optimization linear cost functions robotics, we take first step toward extending this to and provide several positive results. ...

To tackle the co-clustering problem on categorical data, we consider a spectral approach. We first define a generalized modularity measure for the co-clustering task. Then, we reformulate its maximization as a trace maximization problem. Finally we develop a spectral based co-clustering algorithm performing this maximization. The proposed algorithm is then capable to cluster rows and colunms si...

A Two-phase model for configuring a cellular manufacturing system is proposed. In phase (I), for the first time, number of cells is considered as a decision variable. In phase (II), pursing two different objectives, one minimization of underload and the other, maximization of similarity of parts within a group, the design procedure is performed. As one cannot have these two objects together, a ...

The theory of distributed detection is receiving a lot of attention. A common assumption used in previous studies is the conditional independence of the observations. In this paper, the optimization of local decision rules for distributed detection networks with correlated observations is considered. We focus on presenting the detection theory for parallel distributed detection networks with fi...

We propose Action-Reaction Learning as an approach for analyzing and synthesizing human behaviour. This paradigm uncovers causal mappings between past and future events or between an action and its reaction by observing time sequences. We apply this method to analyze human interaction and to subsequently synthesize human behaviour. Using a time series of perceptual measurements, a system automa...

We consider the problem of maximizing certain positive rational functions of a form that includes statistical constructs such as conditional mixture densities and conditional hidden Markov models. The wellknown Baum-Welch and expectation maximization (EM) algorithms do not apply to rational functions and are therefore limited to the simpler maximum-likelihood form of such models. Our main resul...