Mixture of Gaussians

ó The use of autonomous pan-tilt cameras as opposed to static cameras can dramatically enhance the range and effectiveness of surveillance systems, but effective tracking in such pan-tilt scenarios remains a challenge. Existing approaches for constructing mosaiced background models require accurate camera motion parameters, and online updates for the back- ground model in the presence of scene activity, as well

This paper presents a new enhanced text extraction algorithm from degraded document images on the basis of the probabilistic models. The observed document image is considered as a mixture of Gaussian densities which represents the foreground and background document image components. The EM algorithm is introduced in order to estimate and improve the parameters of the mixtures of densities recursively. The initial parameters of the EM algorithm are estimated by the k-means clustering method. After the parameter estimation, the document image is partitioned into text and background classes by the means of ML approach. The performance of the proposed approach is evaluated on a variety of degraded documents comes from the collections of the National library of Tunisia.

This paper describes a low bit-rate segmental for-mant vocoder. The formants are estimated using mixture of Gaussians whose means are constrained to vary linearly with time within a segment. A new method of smoothing the power spectrum has been used in order to ...

Financial time series analysis deals with the understanding of data collected on financial markets. Several parametric distribution models have been entertained for describing, estimating and predicting the dynamics of financial time series. Alternatively, this article considers a Bayesian semiparametric approach. In particular, the usual parametric distributional assumptions of the GARCH-type models are relaxed by entertaining the class of location-scale mixtures

This work proposes a new corner detector, which
uses a binary image as input. This is based on computing the
weight of the foreground and the background in a local
neighborhood. The weight distribution is given by a
convolution kernel, which is built from the sum of two
concentric Gaussians. The kernel works on a multiscale
fashion, and it does not suffer from the flattening effect.
Consequently there are no localization problems and, more
important, it allows an ordered relationship between weights
and angles. Since the neighborhoods centered at the boundary
forms an angular configuration between foreground and
background, the angles (corners) can be represented by the
weights of that configuration. The outputs of the proposed
method are confronted to a ground-truth found in the
literature, and to the methods that used the same set of images.

This paper deals with the problem of blind separation of an instantaneous mixture of Gaussian autoregressive sources, without additive noise, by the exact maximum likelihood approach. The maximization of the likelihood function is divided, using ...

In most solutions to state estimation problems like, for example, target tracking, it is generally assumed that the state evolution and measurement models are known a priori. The model parameters include process and measurement matrices or functions as well as the corresponding noise statistics. However, there are situations where the model parameters are not known a priori or are known only partially (i.e., with some uncertainty). Moreover, there are situations where the measurement is biased. In these scenarios, standard estimation algorithms like the Kalman filter and the extended Kalman Filter (EKF), which assume perfect knowledge of the model parameters, are not accurate. In this paper, the problem with uncertain model parameters is considered as a special case of maximum likelihood estimation with incomplete-data, for which a standard solution called the expectation-maximization (EM) algorithm exists. In this paper a new extension to the EM algorithm is proposed to solve the more general problem of joint state estimation and model parameter identification for nonlinear systems with possibly non-Gaussian noise. In the expectation (E) step, it is shown that the best variational distribution over the state variables is the conditional posterior distribution of states given all the available measurements and inputs. Therefore, a particular type of particle filter is used to estimate and update the posterior distribution. In the maximization (M) step the nonlinear measurement process parameters are approximated using a nonlinear regression method for adjusting the parameters of a mixture of Gaussians (MofG). The proposed algorithm is used to solve a nonlinear bearing-only tracking problem similar to the one reported recently with uncertain measurement process. It is shown that the algorithm is capable of accurately tracking the state vector while identifying the unknown measurement dynamics. Simulation results show the advantages of the new technique over standard algorithms like the EKF whose performance degrades rapidly in the presence of uncertain models.

Mixture of Gaussians (MoG) model is a useful tool in statistical learning. In many learning processes that are based on mixture models, computational requirements are very demanding due to the large number of components involved in the model. We propose a novel algorithm for learning a simplified representation of a Gaussian mixture, that is based on the Unscented Transform which was introduced for filtering nonlinear dynamical systems. The superiority of the proposed method is validated on both simulation experiments and categorization of a real image database. The proposed categorization methodology is based on modeling each image using a Gaussian mixture model. A category model is obtained by learning a simplified mixture model from all the images in the category.

In this paper we present a novel approach to robust visual servoing. This method removes the feature track- ing step from a typical visual servoing algorithm. We do not need correspondences of the features for deriving the control signal. This is achieved by modeling the image features as a Mixture of Gaussians in the current as well as desired images. Using Lyapunov theory, a control signal is derived to minimize a distance function between the two Gaussian mixtures. The distance function is given in a closed form, and its gradient can be efficiently computed and used to control the system. For simplicity, we first consider the 2D motion case. Then, the general case is presented by introducing the depth distribution of the features to control the six degrees of freedom. Experiments are conducted within a simulation framework to validate our proposed method. Visual servo control or visual servoing is the process of positioning a robot end effector with respect to an object or a set ...

In this paper, we propose a generalized group-wise non-rigid registration strategy for multiple unlabeled point-sets of unequal cardinality, with no bias toward any of the given point-sets. To quantify the divergence between the probability distributions--specifically Mixture of Gaussians--estimated from the given point sets, we use a recently developed information-theoretic measure called Jensen-Renyi (JR) divergence. We evaluate a closed-form JR divergence between multiple probabilistic representations for the general case where the mixture models differ in variance and the number of components. We derive the analytic gradient of the divergence measure with respect to the non-rigid registration parameters, and apply it to numerical optimization of the group-wise registration, leading to a computationally efficient and accurate algorithm. We validate our approach on synthetic data, and evaluate it on 3D cardiac shapes.

This paper proposes a robust ear identification system which is developed by fusing SIFT features of color segmented slice regions of an ear. The proposed ear identification method makes use of Gaussian mixture model (GMM) to build ear model with mixture of Gaussian using vector quantization algorithm and K-L divergence is applied to the GMM framework for recording the color similarity in the specified ranges by comparing color similarity between a pair of reference ear and probe ear. SIFT features are then detected and extracted from each color slice region as a part of invariant feature extraction. The extracted keypoints are then fused separately by the two fusion approaches, namely concatenation and the Dempster-Shafer theory. Finally, the fusion approaches generate two independent augmented feature vectors which are used for identification of individuals separately. The proposed identification technique is tested on IIT Kanpur ear database of 400 individuals and is found to ach...

Several machine learning algorithms have been applied to the problem of static hand posture recognition. K-nearesr neighbor (KNN) performs very well in flexible posture recognition, but speed and memory requirements of the algorithm make it difficult to use in real time applications. In this paper we propose an approach to speed up the KNN without changing its behavior. We use the mixture of gaussians (MOG) to model the input training data and apply KNN on this model.