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An approach is proposed to extend bilateral filtering to the vector case so as to simultaneously take spectral and spatial information into account by using spectral distances and multivariate Gaussian functions. To simplify the determination of the parameters of the corresponding covariance matrix, the data vectors are transformed to eigenspace through principal component analysis (PCA). By locally...
Intra-predictive transforms are a kind of block-based transforms that can exploit both the intra- and inter-block correlations. This paper analyzes the coding gains of intra-predictive transforms for the Gaussian process. The tight upper bound of the coding gain is derived, which is shown to be better than both the discrete cosine transform and the Karhunen Loe??ve transform. The optimal intra-predictive...
This paper presents a low-cost tracking algorithm based on multiple multiple fragments, increasing robustness with respect to partial occlusions. Given the initial template representing the desired target, each pixel is classified into a different cluster based on a Mixture of Gaussians (MOG) model, and a set of disjoint fragments is created. The mean vector and covariance matrix of each fragment...
Although there are some recent characterizations of Multivariate Gauss Markov-Random Field (MGMRF) models, these are limited to cases where the interaction matrix coefficients are modeled with some special form. We extend the modeling and parameter estimation for the interaction matrix coefficients for a general anisotropic MGMRF. Although the MGMRF is a natural generalization of its univariate counterpart,...
The ultimate success of a human-robot-interface system depends on how accurately user control signals are classified. This paper is aimed at developing and testing a strategy to accurately classify human-robot control signals. The primary focus is on overcoming the dimensionality problem frequently encountered in the design of Gaussian multivariate signal classifiers. The dimensionality problem is...
We consider the distributed source coding system for L correlated Gaussian remote sources Xi, i = 1, 2,??????, L, where Xi, i = 1, 2, ??, L are L correlated Gaussian random variables. We deal with the case where each of L distributed encoders can not directly observe Xi but its noisy version Yi = Xi +Ni. Here Ni, i = 1,2,??????, L are independent additive L Gaussian noises also independent of Xi,...
In this paper, we study the problem of optimal trajectory generation for a team of mobile robots tracking a moving target using distance and bearing measurements. Contrary to previous approaches, we explicitly consider limits on the robots' speed and impose constraints on the minimum distance at which the robots are allowed to approach the target. We first address the case of a single sensor and show...
In this paper we study the performance of linear analog coding of multivariate Gaussian sources and compare it to the theoretical limits. A general performance analysis is presented for both random and optimal linear encoders. Simulation results show the agreement between the theoretical analysis and the practical implementation.
This paper describes an efficient method for retrieving the 3-dimensional shape associated to novelties in the environment of an autonomous robot, which is equipped with a laser range finder. First, changes are detected over the point clouds using a combination of the Gaussian mixture model (GMM) and the earth mover's distance (EMD) algorithms. Next, the shape retrieval is achieved using two different...
Voice conversion algorithm aims to provide high level of similarity to the target voice with an acceptable level of quality. The main object of this paper was to build a nonlinear relationship between the parameters for the acoustical features of source and target speaker using non-linear canonical correlation analysis (NLCCA) based on jointed Gaussian mixture model. Speaker individuality transformation...
The statistical description of an optimal sampling reconstruction procedure (SRP) of non stationary Gaussian fields is given on the basis of the conditional mean rule. The non stationarity of field is determined in space, along of one axis, but not in time. A new type of the spatial covariance function of non stationary Gaussian field is suggested. A non trivial example of SRP of non stationary Gaussian...
We propose a dimension reduction technique named Resilient Subclass Discriminant Analysis (RSDA) for high dimensional classification problems. The technique iteratively estimates the subclass division by embedding the Fisher Discriminant Analysis (FDA) with Expectation-Maximization (EM) in Gaussian Mixture Models (GMM). The new method maintains the adaptability of SDA to a wide range of data distributions...
In this paper, we propose a new image representation to capture both the appearance and spatial information for image classification applications. First, we model the feature vectors, from the whole corpus, from each image and at each individual patch, in a Bayesian hierarchical framework using mixtures of Gaussians. After such a hierarchical Gaussianization, each image is represented by a Gaussian...
Moving cast shadows are a major concern for foreground detection algorithms. In this paper, a novel method is proposed to detect the moving cast shadows in the scene. This approach uses GMM to generate the background image, and extract the feature using PCA-based transformation to the background image, then, the feature space is utilized to classify moving shadows and foreground objects. Experimental...
This paper realizes a text-independent, speaker verification system on a system on chip (SOC) platform. The system uses Mel-frequency cepstral coefficients (MFCC) features with a Gaussian mixture model-universal background model (GMM-UBM) speaker model. To deal with resource limitations, a new speaker-centric score normalization technique is introduced. This normalization technique results in a relative...
This paper studies the design problem for the multi response linear model with possible bias. It is assumed that the fitted model for each response is polynomial of degree up to two, and the model bias includes the effects due to higher degree terms of multivariate Hermite polynomials. A criterion for choosing designs is proposed based on averaging the mean squared error over all possible bias. Several...
Previously, the Generalized Likelihood Ratio Test - Linear Quadratic (GLRT-LQ) has been extended to the Multiple-Input Multiple-Output (MIMO) case where all transmit-receive subarrays are considered jointly as a system such that only one detection threshold is used. The new MIMO detector is Constant False Alarm Rate (CFAR) with respect to the clutter power fluctuations. In this paper, the adaptive...
We investigate the sudden onset of failure in maximum-likelihood (ML) detection-estimation on multivariate Gaussian models with a critically small number of data samples (observations). Using methods from random matrix theory (RMT) [also known as generalised statistical analysis (GSA) or G-analysis], we demonstrate that, for any set of true (exact) data parameters, we can identify a parametric space...
This paper addresses the synchronization problem using an array of antennas in the general framework of global navigation satellite systems (GNSS) receivers. Although the pervasive approach to exploit spatial diversity is based on beamforming, we propose a statistical approach driven by the use of blocks of data, common in software radio receivers, in contrast to a sample-per-sample basis, typical...
Gaussian mixture model (GMM)-based vector quantization of line spectral frequencies (LSFs) has gained wide acceptance in speech coding. In predictive coding of LSFs, the GMM approach utilizing Kalman filtering principles to account for quantization noise has been shown to perform better than a baseline GMM recursive coder approaches for both clean and packet loss conditions at roughly the same complexity...
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