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We show in this paper how an Infinite Mixture of Gaussians model can be used to estimate/denoise non-Gaussian data with local linear estimators based on the Wiener filter. The decomposition of the data in Gaussian components is straightforwardly computed with the Gaussian Transform, previously derived in [2]. The estimation is based on a two-step procedure, the first step consisting in variance estimation,...
A novel method for spectral-domain fundamental frequency (F0) estimation is proposed. The basis of this method is estimating F0 using the power spectrum of a windowed speech segment. For this purpose, a new transform is introduced. The prominent feature of this transform is that it estimates F0 from the speech segment power spectrum by exploiting the window function power spectrum. As a result, this...
Probability Density Functions defined on IR+ can be successfully modeled with the help of the Mellin Transform : this rather underrated transform is well suited for such functions so that we propose the new definitions of "second kind" characteristic functions based on this transform. By this way, second kind moments and second kind cumulants can also be defined, so that multiplicative noise,...
We compare the objective functions used by GR2T [1] and the L2E estimator [2] that have both been proposed for robust parameter estimation. We show their similarity when estimating location parameters. Of particular interest is their ability for dealing with the scale parameter that is often unknown and acts as a nuisance parameter. Both techniques are tested experimentally for regression (e.g. to...
Recently a new geoacoustic inversion method was introduced in the works [1, 2] for the approximately range-independent shallow-water waveguides. It allows estimation of the acoustical parameters of the seabed (sound speed, density, etc) and the range from the source to the receiver using the recording of the pulse signal by a single hydrophone. This method is based on the use of the so-called warping...
The widespread use of hand held devices having video recording capabilities has made video capture prevalent. However, unwanted hand motion during capture introduces jerks which hampers the video viewing experience. In this paper, a robust method for global motion estimation between frames is presented. The algorithm estimates the rotation, scale and translation parameters between two adjacent frames...
Spacial variability of Received Signal Strength measurements (RSS) is the fluctuation of measured RSS at a fixed location in difference time, which is the main reason for low accuracy and bad robustness of some existing indoor localization techniques. In this paper, we proposed a novel approach to eliminate space variability of RSS in indoor localization. The merits of our proposed method is twofold...
The traditional way of using Hough Transform with SIFT is for the purpose of reliable object recognition. However, it cannot be effectively applied to image registration in the same way as the recall rate can be significantly lower. In this paper, we propose an alternative implementation of Hough Transform that can be used with Improved Symmetric-SIFT for multi-modal image registration. Our experimental...
We aim to match two hypergraphs via pairwise characterization of multiple relationships. To this end, we introduce a technique referred to as Marginalized Constrained Compatibility Estimation (MCCE), which transforms the compatibility tensor representing hyper-edge similarities into a compatibility matrix representing edge similarities. We then cluster graph vertices associated with the compatibility...
The success of restoring images degraded by motion blur highly depends on precise estimation of parameters such as motion direction and length that were involved in the motion Point Spread Function (PSF). This paper presents a new method for determination of linear motion point spread function for automatic restoration of an image. The method assumes a spatially invariant linear blur over the image...
We consider nonlinear, or "event-dependent", sampling, i.e. such that the sampling instances {tk} depend on the function being sampled. The use of such sampling in the construction of Lebesgue's integral sums is noted and discussed as regards physical measurement and also possible nonlinearity of singular systems. Though the limit of the sums, i.e. Lebesgue's integral, is linear with regard...
The period of Arnold transformation is a key parameter, which affects its application effectiveness in the fields of image encryption, digital watermark, information hiding, etc. However, the previous research results on the period of discrete two-dimensional Arnold transformation are very rough estimation and lack of practical application value. By analyzing the relationship between the image order...
A method is proposed to process registered images to reduce the effects of registration noise in change detection. The proposed method is based on the pixel-level misregistration map and the gradients of the registered image. Thin plate spline (TPS) transform is selected to estimate the misregistration of each pixel using available tie points. For each pixel, the compensation is composed of two parts:...
This paper studied the localization problem for a rescue robot based on laser scan matching and extended Kalman filtering (EKF). Scan matching method based on normal distribution transform (NDT) can avoid hard feature extraction problem by estimation of the probability distribution of laser scan data and localization can be achieved using correlation of the NDT. Based on NDT scan matching, the NDT-EKF...
In this paper, we propose a new method for image registration based on multilevel b-splines. We improve the transformation estimation by introducing some constraints in the general scheme that regularize the grid of control points. Our method is computationally effective and results in smooth transformations that are representative of the motion between the images. Comparative results are presented...
In this paper we present a novel approach for estimating feature-space maximum likelihood linear regression (fMLLR) transforms for full-covariance Gaussian models by directly maximizing the likelihood function by repeated line search in the direction of the gradient. We do this in a pre-transformed parameter space such that an approximation to the expected Hessian is proportional to the unit matrix...
This paper presents a new method for container auto-landing system using stereo vision. The position estimation of the spreader is very important for improving the operating efficiency of the port. A central problem in estimation of container position is that it is difficult to satisfy both the computation time problem and accuracy at the same time. To resolve this problem, we propose detection of...
This paper presents a new over complete ICAMM to make decomposition the basis for low-bit high-speed image compression on image sub-blocks. Although the over complete independent component analysis basic number is much larger than the original data dimension, most of the coefficients will be zero after performing the basis transform so that this algorithm reaches the low bit-rate compression goal...
In this paper, we propose the estimation method for the image affine information for computer vision. The first estimation method is given based on the XYS image normalization and the second estimation method is based on the image normalization by Pei and Lin. The XYS normalization method turns out to have better performance than the method by Pei and Lin. In addition, we show that rotation and aspect...
In this paper, we introduce a method to estimate the object's pose from multiple video cameras. We derive a centralized solution to pose estimation from multiple video cameras by solving a general matrix equation. Moreover, we provide an equivalent distributed solution to the pose estimation problem based on the independent pose estimation obtained from each camera. We demonstrate that both methods...
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