Automated segmentation of multiple sclerosis lesions by model outlier detection

This paper presents a fully automated algorithm for segmentation of multiple sclerosis (MS) lesions from multispectral magnetic resonance (MR) images. The method performs intensity-based tissue classification using a stochastic model for normal brain images and simultaneously detects MS lesions as outliers that are not well explained by the model. It corrects for MR field inhomogeneities, estimates tissue-specific intensity models from the data itself, and incorporates contextual information in the classification using a Markov random field. The results of the automated method are compared with lesion delineations by human experts, showing a high total lesion load correlation. When the degree of spatial correspondence between segmentations is taken into account, considerable disagreement is found, both between expert segmentations, and between expert and automatic measurements.     

Robust global motion estimation oriented to video object segmentation.

Most global motion estimation (GME) methods are oriented to video coding while video object segmentation methods either assume no global motion (GM) or directly adopt a coding-oriented method to compensate for GM. This paper proposes a hierarchical differential GME method oriented to video object segmentation. A scheme which combines three-step search and motion parameters prediction is proposed for initial estimation to increase efficiency. A robust estimator that uses object information to reject outliers introduced by local motion is also proposed. For the first frame, when the object information is unavailable, a robust estimator is proposed which rejects outliers by examining their distribution in local neighborhoods of the error between the current and the motion-compensated previous frame. Subjective and objective results show that the proposed method is more robust, more oriented to video object segmentation, and faster than the referenced methods.     

Accurate and robust reconstruction of a surface model of the proximal femur from sparse-point data and a dense-point distribution model for surgical navigation

Constructing a 3-D surface model from sparse-point data is a nontrivial task. Here, we report an accurate and robust approach for reconstructing a surface model of the proximal femur from sparse-point data and a dense-point distribution model (DPDM). The problem is formulated as a three-stage optimal estimation process. The first stage, affine registration, is to iteratively estimate a scale and a rigid transformation between the mean surface model of the DPDM and the sparse input points. The estimation results of the first stage are used to establish point correspondences for the second stage, statistical instantiation, which stably instantiates a surface model from the DPDM using a statistical approach. This surface model is then fed to the third stage, kernel-based deformation, which further refines the surface model. Handling outliers is achieved by consistently employing the least trimmed squares (LTS) approach with a roughly estimated outlier rate in all three stages. If an optimal value of the outlier rate is preferred, we propose a hypothesis testing procedure to automatically estimate it. We present here our validations using four experiments, which include 1) leave-one-out experiment, 2) experiment on evaluating the present approach for handling pathology, 3) experiment on evaluating the present approach for handling outliers, and 4) experiment on reconstructing surface models of seven dry cadaver femurs using clinically relevant data without noise and with noise added. Our validation results demonstrate the robust performance of the present approach in handling outliers, pathology, and noise. An average 95-percentile error of 1.7-2.3 mm was found when the present approach was used to reconstruct surface models of the cadaver femurs from sparse-point data with noise added.     

MPEG-2压缩域中鲁棒性摄像机运动估计算法的研究

在视频检索的很多应用中,比如对象轨迹追踪,都需要首先分离摄像机运动。现提出一种MPEG-2压缩域中鲁棒性摄像机运动估计——自适应尺度残差一致性ASRC（Adaptive-Scale Residual Consensus）算法,只使用P帧的运动向量,并对多重结构噪声可达到80%的击穿点,使MPEG矢量场中奇异值的影响降到最小。对比经典LMedS估计,提出的ASRC具有更好的鲁棒性和击穿点。实验结果显示出令人满意的效果。     

Image registration under illumination variations using region-based confidence weighted M-estimators

We present an image registration model for image sets with arbitrarily shaped local illumination variations between images. Any nongeometric variations tend to degrade the geometric registration precision and impact subsequent processing. Traditional image registration approaches do not typically account for changes and movement of light sources, which result in interimage illumination differences with arbitrary shape. In addition, these approaches typically use a least-square estimator that is sensitive to outliers, where interimage illumination variations are often large enough to act as outliers. In this paper, we propose an image registration approach that compensates for arbitrarily shaped interimage illumination variations, which are processed using robust M -estimators tuned to that region. Each M-estimator for each illumination region has a distinct cost function by which small and large interimage residuals are unevenly penalized. Since the segmentation of the interimage illumination variations may not be perfect, a segmentation confidence weighting is also imposed to reduce the negative effect of mis-segmentation around illumination region boundaries. The proposed approach is cast in an iterative coarse-to-fine framework, which allows a convergence rate similar to competing intensity-based image registration approaches. The overall proposed approach is presented in a general framework, but experimental results use the bisquare M-estimator with region segmentation confidence weighting. A nearly tenfold improvement in subpixel registration precision is seen with the proposed technique when convergence is attained, as compared with competing techniques using both simulated and real data sets with interimage illumination variations.     

广义Pareto分布海杂波模型参数的组合双分位点估计方法

广义Pareto分布的复合高斯模型可以很好地描述高分辨低擦地角对海探测场景中海杂波的重拖尾特性,实现该杂波模型下双参数的有效估计对雷达检测性能具有重要意义。对此,该文提出一种双参数的组合双分位点(CBiP)估计方法。该估计方法基于低阶多项式方程的显式求根表达式,充分组合利用回波中的样本信息,旨在实现高精度的双参数估计过程。此外,考虑到实际雷达工作中存在岛礁、渔船等造成的功率异常大的野点样本时,不同于传统的矩估计、最大似然(ML)估计等方法,组合双分位点估计方法仍可保持估计性能的鲁棒性。仿真及实测数据实验表明,在纯杂波环境中,组合双分位点估计方法可以实现与最大似然估计方法近似的估计精度,若存在异常样本,组合双分位点估计方法的估计性能优于上述几种传统估计方法。     

On the robust estimation of the autocorrelation coefficients ofstationary sequences

This paper discusses methods for the estimation of the autocorrelation coefficients of a finite-dependent stationary random sequence. Three estimators are examined: the sample average and two proposed approaches, namely the pseudo-maximum-likelihood (pseudo-ML) estimator and the pseudo-M estimator. The latter scheme is found as a solution of a Fredholm integral equation. All three estimators are first studied for specific distribution models. Then the existence of a minimax robust design is proved and a suboptimally robust scheme is proposed. Simulation results illustrate the theoretical foundations of the methods and indicate that the pseudo-M estimator achieves significantly better performance than the other two schemes when tested against dependent data and in the presence of outliers. Finally, the results may also be applied to the estimation of a location parameter of a dependent random sequence     

一种基于小波分解及鲁棒估计的ICA算法及其在人脸识别中的应用

鲁棒主分量分析(RPCA)模型在选取幅度参数时,忽略了各变量独有的统计特性.为克服RPCA模型的这一不足,本文提出了通用鲁棒主分量分析(GRPCA)模型,采用M估计器(M-Estimator)为每个变量估计符合其自身统计特性的幅度参数,以提高模型的鲁棒性和通用性,并在此基础上提出了一种集成小波分解、鲁棒估计及独立分量分析的WR-ICA人脸识别算法.WR-ICA对人脸识别中的多种外部干扰(残缺人脸图像、化妆及遮挡等)都表现出很好的鲁棒性.理论分析和实验结果证实了WR-ICA的有效性,采用Cos距离作相似性度量时,WR-ICA的平均识别率达到99.44%.     

Robust Nonparametric Cyclic Correlation-Based Spectrum Sensing for Cognitive Radio

Cognitive radios sense the radio spectrum in order to find underutilized spectrum and then exploit it in an agile manner. Spectrum sensing has to be performed reliably in challenging propagation environments characterized by shadowing and fading effects as well as heavy-tailed noise distributions. In this paper, a robust computationally efficient nonparametric cyclic correlation estimator based on the multivariate (spatial) sign function is proposed. Nonparametric statistics provide additional robustness against heavy-tailed noise and when the noise statistics are not fully known. Asymptotic distribution of the spatial sign cyclic correlation estimator under the null hypothesis is established. Tests using constraint on false alarm rate are derived based on the estimated spatial sign cyclic correlation for single-user and collaborative spectrum sensing by multiple secondary users. Theoretical justification for detecting cyclostationary signals using the spatial sign cyclic correlation is provided. A sequential detection scheme for reducing the average detection time is proposed. Simulation experiments and theoretical results comparing the proposed method with cyclostationary spectrum sensing methods employing the conventional cyclic correlation estimator are presented. Simulations demonstrate the reliable and highly robust performance of the proposed nonparametric spectrum sensing method in both Gaussian and non-Gaussian noise environments.     

Robust shape tracking with multiple models in ultrasound images

This paper addresses object tracking in ultrasound images using a robust multiple model tracker. The proposed tracker has the following features: 1) it uses multiple dynamic models to track the evolution of the object boundary, and 2) it models invalid observations (outliers), reducing their influence on the shape estimates. The problem considered in this paper is the tracking of the left ventricle which is known to be a challenging problem. The heart motion presents two phases (diastole and systole) with different dynamics, the multiple models used in this tracker try to solve this difficulty. In addition, ultrasound images are corrupted by strong multiplicative noise which prevents the use of standard deformable models. Robust estimation techniques are used to address this difficulty. The multiple model data association (MMDA) tracker proposed in this paper is based on a bank of nonlinear filters, organized in a tree structure. The algorithm determines which model is active at each instant of time and updates its state by propagating the probability distribution, using robust estimation techniques.     

A hybrid feature extraction method for SAR image registration

Extracting and matching correct correspondence between two images are significant stages for feature-based synthetic aperture radar (SAR) image registration. Two methods of feature extraction were employed in this study. Blob features were obtained by combining a Gaussian-guided filter (GGF) with a scale invariant feature transform, and corner features were obtained from the GGF. A GGF can store edge information and operate more effectively than a Gaussian filter. The ratio of average was used to compute gradients in order to reduce the speckle effect. Fast sample consensus (FSC) algorithm was combined with complete graph method for feature correspondence matching. Although FSC algorithm can extract valid correspondence, it may not be efficient enough to deal with SAR images due to its random nature and the large number of outliers in the data. Therefore, a graph-based algorithm was employed to solve the problem by eliminating outliers. The proposed hybrid method was tested on several real SAR images having different properties. The results showed that the proposed method performed the automated registration of SAR images more accurately and efficiently.     

基于误差阈值和最小二乘图像拼接关系矩阵的实现

为了获得图像最佳拼接效果,对相邻图像间变换矩阵的求解问题进行研究,提出了一种全稳健的图像拼接算法.此算法采用SIFT进行特征点提取,初步得到了特征点匹配的伪匹配集合,并运用稳健的误差阈值法将伪匹配点集合划分为内点和外点,在内点域上运用误差的最小二乘优化算法精确地估计出了图像间的点变换关系,最后采用颜色插值对交接处进行颜...     

Robust and Improved Channel Estimation Algorithm for MIMO-OFDM Systems

Multiple-input multiple-output (MIMO) system using orthogonal frequency division multiplexing (OFDM) technique has become a promising method for reliable high data-rate wireless transmission system in which the channel is dispersive in both time and frequency domains. Due to multiple cochannel interferences in a MIMO system, the accuracy of channel estimation is a vital factor for proper receiver design in order to realize the full potential performance of the MIMO-OFDM system. A robust and improved channel estimation algorithm is proposed in this paper for MIMO-OFDM systems based on the least squares (LS) algorithm. The proposed algorithm, called improved LS (ILS), employs the noise correlation in order to reduce the variance of the LS estimation error by estimating and suppressing the noise in signal subspace. The performance of the ILS channel estimation algorithm is robust to the number of antennas in transmit and receive sides. The new algorithm attains a significant improvement in performance in comparison with that of the regular LS estimator. Also, with respect to mean square error criterion and without using channel statistics, the ILS algorithm achieves a performance very close to that of the minimum mean square error (MMSE) estimator in terms of the parameters used in practical MIMO-OFDM systems. A modification of the ILS algorithm, called modified ILS (MILS), is proposed based on using the second order statistical parameters of channel. Analytically, it is shown that the MILS estimator achieves the exact performance of the MMSE estimator. Due to no specific data sequences being required to perform the estimation, in addition to the training mode, the proposed channel estimation algorithms can also be extended and used in the tracking mode with decision-aided method.     

Restoration of DWI data using a Rician LMMSE estimator

This paper introduces and analyzes a linear minimum mean square error (LMMSE) estimator using a Rician noise model and its recursive version (RLMMSE) for the restoration of diffusion weighted images. A method to estimate the noise level based on local estimations of mean or variance is used to automatically parametrize the estimator. The restoration performance is evaluated using quality indexes and compared to alternative estimation schemes. The overall scheme is simple, robust, fast, and improves estimations. Filtering diffusion weighted magnetic resonance imaging (DW-MRI) with the proposed methodology leads to more accurate tensor estimations. Real and synthetic datasets are analyzed.      

A multiphase level set formulation for image segmentation using a MRF-based nonsymmetric Student’s-<Emphasis Type="Italic">t</Emphasis> mixture model

This paper proposes a new level set energy function framework in which the Markov random field-based nonsymmetric Student’s-t mixture model (SMM) is incorporated for labelling static images. This framework provides a general strategy by taking the best components of the Bayesian theory and level set technique. Therefore, the proposed segmentation method can bring the topology shape constraints to a statistical finite mixture model. An advantage of this method is that it can overcome the weakness of the conventional level set formulation by filtering out the outliers and stopping at the boundary points. Another feature is that the local relationship among neighbouring pixels is introduced into the prior probability so that the proposed framework is more robust against noise. The method is mainly implemented by modelling the probability density function of the observed data using nonsymmetric SMM. The proposed model has a simplified structure, which effectively reduces the computational complexity. Finally, numerical experiments on various synthetic, real-world images are conducted.     

Guaranteed robust nonlinear estimation with application to robot localization

When reliable prior bounds on the acceptable errors between the data and corresponding model outputs are available, bounded-error estimation techniques make it possible to characterize the set of all acceptable parameter vectors in a guaranteed way, even when the model is nonlinear and the number of data points small. However, when the data may contain outliers, i.e., data points for which these bounds should be violated, this set may turn out to be empty, or at least unrealistically small. The outlier minimal number estimator (OMNE) has been designed to deal with such a situation, by minimizing the number of data points considered as outliers. OMNE has been shown in previous papers to be remarkably robust, even to a majority of outliers. Up to now, it was implemented by random scanning, so its results could not be guaranteed. In this paper, a new algorithm based on set inversion via interval analysis provides a guaranteed OMNE, which is applied to the initial localization of an actual robot in a partially known two-dimensional (2-D) environment. The difficult problems of associating range data to landmarks of the environment and of detecting potential outliers are solved as byproducts of the procedure.     

基于长边缘相关和一致性检测的多传感器图像配准方法

遥感图像的配准特别是当波段相距较远的图像间配准时,由于其相关性小,直接提取的边缘特征中不一致特征所占比例很大,此时直接应用partialHausdorff距离等方法配准往往失效。本文提出了一种基于长边缘相关的图像配准方法,首先对长边缘进行相关计算,然后在相关长边缘的基础上对其余的边缘进行一致性检测。极大提高了边缘特征的一致性。长边缘相关是在比较HuiLi的相关方法基础上提出的改进Freeman链码相关系数方法。一致性特征检测方法是基于V.Randrianarisoa的检测方法并对之进行了改进。最后对一致边缘的相关部分使用最小二乘法得到了配准参数。仿真实验表明本方法对长边缘丰富的图像有很好的配准结果。并且本方法具有配准速度快的优点。     

A Real-Time Intelligent Wireless Mobile Station Location Estimator with Application to TETRA Network

Mobile location estimation has received considerable interest over the past few years due to its great potential in different applications such as logistics, patrol, and fleet management. Many mobile location estimation techniques had been proposed to improve the accuracy of location estimation. Location estimation based on artificial intelligence techniques is a recent alternative approach. In this paper, adaptive neuro-fuzzy inference system (ANFIS) is used as a robust location estimator to locate the mobile station (MS) using the MS geo-fencing area data within 9 km from a serving base station. Extensive evaluations and comparisons have been performed, and a set of statistical parameters has been obtained. From the comparison of the proposed ANFIS estimator with the neural-network-based estimators, it is found that ANFIS estimator is faster and more robust. Its average computation time (ACT) is 0.076 sec. While the ACT for multilayer perceptron (MLP) and radial-based function (RBF) neural networks is 0.88 and 1.7, respectively. Whereas on comparing ANFIS with other techniques, it is found that in ANFIS estimator, 67 percent of the estimated location errors do not exceed 149 m, while these for the statistical, multiple linear regression, and geometric are 170, 280, and 2,346 m, respectively. Thus, the results clearly reveal that the proposed ANFIS estimator outperforms all other techniques.     

On periodic pulse interval analysis with outliers and missingobservations

Analysis of periodic pulse trains based on time of arrival is considered, with perhaps very many missing observations and contaminated data. A period estimator is developed based on a modified Euclidean algorithm. This algorithm is a computationally simple, robust method for estimating the greatest common divisor of a noisy contaminated data set. The resulting estimate, although it is not maximum likelihood, is used as initialization in a three-step algorithm that achieves the Cramer-Rao bound (CRB) for moderate noise levels, as shown by comparing Monte Carlo results with the CRBs. This approach solves linear regression problems with missing observations and outliers. Comparisons with a periodogram approach based on a point process model are shown. An extension using multiple independent data records is also developed that overcomes high levels of contamination     

Robust Huber adaptive filter

Classical filtering methods are not optimal when the statistics of the signals violate the underlying assumptions behind the theoretical development. Most of the classical filtering theory like least-squares filtering assumes Gaussianity as its underlying distribution. We present a new adaptive filter that is optimal in the presence of Gaussian noise and robust to outliers. This novel robust adaptive filter minimizes the Huber objective function. An estimator based on the Huber objective function behaves as an L₁ norm estimator for large residual errors and as an L₂ norm estimator for small residual errors. Simulation results show the improved performance of the Huber adaptive filter (configured as a line enhancer) over various nonlinear filters in the presence of impulsive noise and Gaussian noise