A feature space for edgels in images with landmarks

In many current medical applications of image analysis, objects are detected and delimited by boundary curves or surfaces. Yet the most effective multivariate statistics available pertain to labeled points (landmarks) only. In the finite-dimensional feature space that landmarks support, each case of a data set is equivalent to a deformation map deriving it from the average form. This paper introduces a new extension of the finite-dimensional spline-based approach for incorporating edge information. In this implementation edgels are restricted to landmark loci: they are interpreted as pairs of landmarks at infinitesimal separation in a specific direction. The effect of changing edge direction is a singular perturbation of the thin-plate spline for the landmarks alone. An appropriate normalization yields a basis for image deformations corresponding to changes of edge direction without landmark movement; this basis complements the basis of landmark deformations ignoring edge information. We derive explicit formulas for these edge warps, evaluate the quadratic form expressing bending energies of their formal combinations, and show the resulting spectrum of edge features in typical scenes. These expressions will aid all investigations into medical images that entail comparisons of anatomical scene analyses to a normative or typical form.     

基于ARMA的微惯性传感器随机误差建模方法

针对微惯性传感器随机误差建模效果不理想,影响微惯性组合导航系统性能的问题,提出了采用自回归滑动平均(ARMA)对微惯性传感器随机误差进行建模的方法。通过对随机误差模型应用于微惯性器件误差建模的深入分析,将Yule-Walker方程引入线性预测问题中,实现AR功率谱密度的估计,建立了基于随机过程有理功率谱密度的ARMA模型建立方法,并给出了ARMA建模准确性的LDA验证准则。通过微惯性传感器实测数据,对随机误差建模方法进行了有效性验证。该方法为微惯性器件的随机误差建模和分析提供了一种新的途径。     

广义质心及其在仿射变换参数恢复中的应用

目的 全局的仿射变换配准需估计出仿射变换的参数,现有算法要么效果不佳,要么对二值图像无能为力。本文改造传统质心的定义,提出广义质心的概念。方法 传统的质心以二重积分定义,所提广义质心利用变形累次积分定义,传统质心只是这种广义质心的特例。本文给出了广义质心保持仿射变换前后对应关系的条件,并提出了一种利用这种广义质心进行仿射变换参数恢复的算法。结果 该算法对灰度和二值图像的仿射变换参数恢复都适用,实验结果也表明现有的交叉权重矩方法耗时是本文算法耗时的25倍,但它们的恢复效果相差不大,并且本文算法要比现有的图像矩构造非线性方程组方法恢复效果好。结论 本文提出了广义质心,利用这种广义质心进行仿射变换参数恢复算法,对二值图像和灰度图像均适用,恢复效果较好,并且计算量较小。     

离散系统的鲁棒约束方差估计及应用——模型噪声强度不确定情形

考虑离散随机系统在模型噪声强度不确定及估计误差方差受约束情形下的一类鲁棒状态 估计问题,即希望找到这样的滤波增益,使得当模型噪声强度在一定范围内变动时,每个状态 分量的估计误差方差始终不大于预先指定值.文中给出了这种滤波增益的设计方法,并以一 类机动目标跟踪问题为例,说明这种设计方法的直接性与有效性.     

String Methods for Stochastic Image and Shape Matching

Matching of images and analysis of shape differences is traditionally pursued by energy minimization of paths of deformations acting to match the shape objects. In the large deformation diffeomorphic metric mapping (LDDMM) framework, iterative gradient descents on the matching functional lead to matching algorithms informally known as Beg algorithms. When stochasticity is introduced to model stochastic variability of shapes and to provide more realistic models of observed shape data, the corresponding matching problem can be solved with a stochastic Beg algorithm, similar to the finite-temperature string method used in rare event sampling. In this paper, we apply a stochastic model compatible with the geometry of the LDDMM framework to obtain a stochastic model of images and we derive the stochastic version of the Beg algorithm which we compare with the string method and an expectation-maximization optimization of posterior likelihoods. The algorithm and its use for statistical inference is tested on stochastic LDDMM landmarks and images.     

A Deformable Template Model, with Special Reference to Elliptical Templates

This paper suggests a high-level continuous image model for planar star-shaped objects. Under this model, a planar object is a stochastic deformation of a star-shaped template. The residual process, describing the difference between the radius-vector function of the template and the object, is allowed to be non-stationary. Stationarity is obtained by a time change. A parametric model for the residual process is suggested and straightforward parameter estimation techniques are developed. The deformable template model makes it possible to detect pathologies as demonstrated by an analysis of a data set of cell nuclei from a benign and a malignant tumour, using stochastic deformations of ellipses.     

Event-based input and state estimation for linear discrete time-varying systems

In this paper, the joint input and state estimation problem is considered for linear discrete-time stochastic systems. An event-based transmission scheme is proposed with which the current measurement is released to the estimator only when the difference from the previously transmitted one is greater than a prescribed threshold. The purpose of this paper is to design an event-based recursive input and state estimator such that the estimation error covariances have guaranteed upper bounds at all times. The estimator gains are calculated by solving two constrained optimisation problems and the upper bounds of the estimation error covariances are obtained in form of the solution to Riccati-like difference equations. Special efforts are made on the choices of appropriate scalar parameter sequences in order to reduce the upper bounds. In the special case of linear time-invariant system, sufficient conditions are acquired under which the upper bound of the error covariance of the state estimation is asymptomatically bounded. Numerical simulations are conducted to illustrate the effectiveness of the proposed estimation algorithm.     

离散系统的鲁捧约束方差估计及应用──模型噪声强度不确定情形

考虑离散随机系统在模型噪声强度不确定及估计误差方差受约束情形下的一类鲁棒状态估计问题，即希望找到这样的滤波增益，使得当模型噪声强度在一定范围内变动时，每个状态分量的估计误差方差始终不大于预先指定值。文中给出了这种滤波增益的设计方法，并以一类机动目标跟踪问题为例，说明这种设计方法的直接性与有效性。     

Endocardial Boundary E timation and Tracking in Echocardiographic Images using Deformable Template and Markov Random Fields

We present a new approach to shape-based segmentation and tracking of deformable anatomical structures in medical images, and validate this approach by detecting and tracking the endocardial contour in an echocardiographic image sequence. To this end, some global prior shape knowledge of the endocardial boundary is captured by a prototype template with a set of predefined global and local deformations to take into account its inherent natural variability over time. In this deformable model-based Bayesian segmentation, the data likelihood model relies on an accurate statistical modelling of the grey level distribution of each class present in the ultrasound image. The parameters of this distribution mixture are given by a preliminary iterative estimation step. This estimation scheme relies on a Markov Random Field prior model, and takes into account the imaging process as well as the distribution shape of each class present in the image. Then the detection and the tracking problem is stated in a Bayesian framework, where it ends up as a cost function minimisation problem for each image of the sequence. In our application, this energy optimisation problem is efficiently solved by a genetic algorithm combined with a steepest ascent procedure. This technique has been successfully applied on synthetic images, and on a real echocardiographic image sequence.     

A recursive hierarchical parametric estimation algorithm for nonlinear systems described by Wiener‐Hammerstein models

In this paper, a recursive hierarchical parametric estimation (RHPE) algorithm is proposed for stochastic nonlinear systems which can be described by Wiener‐Hammerstein (W‐H) mathematical models. The formulation of parameters estimation problem is based on the prediction error approach and the gradient techniques. The convergence analysis of the developed RHPE algorithm is derived using stochastic gradient‐based theory. Wiener‐Hammerstein hydraulic process is treated to prove the efficiency of the proposed approach.     

Principal warps: thin-plate splines and the decomposition ofdeformations

The decomposition of deformations by principal warps is demonstrated. The method is extended to deal with curving edges between landmarks. This formulation is related to other applications of splines current in computer vision. How they might aid in the extraction of features for analysis, comparison, and diagnosis of biological and medical images in indicated     

Some Statistical-Estimation Methods for Stochastic Finite-State Transducers

Formal translations constitute a suitable framework for dealing with many problems in pattern recognition and computational linguistics. The application of formal transducers to these areas requires a stochastic extension for dealing with noisy, distorted patterns with high variability. In this paper, some estimation criteria are proposed and developed for the parameter estimation of regular syntax-directed translation schemata. These criteria are: maximum likelihood estimation, minimum conditional entropy estimation and conditional maximum likelihood estimation. The last two criteria were proposed in order to deal with situations when training data is sparse. These criteria take into account the possibility of ambiguity in the translations: i.e., there can be different output strings for a single input string. In this case, the final goal of the stochastic framework is to find the highest probability translation of a given input string. These criteria were tested on a translation task which has a high degree of ambiguity.     

A comparison of DEA and the stochastic frontier model using panel data

Two competing approaches for the measurement of efficiency are the stochastic frontier model and data envelopment analysis (DEA). Previous research has established that the two models applied to cross‐sectional data are both adversely affected by measurement error. While the cross‐sectional stochastic frontier model does not effectively handle statistical noise, panel data models do. This is true because additional information from multiple time periods is incorporated into the estimation. A panel data DEA model that uses averaged data has been shown to effectively smooth out measurement error. In this paper, we compare the panel data models using simulated data.     

时变系统有限数据窗最小二乘辨识的有界收敛性

利用随机过程理论证明了有限数据窗最小二乘法的有界收敛性,给出了参数估计误差 上界的计算公式,阐述了获得最小均方参数估计误差上界时数据窗长度的选择方法.分析表明, 对于时不变随机系统,数据窗长度越大,均方参数估计误差上界越小;对于确定性时变系统,数 据窗长度越小,均方参数估计误差上界越小.因此,对于时变随机系统,一个折中方案是寻求一 个最佳数据窗长度,以使均方参数估计误差最小.该文的研究成果对于提高辨识算法的实际应 用效果有重要意义.     

基于不确定性估计的医学影像标志点定位

基于热力图的方法是当前医学影像标志点定位算法中的主流方法,然而,现有方法几乎都使用预定义的热力图作为标签,不能很好地表示真实的标志点位置分布,从而限制了模型的性能.为此,本文提出基于不确定性估计的医学影像标志点定位算法,同时预测标志点位置及其分布.模型利用多分支空洞卷积提取多尺度的上下文信息,同时使用自注意力机制强化重要特征,从而在预测分布的同时提高算法的定位能力.在公开数据集上的结果表明,本文提出的算法整体上提升了标志点定位的性能,在大部分指标上优于现有算法,并且其预测出的标志点分布与真实标注下的标志点分布相符.     

On the adaptive control of a class of systems with random parameters and disturbances

The adaptive control of discrete time parameter linear stochastic systems with random parameters is investigated. It is shown that systems whose (unknown) autoregressive parameters undergo bounded martingale difference disturbances may be stabilized by the application of the so-called Modified Least Squares adaptive control algorithm. Asymptotically, the sample mean square performance criterion is equal to the one step ahead minimum variance control loss (which equals the prediction error variance when the system parameters are known) plus a term which is bounded by a quantity proportional to the square of the bound on the parameter disturbance. This latter term may be interpreted as the increase in the prediction error variance due to the random parameter variation.     

Constrained state estimation for stochastic jump systems: moving horizon approach

We discuss the state estimation advantages for a class of linear discrete-time stochastic jump systems, in which a Markov process governs the operation mode, and the state variables and disturbances are subject to inequality constraints. The horizon estimation approach addressed the constrained state estimation problem, and the Bayesian network technique solved the stochastic jump problem. The moving horizon state estimator designed in this paper can produce the constrained state estimates with a lower error covariance than under the unconstrained counterpart. This new estimation method is used in the design of the restricted state estimator for two practical applications.     

Automated bone landmarks prediction on the femur using anatomical deformation technique

Anatomical landmarks on bones play important roles in musculoskeletal simulations and surgical planning. This study develops an anatomically deformable model of the femur to predict bone landmarks automatically and quantifies its prediction accuracy. Forty-three angiographic computed tomography (CT) images of femurs were collected and 14 bone landmarks were manually marked on these images by experts. Surface mesh models of the femur were extracted from the CT images and combined with the bone landmark information to create an anatomical deformable model. The anatomical deformation technique developed in this study predicted bone landmarks automatically as the surface of a deformable model was matched to the surface of a given femur model. The prediction accuracy was quantified using the leave-one-out cross-validation method. The average prediction error for the 14 landmarks ranged from 2.80 to 5.93 mm. While the prediction accuracies of anterior and posterior cruciate ligaments and lateral epicondyle sites were high with averages (standard deviation) of 3.00 (±1.55), 2.80 (±1.76) and 2.97 (±1.87) mm, respectively, those of gluteus minimus, ligament of head of femur and piriformis sites were low with averages of 5.93 (±3.77), 4.89 (±3.49) and 4.87 (±2.70) mm, respectively. Accuracy can be expected to increase with the use of more population data as is the nature of a population-based statistical deformable model.     

时变系统最小均方算法的性能分析

在无过程数据平稳性假设和各态遍历等条件下,运用随机过程理论研究了最小方算法（LMS）的有界收敛性,给出了估计误差的上界,论述了LMS算法收敛因子或步长的选择方法,以使参数估计误差上界最小。这对于提高LMS算法的实际应用效果有着重要意义。LMS算法的收敛性分析表明：（1）对于确定性时不变系统,LMS算法是指数速度收敛的;（2）对于确定性时变系统,收敛因子等于1,LMS算法的参数估计误差上界最小;（3）对于时变或不变随机系统,LMS算法的参数估计误差一致有上界。     

Vision-Based Odometry and SLAM for Medium and High Altitude Flying UAVs

This paper proposes vision-based techniques for localizing an unmanned aerial vehicle (UAV) by means of an on-board camera. Only natural landmarks provided by a feature tracking algorithm will be considered, without the help of visual beacons or landmarks with known positions. First, it is described a monocular visual odometer which could be used as a backup system when the accuracy of GPS is reduced to critical levels. Homography-based techniques are used to compute the UAV relative translation and rotation by means of the images gathered by an onboard camera. The analysis of the problem takes into account the stochastic nature of the estimation and practical implementation issues. The visual odometer is then integrated into a simultaneous localization and mapping (SLAM) scheme in order to reduce the impact of cumulative errors in odometry-based position estimation approaches. Novel prediction and landmark initialization for SLAM in UAVs are presented. The paper is supported by an extensive experimental work where the proposed algorithms have been tested and validated using real UAVs.