An intensity-based cooperative bidirectional stereo matching with simultaneous detection of discontinuities and occlusions

This paper presents a new intensity-based stereo algorithm using cooperative bidirectional matching with a hierarchical multilevel structure. Based on a new model of piecewise smooth depth fields and the consistency constraint, the algorithm is able to estimate the 3-D structure and detect its discontinuities and the occlusion reliably with low computational costs. In order to find the global optimal estimates, we utilize a multiresolution two-stage algorithm minimizing nonconvex cost functions, which is equivalent to the MAP estimation. This basic framework computing the 3-D structure from binocular stereo images has been extended to the trinocular stereo vision for a further improvement of the performance. A few examples for the binocular and trinocular stereo problems are given to illustrate the performance of the new algorithms.     

基于传感器网络的水下声音源定位方法研究

提出一种分层结构的自组织无线传感器网络(WSN)用于水下声音源的定位研究,可以广泛应用于军事、民用监控等场景;在修正的声音源衰减模型基础上,提出一种改进的非线性最小二乘算法以及极大似然算法用于水下声音源定位;仿真试验对比研究了两种算法在不同的传感器节点以及背景噪声情况下对预估定位误差的影响;试验结果表明了这种分层结构的WSN用于水下声音源定位是可行的,同时验证了最小二乘算法以及极大似然两种算法定位的有效性。     

Segmentation of fuzzy images: a novel and fast two-step pseudo MAP method

This paper presents a new two-step pseudo maximum a posteriori (MAP) segmentation method for the Markov random field (MRF)-modeled image because the exact MAP estimation is hard to implement due to intractable complexity. The expectation maximization (EM) and Markov Chain Monte Carlo (MCMC) methods are adopted to estimate the parameters for the MRF model due to their comparatively good performance. Although the image segmentation algorithms via graph cuts have become very popular nowadays, our proposed algorithm still performs significantly better in automatic identification and segmentation of fuzzy images than them, which is shown by the quantitative results on synthesized images. In practical applications, the proposed two-step pseudo MAP method is superior in segmenting the fuzzy laser images reflected from the weld pool surfaces during the P-GMAW welding process.     

Efficient Markov chain Monte Carlo methods for decoding neural spike trains

Stimulus reconstruction or decoding methods provide an important tool for understanding how sensory and motor information is represented in neural activity. We discuss Bayesian decoding methods based on an encoding generalized linear model (GLM) that accurately describes how stimuli are transformed into the spike trains of a group of neurons. The form of the GLM likelihood ensures that the posterior distribution over the stimuli that caused an observed set of spike trains is log concave so long as the prior is. This allows the maximum a posteriori (MAP) stimulus estimate to be obtained using efficient optimization algorithms. Unfortunately, the MAP estimate can have a relatively large average error when the posterior is highly nongaussian. Here we compare several Markov chain Monte Carlo (MCMC) algorithms that allow for the calculation of general Bayesian estimators involving posterior expectations (conditional on model parameters). An efficient version of the hybrid Monte Carlo (HMC) algorithm was significantly superior to other MCMC methods for gaussian priors. When the prior distribution has sharp edges and corners, on the other hand, the "hit-and-run" algorithm performed better than other MCMC methods. Using these algorithms, we show that for this latter class of priors, the posterior mean estimate can have a considerably lower average error than MAP, whereas for gaussian priors, the two estimators have roughly equal efficiency. We also address the application of MCMC methods for extracting nonmarginal properties of the posterior distribution. For example, by using MCMC to calculate the mutual information between the stimulus and response, we verify the validity of a computationally efficient Laplace approximation to this quantity for gaussian priors in a wide range of model parameters; this makes direct model-based computation of the mutual information tractable even in the case of large observed neural populations, where methods based on binning the spike train fail. Finally, we consider the effect of uncertainty in the GLM parameters on the posterior estimators.     

信度网中条件概率表的学习

一、引言信度网B的学习包括结构B(?)的学习和条件概率表B_p的学习。因果马尔可夫条件原理表明:如果图形G是一个随机变量集合X的因果图,那么图形G也是该随机变量集合的联合概率分布所对应的信度网的结构图。根据这一原理,在实际应用中,可以利用领域     

Faster ARMA maximum likelihood estimation

A new likelihood based AR approximation is given for ARMA models. The usual algorithms for the computation of the likelihood of an ARMA model require O(n) flops per function evaluation. Using our new approximation, an algorithm is developed which requires only O(1) flops in repeated likelihood evaluations. In most cases, the new algorithm gives results identical to or very close to the exact maximum likelihood estimate (MLE). This algorithm is easily implemented in high level quantitative programming environments (QPEs) such as Mathematica, MatLab and R. In order to obtain reasonable speed, previous ARMA maximum likelihood algorithms are usually implemented in C or some other machine efficient language. With our algorithm it is easy to do maximum likelihood estimation for long time series directly in the QPE of your choice. The new algorithm is extended to obtain the MLE for the mean parameter. Simulation experiments which illustrate the effectiveness of the new algorithm are discussed. Mathematica and R packages which implement the algorithm discussed in this paper are available [McLeod, A.I., Zhang, Y., 2007. Online supplements to “Faster ARMA Maximum Likelihood Estimation”, 〈http://www.stats.uwo.ca/faculty/aim/2007/faster/〉]. Based on these package implementations, it is expected that the interested researcher would be able to implement this algorithm in other QPEs.     

基于双树复小波二元统计模型的图像去噪方法

为了更有效地进行图像去噪,提出了一种基于双树复小波二元统计模型的图像去噪方法,该方法先用带参数的二元广义高斯分布(GGD)来模拟原图双树复小波系数的统计分布;然后结合最大似然估计(MLE)得到优化的参数估计;最后在此先验分布的基础上,运用最大后验概率(MAP)来估计从噪声图的小波系数中恢复原图的系数,从而达到去噪的目的。实验表明该新方法不仅可以干净地去除图像的噪声,还可以有效地保留图像细节,取得了良好的去噪效果,尤其是去噪图像的视觉效果要明显优于目前的很多算法。     

基于层次随机图模型的脑网络链路预测

针对一般链路预测算法在具有层次结构的脑网络中计算效率低且复杂度高的问题,提出了一种基于最大似然估计的层次随机图模型。该算法首先利用脑网络数据建立层次随机图,然后通过改进的马尔科夫蒙特卡罗算法采样树状图空间,最后计算脑网络边的平均连接概率,且通过评价指标对算法进行评价。实验结果表明,利用该算法对脑网络和3种不同的层次结构网络进行链路预测比较, 脑网络的预测结果最好。此外,所提出的算法较之传统的基于相似性的算法,该算法效果明显,且具有理想的计算复杂度。     

Terrain analysis using radar shape-from-shading

This paper develops a maximum a posteriori (MAP) probability estimation framework for shape-from-shading (SFS) from synthetic aperture radar (SAR) images. The aim is to use this method to reconstruct surface topography from a single radar image of relatively complex terrain. Our MAP framework makes explicit how the recovery of local surface orientation depends on the whereabouts of terrain edge features and the available radar reflectance information. To apply the resulting process to real world radar data, we require probabilistic models for the appearance of terrain features and the relationship between the orientation of surface normals and the radar reflectance. We show that the SAR data can be modeled using a Rayleigh-Bessel distribution and use this distribution to develop a maximum likelihood algorithm for detecting and labeling terrain edge features. Moreover, we show how robust statistics can be used to estimate the characteristic parameters of this distribution. We also develop an empirical model for the SAR reflectance function. Using the reflectance model, we perform Lambertian correction so that a conventional SFS algorithm can be applied to the radar data. The initial surface normal direction is constrained to point in the direction of the nearest ridge or ravine feature. Each surface normal must fall within a conical envelope whose axis is in the direction of the radar illuminant. The extent of the envelope depends on the corrected radar reflectance and the variance of the radar signal statistics. We explore various ways of smoothing the field of surface normals using robust statistics. Finally, we show how to reconstruct the terrain surface from the smoothed field of surface normal vectors. The proposed algorithm is applied to various SAR data sets containing relatively complex terrain structure.     

Iterative proportional scaling via decomposable submodels for contingency tables

We propose iterative proportional scaling (IPS) via decomposable submodels for maximizing the likelihood function of a hierarchical model for contingency tables. In ordinary IPS the proportional scaling is performed by cycling through the members of the generating class of a hierarchical model. We propose the adjustment of more marginals at each step. This is accomplished by expressing the generating class as a union of decomposable submodels and cycling through the decomposable models. We prove the convergence of our proposed procedure, if the amount of scaling is adjusted properly at each step. We also analyze the proposed algorithms around the maximum likelihood estimate (MLE) in detail. The faster convergence of our proposed procedure is illustrated by numerical examples.     

A note on the estimation of variances in state-space models using the maximum a posteriori procedure

This note examines an estimation procedure for the unknown parameters in a state-space model proposed by Tsang, Glover, and Bach. The method is based on the maximum a posteriori (MAP) principle. Contrary to the claims of Tsang et al. it is shown that the algorithm performs very poorly compared to maximum likelihood. Some insight into the shortcomings of the MAP procedure is obtained by comparing it to the computation of maximum likelihood estimators by the EM algorithm.     

Multilevel discrete time system identification in large scale systems†

Hierarchical decomposition is considered to be one of the most powerful and offective tools to deal with complexity. Hierarchical system theory, which deals with system decomposition and coordination, can be used to decentralize and reduce the computational efforts requirements for many large-scale problems. This is achieved by decomposing the original system problem into several lower order easier to handle sub-problems, which are then coordinated such that the overall system objectives are met. In this work a hierarchical system theory approach to the discrete-time system identification problem is considered for stochastic large-scale system applications. A set of sequential discrete-time hierarchical identification algorithms, suitable for known and unknown system noise moments, are first obtained using a maximum a posteriori (MAP) approach with covariance matching and maximum likelihood (ML) methods. This is conducted in a two level hierarchical structure with two principles of coordination. Next, the hierarchical system identification algorithms are extended to multilevel hierarchical structures based on system characteristics of priority of action, spatial structure and time behaviour. This results in multilevel and composite multilevel coordinated system identification procedures, where each subsystem unit can be treated independently in reaching the overall system optimality. Application of these algorithms for the purpose of decentralization and reduction of computational requirements as well as adaptation to structural changes in growth and merger are considered.     

Simple parallel hierarchical and relaxation algorithms for segmenting noncausal markovian random fields

The modeling and segmentation of images by MRF's (Markov random fields) is treated. These are two-dimensional noncausal Markovian stochastic processes. Two conceptually new algorithms are presented for segmenting textured images into regions in each of which the data are modeled as one of C MRF's. The algorithms are designed to operate in real time when implemented on new parallel computer architectures that can be built with present technology. A doubly stochastic representation is used in image modeling. Here, a Gaussian MRF is used to model textures in visible light and infrared images, and an autobinary (or autoternary, etc.) MRF to model a priori information about the local geometry of textured image regions. For image segmentation, the true texture class regions are treated either as a priori completely unknown or as a realization of a binary (or ternary, etc.) MRF. In the former case, image segmentation is realized as true maximum likelihood estimation. In the latter case, it is realized as true maximum a posteriori likelihood segmentation. In addition to providing a mathematically correct means for introducing geometric structure, the autobinary (or ternary, etc.) MRF can be used in a generative mode to generate image geometries and artificial images, and such simulations constitute a very powerful tool for studying the effects of these models and the appropriate choice of model parameters. The first segmentation algorithm is hierarchical and uses a pyramid-like structure in new ways that exploit the mutual dependencies among disjoint pieces of a textured region.     

Boosting learning and inference in Markov logic through metaheuristics

Markov Logic (ML) combines Markov networks (MNs) and first-order logic by attaching weights to first-order formulas and using these as templates for features of MNs. State-of-the-art structure learning algorithms in ML maximize the likelihood of a database by performing a greedy search in the space of structures. This can lead to suboptimal results because of the incapability of these approaches to escape local optima. Moreover, due to the combinatorially explosive space of potential candidates these methods are computationally prohibitive. We propose a novel algorithm for structure learning in ML, based on the Iterated Local Search (ILS) metaheuristic that explores the space of structures through a biased sampling of the set of local optima. We show through real-world experiments that the algorithm improves accuracy and learning time over the state-of-the-art algorithms. On the other side MAP and conditional inference for ML are hard computational tasks. This paper presents two algorithms for these tasks based on the Iterated Robust Tabu Search (IRoTS) metaheuristic. The first algorithm performs MAP inference and we show through extensive experiments that it improves over the state-of-the-art algorithm in terms of solution quality and inference time. The second algorithm combines IRoTS steps with simulated annealing steps for conditional inference and we show through experiments that it is faster than the current state-of-the-art algorithm maintaining the same inference quality.     

Uncertainty-based learning of acoustic models from noisy data

We consider the problem of acoustic modeling of noisy speech data, where the uncertainty over the data is given by a Gaussian distribution. While this uncertainty has been exploited at the decoding stage via uncertainty decoding, its usage at the training stage remains limited to static model adaptation. We introduce a new expectation maximization (EM) based technique, which we call uncertainty training, that allows us to train Gaussian mixture models (GMMs) or hidden Markov models (HMMs) directly from noisy data with dynamic uncertainty. We evaluate the potential of this technique for a GMM-based speaker recognition task on speech data corrupted by real-world domestic background noise, using a state-of-the-art signal enhancement technique and various uncertainty estimation techniques as a front-end. Compared to conventional training, the proposed training algorithm results in 3–4% absolute improvement in speaker recognition accuracy by training from either matched, unmatched or multi-condition noisy data. This algorithm is also applicable with minor modifications to maximum a posteriori (MAP) or maximum likelihood linear regression (MLLR) acoustic model adaptation from noisy data and to other data than audio.     

Maximum likelihood based identification methods for rational models

ABSTRACT

In a rational model, some terms of the information vector are correlated with the noise, which makes the traditional least squares based iterative algorithms biased. In order to overcome this shortcoming, this paper develops two recursive algorithms for estimating the rational model parameters. These two algorithms, based on the maximum likelihood principle, have three integrated key features: (1) to establish two unbiased maximum likelihood recursive algorithms, (2) to develop a maximum likelihood recursive least squares (ML-RLS) algorithm to decrease the computational efforts, (3) to update the parameter estimates by the ML-RLS based particle swarm optimisation (ML-RLS-PSO) algorithm when the noise-to-output ratio is large. Comparative studies demonstrate that (1) the ML-RLS algorithm is only valid for rational models when the noise-to-output ratio is small, (2) the ML-RLS-PSO algorithm is effective for rational models with random noise-to-output ratio, but at the cost of heavy computational efforts. Furthermore, the simulations provide cases for potential expansion and applications of the proposed algorithms.     

基于上下文分析的无监督分层迭代算法用于SAR图像分割

基于聚类的分割算法能够有效地分析目标特征在特征域的分布结构,进而准确判断目标的所属类别,但难以利用图像的空间和边缘信息,而基于区域增长的分割算法能够在空间域利用多种图像信息计算目标之间的相似性,但缺乏对特征结构本身的深层挖掘,容易出现欠分割或过分割的结果. 本文结合这两种算法各自的优势,针对合成孔径雷达（Synthetic aperture radar,SAR）图像的特点,提出了一种基于上下文分析的无监督分层迭代算法. 该算法使用过分割区域作为操作单元,以提高分割速度,降低SAR图像相干斑噪声的影响. 在合并过分割区域时,该算法采用了分层迭代的策略:首先,设计了一种改进的模糊C均值聚类算法,对过分割区域的外观特征进行聚类分析,获得其类别标记,该类别标记包含了特征的分布结构信息. 然后,利用多种SAR图像特征对同类区域的空域上下文进行分析,使用区域迭代增长算法对全局范围内的相似区域进行合并,直到不存在满足合并条件的过分割区域对为止,再重新执行聚类算法. 这两种子算法分层交替迭代,扬长避短,实现了一种有效的方法来组织和利用多种信息对SAR图像进行分割. 对模拟和真实SAR图像的实验表明,本文提出的算法能够在区域一致性和细节保留之间做到很好的平衡,准确地分割出各类目标区域,对相干斑噪声具有很强的鲁棒性.     

Intrapersonal subspace analysis with application to adaptive Bayesian face recognition

We propose a subspace distance measure to analyze the similarity between intrapersonal face subspaces, which characterize the variations between face images of the same individual. We call the conventional intrapersonal subspace the average intrapersonal subspace (AIS) because the image differences often come from a large number of persons. We call an intrapersonal subspace specific intrapersonal subspace (SIS) if the image differences are from just one person. We demonstrate that SIS varies from person to person and most SISs are not similar to AIS. Based on these observations, we introduce the maximum a posteriori (MAP) adaptation to the problem of SIS estimation, and apply it to the Bayesian face recognition algorithm. Experimental results show that the adaptive Bayesian algorithm outperforms the non-adaptive Bayesian algorithm as well as Eigenface and Fisherface methods when a small number of adaptation images are available.     

Bayesian estimation of motion vector fields

A stochastic approach to the estimation of 2D motion vector fields from time-varying images is presented. The formulation involves the specification of a deterministic structural model along with stochastic observation and motion field models. Two motion models are proposed: a globally smooth model based on vector Markov random fields and a piecewise smooth model derived from coupled vector-binary Markov random fields. Two estimation criteria are studied. In the maximum a posteriori probability (MAP) estimation, the a posteriori probability of motion given data is maximized, whereas in the minimum expected cost (MEC) estimation, the expectation of a certain cost function is minimized. Both algorithms generate sample fields by means of stochastic relaxation implemented via the Gibbs sampler. Two versions are developed: one for a discrete state space and the other for a continuous state space. The MAP estimation is incorporated into a hierarchical environment to deal efficiently with large displacements