Off-line recognition of realistic Chinese handwriting using segmentation-free strategy

Great challenges are faced in the off-line recognition of realistic Chinese handwriting. This paper presents a segmentation-free strategy based on Hidden Markov Model (HMM) to handle this problem, where character segmentation stage is avoided prior to recognition. Handwritten textlines are first converted to observation sequence by sliding windows. Then embedded Baum-Welch algorithm is adopted to train character HMMs. Finally, best character string maximizing the a posteriori is located through Viterbi algorithm. Experiments are conducted on the HIT-MW database written by more than 780 writers. The results show the feasibility of such systems and reveal apparent complementary capacities between the segmentation-free systems and the segmentation-based ones.     

Modeling Default Data Via an Interactive Hidden Markov Model

In this paper, we first introduce the use of an interactive hidden Markov model (IHMM) for modeling and analyzing default data in a sector. Under the IHMM, transitions of the hidden risk states of the sector depend on the observed number of bonds in the sector that default in the current time period. This incorporates the feedback effect of the number of defaults on the transitions of the hidden risk states. This feature seems to be more realistic and does not enjoy by the traditional HMMs. We then develop a “dynamic” version of the binomial expansion technique (BET) modulated by the IHMM for modeling the occurrence of defaults of bonds issued by firms in the same sector. Under the BET modulated by the IHMM, the number of bonds defaulting in each time period follows a Markov-modulated binomial distribution with the probability of defaulting of each bond depending on the states of the IHMM, which represent the hidden risk states of the sector. Efficient method will be presented for estimating the model parameters in the BET modulated by the IHMM. We shall compare the hidden risk state process extracted from the IHMM-modulated BET with that extracted from the BET modulated by HMM in order to illustrate the significance of the feedback effect using real data. We shall also present the estimation results for the BET modulated by the IHMM and compare them with those for the BET modulated by the HMM.     

Effects of the number of hidden nodes used in a structured-based neural network on the reliability of image classification

A structured-based neural network (NN) with backpropagation through structure (BPTS) algorithm is conducted for image classification in organizing a large image database, which is a challenging problem under investigation. Many factors can affect the results of image classification. One of the most important factors is the architecture of a NN, which consists of input layer, hidden layer and output layer. In this study, only the numbers of nodes in hidden layer (hidden nodes) of a NN are considered. Other factors are kept unchanged. Two groups of experiments including 2,940 images in each group are used for the analysis. The assessment of the effects for the first group is carried out with features described by image intensities, and, the second group uses features described by wavelet coefficients. Experimental results demonstrate that the effects of the numbers of hidden nodes on the reliability of classification are significant and non-linear. When the number of hidden nodes is 17, the classification rate on training set is up to 95%, and arrives at 90% on the testing set. The results indicate that 17 is an appropriate choice for the number of hidden nodes for the image classification when a structured-based NN with BPTS algorithm is applied.     

一种改进的步态识别方法

步态识别通过人体走路的姿势来识别人的身份。近年来,步态作为一种生物特征识别技术备受计算机视觉研究者的关注。对某个人的一个步态序列利用动态Viterbi算法得到一个样本姿态序列,对其多个步态样本姿态序列的对应姿态取平均得到这个人的特征姿态序列,对特征姿态采用主成分分析法和线性判别分析法处理特征空间,并用最近邻法进行识别。利用CASIA数据库对本文方法进行验证,取得了较高的识别率,并对体形变化具有较强的鲁棒性。     

基于隐马尔可夫模型的移动终端定位算法

移动互联网技术的快速发展,对移动终端定位方法提出了新的要求.针对传统方法在功耗、精度、通用性方面往往不能兼顾的问题,在充分挖掘个人行为习惯的基础上,提出了一种基于隐马尔可夫模型的定位算法.该算法根据地图信息对基站覆盖区域进行路段划分,通过记录个人的行走轨迹,利用其统计规律来辅助建立GSM信号的隐马尔可夫模型,通过测量基站的信号强度序列,完成对当前位置给出较为精确的估计.实验结果表明该算法在保证低功耗的同时有效提高了定位精度.     

水文水资源集对分析

集对分析是处理不确定性问题的新方法,它通过联系度展示了研究对象间关系的详细结构。方法原理简单,使用方便,能解决很多实际水文水资源问题。简要介绍了集对分析的基本原理,重点分析了集对分析在水文水资源分析计算、预测和评价中的应用研究进展,展望了集对分析在水文水资源领域的研究目标、内容和拟解决的关键问题。     

Control of discrete-time HMM partially observed under fractional Gaussian noises

A discrete-time control problem of a finite-state hidden Markov chain partially observed in a fractional Gaussian process is discussed using filtering. The control problem is then recast as a separated problem with information variables given by the unnormalized conditional probabilities of the whole path of the hidden Markov chain. A dynamic programming result and a minimum principle are obtained.     

Hidden Markov models with arbitrary state dwell-time distributions

A hidden Markov model (HMM) with a special structure that captures the ‘semi’-property of hidden semi-Markov models (HSMMs) is considered. The proposed model allows arbitrary dwell-time distributions in the states of the Markov chain. For dwell-time distributions with finite support the HMM formulation is exact while for those that have infinite support, e.g. the Poisson, the distribution can be approximated with arbitrary accuracy. A benefit of using the HMM formulation is that it is easy to incorporate covariates, trend and seasonal variation particularly in the hidden component of the model. In addition, the formulae and methods for forecasting, state prediction, decoding and model checking that exist for ordinary HMMs are applicable to the proposed class of models. An HMM with explicitly modeled dwell-time distributions involving seasonality is used to model daily rainfall occurrence for sites in Bulgaria.     

一种基于HMM的场景识别方法

隐马尔科夫模型作为一种统计分析模型,能够通过观测向量序列计算其隐含状态的概率分布密度。提出一种智能空间中基于HMM的场景识别方法,该方法指定系统相关情境信息,确定隐含场景集和观察情境集,采用部分相关情境信息而非全部情境信息作为场景特征参与场景识别,利用HMM对隐含场景间的关系进行建模,设计了基于HMM的场景识别算法。实验结果表明,采用基于HMM的场景识别方法能够获得较高的识别效率。     

隐马尔可夫模型的一种有区分力的反向传播训练方法

研究隐马尔可夫模型(HMM)的一种有区分力的训练方法.在多层前向神经网络的 框架中实现了HMM的前向概率计算.基于这一框架,利用偏导数的反向传播计算方法,通 过梯度上升的优化过程来实现互信息的最大化,从而对HMM进行有区分力的训练.这一 训练方法被称之为HMM的反向传播训练方法.此外,还设计了一个用以实现这一训练方 法的在数值计算上具有强鲁棒性的算法.语音识别的实验结果证实了这一训练方法的优越 性.