基于手势识别的机器人人机交互技术研究

研究了基于视觉的动态手势识别技术,采用基于肤色的高斯模型与改进的光流场跟踪算法结合的方 法,实现了复杂背景下实时的手势跟踪,具有快速和准确的特点,且具有较好的鲁棒性．对于动态手势识别器,采 用了隐马尔可夫模型（HMM）作为训练识别算法．考虑到动态手势特征本身的一些特点,对HMM 参数优化算法重 估式加以修正,调整了算法比例因子,从而推导了最佳状态链的确定算法、HMM 参数优化算法．最后将研究开发 的动态手势识别算法成功地应用到了基于网络的远程机器人控制系统中．     

基于“快速投票”算法的HMM／SVM混合识别模型及应用

提出一种基于隐马尔可夫模型（HMM）和支持向量机（SVM）的双层过滤识别系统。根据隐马尔可夫模型训练中不同结构的序列其L值分布范围不同的特点，对传统多类“投票模型”进行改进，提出一种“快速投票”算法。先用HMM对人类内含子和外显子进行识别，同时，对于L值区域有重叠造成识别率较低的部分，再用支持向量机进行第二次识别过滤。这一模型克服了传统用单一HMM识别方法的不足，实现了HMM和SVM的优势互补。实验表明，用HMM／SVM进行两类识别，其平均识别率达到了90％，进行多类识别，平均识别率达到了91．5％。     

AdaBoost-EHMM算法及其在行为识别中的应用

隐马尔可夫模型（Hidden Markov Model,HMM）是一种有效的时序信号建模方法,已广泛用于语音识别、文字识别等领域,近年来也被用于人的行为识别。人的行为序列是一种特殊的时序信号,每类行为往往包含若干帧关键姿势。利用行为序列的这个特点,提出了AdaBoost-EHMM（AdaBoost-Exemplar-based HMM）算法,并将该算法应用于行为识别中。利用AdaBoost的特征选择方法将行为序列中的典型样本逐个选择出来作为HMM观测概率模型的均值,之后融合多级分类器进行行为识别。实验结果证明AdaBoost-EHMM算法在保证算法收敛的同时提高了识别率。     

一种改进的隐马尔可夫模型在语音识别中的应用

提出了一种新的马尔可夫模型——异步隐马尔可夫模型．该模型针对噪音环境下语音识别过程中出现丢失帧的情况，通过增加新的隐藏时间标示变量Ck，估计出实际观察值对应的状态序列，实现对不规则或者不完整采样数据的建模．详细介绍了适合异步HMM的前后向算法以及用于训练的EM算法，并且对转移矩阵的计算进行了优化．最后通过实验仿真，分别使用经典HMM和异步HMM对相同的随机抽取帧的语音数据进行识别,识别结果显示在抽取帧相同情况下异步HMM比经典HMM的识别错误率低．     

基于二阶隐马尔可夫模型的连续手语识别

在传统的一阶隐马尔可夫模型(HMM1)中,状态序列中的每一个状态被假设只与前一个状态有关,这样虽然可以简单、有效地推导出模型的学习和识别算法,但也丢失了许多从上文传递下来的信息.因此,在传统一阶隐马尔可夫模型的基础上,为了解决手语识别困难、正确率低的问题,提出了一种基于二阶隐马尔可夫模型(HMM2)的连续手语识别方法....     

一种面向加密流量的网络应用识别方法

为实现网络流量的有效管控,提出一种基于安全套接层(SSL)协议交互字段与多输入最大化单输出隐马尔可夫模型(HMM)的加密应用并行识别方法.将来自客户端或者服务器的单向数据流SSL协议交互阶段的字段作为HMM模型的观测序列,并对所有待识别的加密应用建立HMM模型形成指纹库.在此基础上,利用前向算法计算未知观测序列被识别为HMM模型的概率,选取概率最大HMM模型所对应的加密应用作为识别结果.实验结果表明,与传统应用识别方法相比,该方法对典型加密应用具有更好的识别效果及鲁棒性.     

基于乘积HMM的双模态语音识别方法

针对噪声环境中的语音识别,提出一种用于双模态语音识别的乘积隐马尔可夫模型(HMM)。在独立训练音频HMM和视频HMM的基础上,建立二维训练模型,表征音频流和视频流之间的异步特性。引入权重系数,根据不同噪声环境自适应调整音频流与视频流的权重。实验结果证明,与其他双模态语音识别方法相比,该方法的识别性能更高。     

基于连续隐马尔可夫模型的人脸识别方法

提出了一种基于连续隐马尔可夫模型的人脸图像识别方法,主要内容包括以下方面:①由于奇异值向量具有稳定性.转置不变性等特点,对归一化的人脸图像,采用奇异值分解抽取人脸图像特征作为观察值序列;②在人脸识别中应用连续隐马尔可夫模型,采用双高斯概率密度函数训练,建立HMM模型,再利用建好的HMM模型进行识别.实验结果显示,所提出的方法减少了数据计算量,运行速度快,并提高了识别率,完全满足人脸识别系统实时性要求.     

隐马尔可夫模型的多序列比对研究

研究一种关于隐马尔可夫模型的多序列比对,利用值和特征序列的保守性,通过增加频率因子,改进传统隐马尔可夫模型算法的不足。实验表明,新算法不但提高了模型的稳定性,而且应用于蛋白质家族识别,平均识别率比传统隐马尔可夫算法提高了3.3个百分点。     

基于HMM-FNN模型的复杂动态手势识别

复杂动态手势识别是利用视频手势进行人机交互的关键问题.提出一种HMM-FNN模型结构.它整合了隐马尔可夫模型对时序数据的建模能力与模糊神经网络的模糊规则构建与推理能力,并将其应用到复杂动态手势的识别中.复杂动态手势具备两大特点:运动特征的可分解性与定义描述的模糊性.针对这两种特性,复杂手势被分解为手形变化、2D平面运动与Z轴方向运动3个子部分,分别利用HMM进行建模,HMM模型对观察子序列的似然概率被作为FNN的模糊隶属度,通过模糊规则推理,最终得到手势的分类类别.HMM-FNN方法将高维手势特征分解为低维子特征序列,降低了模型的复杂度.此外,它还可以充分利用人的经验辅助模型结构的创建与优化.实验表明,该方法是一种有效的复杂动态手势识别方法,并且优于传统的HMM模型方法.     

基于改进HMM的模拟电路早期故障识别和诊断

针对模拟电路运行过程中存在的不确定性,对传统的隐马尔可夫模型（HMM）进行了改进,将模型中满足不变性的状态转移概率矩阵改为时变状态转移概率矩阵,使之更符合实际情况。在状态初期为了防止状态转移概率发生过度更新,设置了更新概率控制因子。采用线性辨别分析（LDA）方法对测量信号进行特征提取,用于HMM的训练和测试,从而实现模拟电路早期故障的识别和诊断。仿真结果表明,改进后的HMM具有更强的故障识别和诊断能力。     

基于拉普拉斯脸和隐马尔可夫的视频人脸识别

提出了一种基于拉普拉斯脸和隐马尔可夫模型的视频人脸识别方法。在训练过程中，采用拉普拉斯脸方法将每一视频序列中的人脸图像映射到拉普拉斯空间，将降维后的特征作为观测值，通过隐马尔可夫模型得到每一训练视频的统计特性和时间动态特性。在识别过程中，用每一个训练视频的隐马尔可夫模型来分析测试视频的时间动态特性，计算出每一训练模型产生该序列的概率，概率最大值所对应的模型就是待识别序列所属的类别。实验结果表明，该方法能够很好地进行视频人脸识别。     

A heterogeneous speech feature vectors generation approach with hybrid hmm classifiers

Automatic speech recognition (ASR) system plays a vital role in the human–machine interaction. ASR system faces the challenge of performance degradation due to inconsistency between training and testing phases. This occurs due to extraction and representation of erroneous, redundant feature vectors. This paper proposes three different combinations at speech feature vector generation phase and two hybrid classifiers at modeling phase. In feature extraction phase MFCC, RASTA-PLP, and PLP are combined in different ways. In modeling phase, the mean and variance are calculated to generate the inter and intra class feature vectors. These feature vectors are further adopted by optimization algorithm to generate refined feature vectors with traditional statistical technique. This approach uses GA?+?HMM and DE?+?HMM techniques to produce refine model parameters. The experiments are conducted on datasets of large vocabulary isolated Punjabi lexicons. The simulation result shows the performance improvement using MFCC and DE?+?HMM technique when compared with RASTA-PLP, PLP using hybrid HMM classifiers.     

基于差分过零峰值幅度特征的抗噪语音识别

针对多数语音识别系统在噪音环境下性能急剧下降的问题,提出了一种新的语音识别特征提取方法。该方法是建立在听觉模型的基础上,通过组合语音信号和其差分信号的上升过零率获得频率信息,通过峰值检测和非线性幅度加权来获取强度信息,二者组合在一起,得到输出语音特征,再分别用BP神经网络和HMM进行训练和识别。仿真实现了不同信噪比下不依赖人的50词的语音识别,给出了识别的结果,证明了组合差分信息的过零与峰值幅度特征具有较强的抗噪声性能。     

Incorporating Intra‐Query Term Dependencies in an Aspect Query Language Model

Query language modeling based on relevance feedback has been widely applied to improve the effectiveness of information retrieval. However, intra‐query term dependencies (i.e., the dependencies between different query terms and term combinations) have not yet been sufficiently addressed in the existing approaches. This article aims to investigate this issue within a comprehensive framework, namely the Aspect Query Language Model (AM). We propose to extend the AM with a hidden Markov model (HMM) structure to incorporate the intra‐query term dependencies and learn the structure of a novel aspect HMM (AHMM) for query language modeling. In the proposed AHMM, the combinations of query terms are viewed as latent variables representing query aspects. They further form an ergodic HMM, where the dependencies between latent variables (nodes) are modeled as the transitional probabilities. The segmented chunks from the feedback documents are considered as observables of the HMM. Then the AHMM structure is optimized by the HMM, which can estimate the prior of the latent variables and the probability distribution of the observed chunks. Our extensive experiments on three large‐scale text retrieval conference (TREC) collections have shown that our method not only significantly outperforms a number of strong baselines in terms of both effectiveness and robustness but also achieves better results than the AM and another state‐of‐the‐art approach, namely the latent concept expansion model. © 2014 Wiley Periodicals, Inc.     

Face recognition based on multi-class mapping of Fisher scores

A new hidden Markov model (HMM) based feature generation scheme is proposed for face recognition (FR) in this paper. In this scheme, HMM method is used to model classes of face images. A set of Fisher scores is calculated through partial derivative analysis of the parameters estimated in each HMM. These Fisher scores are further combined with some traditional features such as log-likelihood and appearance based features to form feature vectors that exploit the strengths of both local and holistic features of human face. Linear discriminant analysis (LDA) is then applied to analyze these feature vectors for FR. Performance improvements are observed over stand-alone HMM method and Fisher face method which uses appearance based feature vectors. A further study reveals that, by reducing the number of models involved in the training and testing stages of LDA, the proposed feature generation scheme can maintain very high discriminative power at much lower computational complexity comparing to the traditional HMM based FR system. Experimental results on a public available face database are provided to demonstrate the viability of this scheme.     

Independent shape component-based human activity recognition via Hidden Markov Model

In proactive computing, human activity recognition from image sequences is an active research area. In this paper, a novel human activity recognition method is proposed, which utilizes Independent Component Analysis (ICA) for activity shape information extraction from image sequences and Hidden Markov Model (HMM) for recognition. Various human activities are represented by shape feature vectors from the sequence of activity shape images via ICA. Based on these features, each HMM is trained and activity recognition is achieved by the trained HMMs of different activities. Our recognition performance has been compared to the conventional method where Principal Component Analysis (PCA) is typically used to derive activity shape features. Our results show that superior recognition is achieved with the proposed method especially for activities (e.g., skipping) that cannot be easily recognized by the conventional method. Furthermore, by employing Linear Discriminant Analysis (LDA) on IC features, the recognition results further improved significantly in the recognition performance.