Human recognition system for outdoor videos using Hidden Markov model

Human recognition is an essential requirement for human-centric surveillance, activity recognition, gait recognition etc. Inaccurate recognition of humans in such applications may leads to false alarm and unnecessary computation. In the proposed work a robust background modeling algorithm using fuzzy logic is used to detect foreground objects. Three distinct features are extracted from the contours of detected objects. An unique aggregated feature vector is formed using a fuzzy inference system by aggregating three feature vectors. To minimize computation in recognition using Hidden Markov model (HMM), the length of final feature vector is reduced using vector quantization. The proposed method is explained using five basic phases; background modeling and foreground object detection, features extraction, aggregated feature vector calculation, vector quantization, and recognition using Hidden Markov model.     

Hidden Markov models for character recognition

A hierarchical system for character recognition with hidden Markov model knowledge sources which solve both the context sensitivity problem and the character instantiation problem is presented. The system achieves 97-99% accuracy using a two-level architecture and has been implemented using a systolic array, thus permitting real-time (1 ms per character) multifont and multisize printed character recognition as well as handwriting recognition.     

Hidden Markov Models With Stick-Breaking Priors

The number of states in a hidden Markov model (HMM) is an important parameter that has a critical impact on the inferred model. Bayesian approaches to addressing this issue include the nonparametric hierarchical Dirichlet process, which does not extend to a variational Bayesian (VB) solution. We present a fully conjugate, Bayesian approach to determining the number of states in a HMM, which does have a variational solution. The infinite-state HMM presented here utilizes a stick-breaking construction for each row of the state transition matrix, which allows for a sparse utilization of the same subset of observation parameters by all states. In addition to our variational solution, we discuss retrospective and collapsed Gibbs sampling methods for MCMC inference. We demonstrate our model on a music recommendation problem containing 2250 pieces of music from the classical, jazz, and rock genres.     

基于隐马尔可夫模型(HMM)的人脸表情识别

人脸表情识别是目前的研究热点．文中介绍了人脸表情识别的过程，给出了基于隐马尔可夫模型（HMM）的人脸表情识别方法．通过分析人脸表情的变化情况，利用二维离散余弦变换（2D—DCT）提取脸部表情特征，经过大样本训练构建HMM模型来识别图像中的人脸表情．实验结果表明该方法是一种高效的面部表情识别方法。     

Music Analysis Using Hidden Markov Mixture Models

We develop a hidden Markov mixture model based on a Dirichlet process (DP) prior, for representation of the statistics of sequential data for which a single hidden Markov model (HMM) may not be sufficient. The DP prior has an intrinsic clustering property that encourages parameter sharing, and this naturally reveals the proper number of mixture components. The evaluation of posterior distributions for all model parameters is achieved in two ways: 1) via a rigorous Markov chain Monte Carlo method; and 2) approximately and efficiently via a variational Bayes formulation. Using DP HMM mixture models in a Bayesian setting, we propose a novel scheme for music analysis, highlighting the effectiveness of the DP HMM mixture model. Music is treated as a time-series data sequence and each music piece is represented as a mixture of HMMs. We approximate the similarity of two music pieces by computing the distance between the associated HMM mixtures. Experimental results are presented for synthesized sequential data and from classical music clips. Music similarities computed using DP HMM mixture modeling are compared to those computed from Gaussian mixture modeling, for which the mixture modeling is also performed using DP. The results show that the performance of DP HMM mixture modeling exceeds that of the DP Gaussian mixture modeling.     

隐马尔可夫模型在生物信息学中的应用

结合DNA序列分析例子,介绍了HMMs与其的解码、估计、学习3个计算问题。综述了HMMs在生物信息学中的应用情况,同时对HMMs在生物信息学中可能的发展方向进行了展望。     

Trajectory-based multi-dimensional outlier detection in wireless sensor networks using Hidden Markov Models

Wireless sensor networks (WSNs) have been increasingly available for monitoring the traffic, weather, pollution, etc. Outlier detection in WSNs is an essential step for many important applications, such as abnormal event detection, fraud analysis, etc. While existing efforts focus on identifying individual outliers from sensory data, the unsupervised high semantic outlier detection in WSNs is more challenging and has received far less attentions. In addition, the correlation between multi-dimensional sensory data has not yet been considered when detecting outliers in WSNs. In this paper, based on multi-dimensional Hidden Markov Models, we propose a trajectory-based outlier detection algorithm by model training and model-based likelihood estimation. Our data preprocessing, clustering, model training and model updating schemes are developed to reduce the computational complexity and enhance the detecting performance. We also explore the possibility and feasibility of adapting the proposed algorithm to real-time outlier detections. Experimental results show that our methods achieve good performance on detecting various kinds of abnormal trajectories composed of multi-dimensional data.     

Facial expression feature extraction using hybrid PCA and LBP

In order to recognize facial expression accurately, the paper proposed a hybrid method of principal component analysis (PCA) and local binary pattern (LBP). Firstly, the method of eight eyes segmentation was introduced to extract the effective area of facial expression image, which can reduce some useless information to subsequent feature extraction. Then PCA extracted the global grayscale feature of the whole facial expression image and reduced the data size at the same time. And LBP extracted local neighbor texture feature of the mouth area, which contributes most to facial expression recognition. Fusing the global and local feature will be more effective for facial expression recognition. Finally, support vector machine (SVM) used the fusion feature to complete facial expression recognition. Experiment results show that, the method proposed in this paper can classify different expressions more effectively and can get higher recognition rate than the traditional recognition methods.     

Using Hidden Markov Models in Vehicular Crash Detection

This paper presents a system for automotive crash detection based on hidden Markov models (HMMs). The crash pulse library used for training comprises a number of head-on and oblique angular crash events involving rigid and offset deformable barriers. Stochastic distribution characteristics of crash signals are validated to ensure conformity with the modeling assumptions. This step is achieved by analyzing the quantile–quantile (Q–Q) plot of actual pulses against the assumed bivariate Gaussian distribution. HMM parameters are next induced by utilizing the expectation–maximization (EM) procedure. The search for an optimal crash pulse model proceeds using the “leave-one-out” technique with the exploration encompassing both fully connected and left–right HMM topologies. The optimal crash pulse architecture is identified as a seven-state left–right HMM with its parameters computed using real and computer-aided engineering (CAE)-generated data. The system described in the paper has the following advantages. First, it is fast and can accurately detect crashes within 6 ms. Second, its implementation is simple and uses only two sensors, which makes it less vulnerable to failures, considering the overall simplicity of interconnects. Finally, it represents a general and modularized algorithm that can be adapted to any vehicle line and readily extended to use additional sensors.      

Structural Bayesian Linear Regression for Hidden Markov Models

Linear regression for Hidden Markov Model (HMM) parameters is widely used for the adaptive training of time series pattern analysis especially for speech processing. The regression parameters are usually shared among sets of Gaussians in HMMs where the Gaussian clusters are represented by a tree. This paper realizes a fully Bayesian treatment of linear regression for HMMs considering this regression tree structure by using variational techniques. This paper analytically derives the variational lower bound of the marginalized log-likelihood of the linear regression. By using the variational lower bound as an objective function, we can algorithmically optimize the tree structure and hyper-parameters of the linear regression rather than heuristically tweaking them as tuning parameters. Experiments on large vocabulary continuous speech recognition confirm the generalizability of the proposed approach, especially when the amount of adaptation data is limited.     

Hidden Markov Models with Factored Gaussian Mixtures Densities

1　Introduction　Manyrealobserveddataarecharacterizedbymultiplecoupledcausesorfactors.Forinstance ,faceimagesmaybegeneratedbycombiningeyebrows,eyes ,noseandmouth .Similarly ,speechsignalsmayresultfromanin teractionofmotionsoffactorssuchasthejaw ,tongue ,velum ,lipandmouth .RecentlyZemelandHintonpro posedafactoriallearningarchitecture[1～ 2 ] todealwithfactorialdata .Thegoaloffactoriallearningistodiscov erthemultipleunderlyingcausesorfactorsfromtheob serveddataandfindarepresentationthatwillbo…     

2维隐马尔可夫模型的基本问题求解

研究了2维隐马尔可夫模型的三个基本问题,包括概率评估问题、最优状态问题和参数估计问题.通过把2维隐马尔可夫模型行或者列上的状态序列看作一个马尔可夫模型,从理论上分别给出了解决这三个基本问题的新算法;计算机仿真对新算法的实现和运行作了进一步的说明.     

Maximum a Posteriori Estimation of Coupled Hidden Markov Models

Coupled Hidden Markov Models (CHMM) are a tool which model interactions between variables in state space rather than observation space. Thus they may reveal coupling in cases where classical tools such as correlation fail. In this paper we derive the maximum a posteriori equations for the Expectation Maximisation algorithm. The use of the models is demonstrated on simulated data, as well as in a variety of biomedical signal analysis problems.     

Hidden Markov models applied to on-line handwritten isolatedcharacter recognition

Hidden Markov models are used to model the generation of handwritten, isolated characters. Models are trained on examples using the Baum-Welch optimization routine. Then, given the models for the alphabet, unknown characters can be classified using maximum-likelihood classification. Experiments have been conducted, and an average error rate of 6.9% was achieved over the alphabet consisting of the lowercase English alphabet.     

隐马氏过程在语音合成技术中的应用

不同于传统基于拼接(Concatenative)的语音合成技术,基于隐马氏模型(Hidden Markov Models)的参数统计语音合成技术(HMM-Based Speech Synthesis System,又名HTS)在过去十年中得到了长足的发展。相比传统的基于拼接的合成技术,HTS技术具有所需训练录音少、语音合成模型小、合成可调节灵活性强等特点,因而被广泛应用在语音合成领域特别是移动设备上。介绍了HTS的语音合成原理,HTS技术的一些典型应用场景,以及在此领域的最新研究发展方向。     

基于人脸非对称性的表情识别

阐述了一种基于人脸非对称性的表情识别算法。该算法借助非对称人脸（D-face和S-face）来表征人脸非对称性,利用增强型方差率（AVR）和改进的基于排序思想的方差率自动选取人脸非对称性特征,然后分别用欧氏距离和K-近邻（KNN）算法测定表情识别率。仿真结果表明,人脸非对称性测量包含有判决信息,应用在表情识别中可以有效提高识别的准确率。     

Novel Text Recognition Based on Modified K-Clustering and Hidden Markov Models

Wireless Personal Communications - Currently, many researchers have paid more attention to identifying scene texts from the image with background interferences. This study aims to develop an App...     

基于改进小波域隐马尔可夫模型的遥感图像分割

该文提出了一种基于改进小波域隐马尔可夫树(HMT)模型进行图像分割的方法。该方法利用基于希尔伯特变换对的二维方向小波,这种小波变换具有平移不变性、方向检测性好的特点。同时该方法还利用拓展HMT对该改进小波域中尺度间的小波系数相关性进行建模,并结合多背景融合技术进行遥感图像的分割,得到了优于已有文献的分割结果,而且与同类算法相比,降低了算法所需的计算量。     

Facial expression recognition using frequency multiplication network with uniform rectangular features

Facial expression recognition (FER) is a popular research field in cognitive interaction systems and artificial intelligence. Many deep learning methods achieve outstanding performances at the expense of enormous computation workload. Limiting their application in small devices or offline scenarios. To cope with this drawback, this paper proposes the Frequency Multiplication Network (FMN), a deep learning method operating in the frequency domain that significantly reduces network capacity and computation workload. By taking advantage of the frequency domain conversion, this novel deep learning method utilizes multiplication layers for effective feature extraction. In conjunction with the Uniform Rectangular Features (URF), our method further improves the performance and reduces the training effort. On three publicly available datasets (CK+, Oulu, and MMI), our method achieves substantial improvements in comparison to popular approaches.     

Context-Sensitive Hidden Markov Models for Modeling Long-Range Dependencies in Symbol Sequences

The hidden Markov model (HMM) has been widely used in signal processing and digital communication applications. It is well known for its efficiency in modeling short-term dependencies between adjacent symbols. However, it cannot be used for modeling long-range interactions between symbols that are distant from each other. In this paper, we introduce the concept of context-sensitive HMM. The proposed model is capable of modeling strong pairwise correlations between distant symbols. Based on this model, we propose dynamic programming algorithms that can be used for finding the optimal state sequence and for computing the probability of an observed symbol string. Furthermore, we also introduce a parameter re-estimation algorithm, which can be used for optimizing the model parameters based on the given training sequences.