Hidden Markov Models for crop recognition in remote sensing image sequences

This work proposes a Hidden Markov Model (HMM) based technique to classify agricultural crops. The method uses HMM to relate the varying spectral response along the crop cycle with plant phenology, for different crop classes, and recognizes different agricultural crops by analyzing their spectral profiles over a sequence of images. The method assigns each image segment to the crop class whose corresponding HMM delivers the highest probability of emitting the observed sequence of spectral values. Experimental analysis was conducted upon a set of 12 co-registered and radiometrically corrected LANDSAT images of region in southeast Brazil, of approximately 124.100 ha, acquired between 2002 and 2004. Reference data was provided by visual classification, validated through extensive field work. The HMM-based method achieved 93% average class accuracy in the identification of the correct crop, being, respectively, 10% and 26% superior to multi-date and single-date alternative approaches applied to the same data set.     

马尔可夫及隐马尔可夫模型在数据挖掘中的应用

随着用户对于数据挖掘的精确度与准确度要求的日益提高,马尔可夫模型与隐马尔可夫模型被广泛用于数据挖掘领域。本文阐述了马尔可夫模型和隐马尔可夫模型数据挖掘领域的应用,以及隐马尔可夫模型可解决的问题,以供其他研究者借鉴。     

马尔可夫及隐马尔可夫模型在数据挖掘中的应用

随着用户对于数据挖掘的精确度与准确度要求的日益提高,马尔可夫模型与隐马尔可夫模型被广泛用于数据挖掘领域。本文阐述了马尔可夫模型和隐马尔可夫模型数据挖掘领域的应用,以及隐马尔可夫模型可解决的问题,以供其他研究者借鉴。     

A Hidden Markov Model approach for appearance-based 3D object recognition

In this paper, a new appearance-based 3D object classification method is proposed based on the Hidden Markov Model (HMM) approach. Hidden Markov Models are a widely used methodology for sequential data modelling, of growing importance in the last years. In the proposed approach, each view is subdivided in regular, partially overlapped sub-images, and wavelet coefficients are computed for each window. These coefficients are then arranged in a sequential fashion to compose a sequence vector, which is used to train a HMM, paying particular attention to the model selection issue and to the training procedure initialization. A thorough experimental evaluation on a standard database has shown promising results, also in presence of image distortions and occlusions, the latter representing one of the most severe problems of the recognition methods. This analysis suggests that the proposed approach represents an interesting alternative to classic appearance-based methods to 3D object classification.     

DNA序列的二阶隐马尔科夫模型分类

隐马尔可夫模型是对DNA序列建模的一种简单且有效的模型, 实际应用中通常采用一阶隐马尔可夫模型. 然而, 由于其一阶无后效性的特点, 一阶隐马尔科夫模型无法表示非相邻碱基间的依赖关系, 从而导致序列中一些有用统计特征的丢失. 本文在分析DNA序列特有的生物学构造的基础上, 提出一种用于DNA序列分类的二阶隐马尔可夫模型, 该模型继承了一阶隐马尔可夫模型的优点, 充分表达了蕴涵在DNA序列中的生物学统计特征, 使得新模型具有明确的生物学意义. 基于新模型, 提出一种DNA序列的贝叶斯分类新方法, 并在实际DNA序列上进行了实验验证. 实验结果表明, 由于二阶隐马尔可夫模型充分反映了DNA序列碱基间的结构信息, 新方法有效地提高了序列的分类精度.     

对隐马尔科夫模型中动态编程的探讨

主要对隐马尔科夫模型中的三个问题（估计问题、译码问题、学习问题）中的译码问题作了研究．并对其应用进行了探讨。     

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。     

Multivariate time series anomaly detection: A framework of Hidden Markov Models

In this study, we develop an approach to multivariate time series anomaly detection focused on the transformation of multivariate time series to univariate time series. Several transformation techniques involving Fuzzy C-Means (FCM) clustering and fuzzy integral are studied. In the sequel, a Hidden Markov Model (HMM), one of the commonly encountered statistical methods, is engaged here to detect anomalies in multivariate time series. We construct HMM-based anomaly detectors and in this context compare several transformation methods. A suite of experimental studies along with some comparative analysis is reported.     

基于隐马尔可夫模型的音乐分类

音乐类型（Genre）是应用最普遍的管理数字音乐数据库的方式,提出一种基于隐马尔可夫模型（Hidden Markov Models,HMMs）的音乐自动分类方案。在考虑传统的音色特征（Timbre）的同时,将另一重要特征节奏（Tempo）也加以考虑,并通过bagging训练两组HMM进行分类,达到了良好的效果。从结构、状态数和混合高斯模型数三个方面进行了参数优化,找到了最佳的HMM参数。在音乐数据集GTZAN上对传统模型和新模型分类效果进行了测试,结果表明考虑了节奏特征的HMM分类效果更佳。     

Realistic disturbance modeling using Hidden Markov Models: Applications in model-based process control

Understanding and modeling disturbances play a critical part in designing effective advanced model-based control solutions. Existing linear, stationary disturbance models are oftentimes limiting in the face of time-varying characteristics typically witnessed in process industries. These include intermittent drifts, abrupt changes, temporary oscillations, outliers and the likes. This work proposes a Hidden Markov Model-based framework to deal with such situations that exhibit discrete, modal behavior. The usefulness of the proposed disturbance framework – from modeling to ensuring the integral action under a wide variety of scenarios – is demonstrated through several examples.     

基于隐马尔可夫模型的行为轨迹还原算法

针对行为轨迹还原过程中观察序列状态缺失、无法对终端轨迹进行精确还原的问题,提出一种基于隐马尔可夫模型的行为轨迹还原算法。利用基站布局的空间相关性,在不考虑缺失观察状态的情况下,对隐马尔可夫模型求解过程中的局部概率进行修订,还原出轨迹序列。性能分析和仿真结果表明,状态倾向度越大,轨迹还原成功率越高,当状态倾向度取0.8时,轨迹还原成功率在90%左右。     

IP包间关联异常检测的免疫隐马尔柯夫模型

数据包是网络通信的基本单位,网络上的入侵行为都应该在数据包中以不同的形式存在、表达着,而且网络中流通的数据包之间必然存在一定的关联性。人工免疫以往的研究多集中在对单个数据包的分析,很难察觉到隐蔽的、缓慢的入侵行为（这些行为的数据包序列间大多有一定的关联性）。因此,该文对于相互联系的多个数据包,挖掘出它们之间的关联特征,并针对这些特征采用了隐马尔柯夫模型的自动机识别器来检测入侵。     

一种基于HMM的维吾尔文联机手写识别的方法

在维吾尔文联机手写识别过程的训练阶段,单词被切分成字母,经过特征提取和聚类形成特征向量作为模型的输入。构造出以字符为基元的隐马尔可夫模型（HMM）,将其嵌入到识别字典网络中。通过基于HMM的分类识别器,最终得到识别结果。首次将消除延迟笔画、建立有延迟笔画和无延迟笔画的字典的方法应用于维吾尔文手写识别中,取得了较高的识别率。     

基于HMM-SVM的图像型火焰识别

针对单一的隐马尔科夫模型在图像型火灾探测中误报率偏高的问题,提出了隐马尔科夫模型和支持向量机相结合的图像型火焰识别算法。对捕获到的图像进行运动区域检测和颜色分析,提取疑似火焰区域,利用隐马尔科夫模型计算疑似区域与火焰模型的相似度,并输入到训练好的支持向量机进行二次识别。实验结果表明,与传统单一隐马尔科夫模型相比,该方法可以有效地降低误报率,提高火焰识别准确性。     

基于离散HMM的眉毛识别方法研究

为说明人类的眉毛作为一种生物特征使用的可能性和可行性,提出了一种基于离散HMM的眉毛识别方法,并对它的识别率随观察符号个数和模型状态数的变化关系进行了初步的实验研究。实验结果表明,该方法在一个27人的小规模眉毛数据库上最高识别率可以达到92.6%。     

The Realization Problem for Hidden Markov Models

If {X _t} is a finite-state Markov process, and {Y _t} is a finite-valued output process with Y _t+1 depending (possibly probabilistically) on X _t, then the process pair is said to constitute a hidden Markov model. This paper considers the realization question: given the probabilities of all finite-length output strings, under what circumstances and how can one construct a finite-state Markov process and a state-to-output mapping which generates an output process whose finite-length strings have the given probabilities? After reviewing known results dealing with this problem involving Hankel matrices and polyhedral cones, we develop new theory on the existence and construction of the cones in question, which effectively provides a solution to the realization problem. This theory is an extension of recent theoretical developments on the positive realization problem of linear system theory. Date received: December 13, 1996. Date revised: October 9, 1998.     

基于隐马尔科夫模型的DNA序列分类方法

DNA序列分类是生物信息学的一项基础任务,目的是根据结构或功能的相似性预测DNA序列所属的类别。为进行有效分类,如何将序列映射到特征向量空间并最大程度地保留序列中蕴含的碱基间顺序关系是一项困难的任务。为克服现有方法容易导致因DNA序列碱基残缺而影响分类精度等问题,提出一种新的DNA序列特征表示方法。新方法首先为每条序列训练一个隐马尔科夫模型（HMM）,然后将DNA序列投影到由HMM状态转移概率矩阵的特征向量构成的向量空间中。基于这种新的特征表示法,构造了一种 K-NN分类器对DNA序列进行分类。实验结果表明,新型特征表示方法可以较为完整地保留 DNA 序列中不同碱基间的关系,充分反映序列的结构信息,从而有效提高了序列的分类精度。     

基于隐马尔可夫模型的车牌自动识别技术

本文基于隐马尔可夫模型（HMM）提出了一种车牌字符识别的新方法,用二维隐马尔可夫模型（2D－HMM）方法来识别车牌中的汉字,用伪二 维隐马尔可夫模型（P2DHMM）方法来识别车牌中的英文字符及阿拉伯数字。该算法适用于不同的字符大小、字符倾斜,污损等情况,抗噪声能力强。字符识别正确率达94％以上,具有实用技术的指标。