EMG-based speech recognition using hidden markov models with global control variables

It is well known that a strong relationship exists between human voices and the movement of articulatory facial muscles. In this paper, we utilize this knowledge to implement an automatic speech recognition scheme which uses solely surface electromyogram (EMG) signals. The sequence of EMG signals for each word is modelled by a hidden Markov model (HMM) framework. The main objective of the work involves building a model for state observation density when multichannel observation sequences are given. The proposed model reflects the dependencies between each of the EMG signals, which are described by introducing a global control variable. We also develop an efficient model training method, based on a maximum likelihood criterion. In a preliminary study, 60 isolated words were used as recognition variables. EMG signals were acquired from three articulatory facial muscles. The findings indicate that such a system may have the capacity to recognize speech signals with an accuracy of up to 87.07%, which is superior to the independent probabilistic model.     

Planar shape recognition based on hidden Markov models

A new deformed shape recognition method based on hidden Markov models (HMMs), which is very resistant against transformations and non-rigid deformations, is presented. Since shape features are not referred to an absolute point, the method is also resistant to severe shape distortions. The method has been successfully tested using different databases     

基于不同隐马尔科夫模型的图像识别方法

为了更详细地研究隐马尔科夫模型在图像识别中的应用,以指纹识别为例,纵向总结了几种基于隐马尔科夫模型的指纹图像识别算法,包括一维隐马尔科夫模型、伪二维隐马尔科夫模型、二维模型及一维模型组。分别从时间复杂度、识别精确度等方面总结出这四种隐马尔科夫模型在图像识别时的优缺点,得出不同待识别图像适合使用的识别模型的结论。     

Curvature based image recognition using hidden Markov models

A new deformed shape recognition method that relies on hidden Markov models to evaluate the sequentiality of the relevant points of the shape is proposed. These points are extracted from its adaptively calculated curvature function to give stability against noise transformations and deformations. The proposed method is very fast. Comparative tests for different shapes have been successful.     

Human gait recognition based on Haralick features

This paper proposes a supervised feature extraction approach that is capable of selecting distinctive features for the recognition of human gait under clothing and carrying conditions, thus improving the recognition performances. The principle of the suggested approach is based on the Haralick features extracted from gait energy image (GEI). These features are extracted locally by dividing vertically or horizontally the GEI locally into two or three equal regions of interest, respectively. RELIEF feature selection algorithm is then employed on the extracted features in order to select only the most relevant features with a minimum redundancy. The proposed method is evaluated on CASIA gait database (Dataset B) under variations of clothing and carrying conditions for different viewing angles, and the experimental results using k-NN classifier have yielded attractive results of up to 80% in terms of highest identification rate at rank-1 when compared to existing and similar state-of-the-art methods.     

结合小波域马尔可夫树模型的压缩采样图像重建

已有的研究表明基于模型的压缩采样信号重建可以取得更好的重建效果。本文提出一种结合小波域马尔可夫树模型的压缩采样图像重建方法。马尔可夫树模型很好的匹配了图像小波变换后的系数在尺度间的持续性。这种统计特性可以在正交匹配追踪算法中协助原子的选取,从而更准确的选取具有大幅值系数的原子。在本文提出的新算法中,每次迭代新增的原子是从与残差信号较匹配的候选原子中选取。候选原子中使模型的状态似然函数最大的原子被选出。实验结果表明,新算法可以更准确选出具有大系数原子,重建的图像质量好于其它传统方法。     

基于加速度传感器和神经网络的人体活动行为识别

人体活动行为识别在医疗、安全、娱乐等方面有着广泛的应用,为了高效、准确地获取人体活动的行为信息,提出一种基于加速度传感器和神经网络的个人活动行为识别方法。该方法通过在个人手上佩戴加速度传感器,实时采集个人活动的行为数据;再通过BP神经网络分析相关行为数据并建立个人活动行为模型,分类识别个人的行走、坐着、躺卧、站立和突然跌倒等活动行为特征。实验结果表明,该方法能够有效检测到个人活动的行为特征参数,并可准确识别出人体活动的五种典型行为。     

基于循环神经网络的人体异常行为识别模型

传统识别模型在进行人体异常行为识别时,无法准确地定位到识别目标的IP地址与目标源.针对此问题,设计了一种基于循环神经网络的人体异常行为识别模型.根据人体异常行为在循环神经网络中的传播方式,计算人体规律性异常行为、重复性异常行为在网络中占用的流量,并通过Lex.net技术提取网络规则,对比人体行为执行规则与循环神经网络规则,描述人体异常行为网络执行规则;同时,引进CNN标记方式,对异常信息进行标记,引入卷积神经网络标记异常信息,将标记结果按照高语义特征与低语义特征,以此实现对行为的有效识别.实验证明,本文设计的识别模型,可以在有限时间内输出所有人体异常行为,并能在完成对异常行为的识别后,追踪到行为对应的目标个体.     

基于RealSense的三维物体识别算法研究

分析比较了Intel推出的RealSense摄像头,与热门的Kinect摄像头之间的异同。针对RealSense没有全平台点云库支持的问题,给出基于Librealsense的数据获取转换流程。针对三维物体识别算法实时性较差的问题,根据目标物体颜色空间的特性,提出了改进的快速点特征直方图描述符算法。新算法利用目标物体的HSV颜色空间特征,提升了描述符间匹配的准确率,同时利用物体色调位图降低了场景描述符计算量。除此以外,利用综合滤波的方式,显著地提升了图像的有效信息量。     

Multi-pose 3D face recognition based on 2D sparse representation

The increasing availability of 3D facial data offers the potential to overcome the difficulties inherent with 2D face recognition, including the sensitivity to illumination conditions and head pose variations. In spite of their rapid development, many 3D face recognition algorithms in the literature still suffer from the intrinsic complexity in representing and processing 3D facial data. In this paper, we propose the intrinsic 3D facial sparse representation (I3DFSR) algorithm for multi-pose 3D face recognition. In this algorithm, each 3D facial surface is first mapped homeomorphically onto a 2D lattice, where the value at each site is the depth of the corresponding vertex on the 3D surface. Each 2D lattice is then interpolated and converted into a 2D facial attribute image. Next, the sparse representation is applied to those attribute images. Finally, the identity of each query face can be obtained by using the corresponding sparse coefficients. The innovation of our approach lies in the strategy of converting irregular 3D facial surfaces into regular 2D attribute images such that 3D face recognition problem can be solved by using the sparse representation of those attribute images. We compare the proposed algorithm to three widely used 3D face recognition algorithms in the GavabDB database, to six state-of-the-art algorithms in the FRGC2.0 database, and to three baseline algorithms in the NPU3D database. Our results show that the proposed I3DFSR algorithm can substantially improve the accuracy and efficiency of multi-pose 3D face recognition.     

Color image retrieval based on hidden Markov models

In this correspondence, a new approach to retrieving images from a color image database is proposed. Each image in the database is represented by a two-dimensional pseudo-hidden Markov model (2-D PHMM), which characterizes the chromatic and spatial information about the image. In addition, a flexible pictorial querying method is used, by which users can paint the rough content of the desired images in a query picture. Image matching is achieved by comparing the query picture with each 2-D PHMM in the database. Experimental results show that the proposed approach is indeed effective.     

A probabilistic framework based on hidden markov model for fiducial identification in image-guided radiation treatments

Fiducial tracking is a common target tracking method widely used in image-guided procedures such as radiotherapy and radiosurgery. In this paper, we present a multifiducial identification method that incorporates context information in the process. We first convert the problem into a state sequence problem by establishing a probabilistic framework based on a hidden Markov model (HMM), where prior probability represents an individual candidate's resemblance to a fiducial; transition probability quantifies the similarity of a candidate set to the fiducials' geometrical configuration; and the Viterbi algorithm provides an efficient solution. We then discuss the problem of identifying fiducials using stereo projections, and propose a special, higher order HMM, which consists of two parallel HMMs, connected by an association measure that captures the inherent correlation between the two projections. A novel algorithm, the concurrent viterbi with association (CVA) algorithm, is introduced to efficiently identify fiducials in the two projections simultaneously. This probabilistic framework is highly flexible and provides a buffer to accommodate deformations. A simple implementation of the CVA algorithm is presented to evaluate the efficacy of the framework. Experiments were carried out using clinical images acquired during patient treatments, and several examples are presented to illustrate a variety of clinical situations. In the experiments, the algorithm demonstrated a large tracking range, computational efficiency, ease of use, and robustness that meet the requirements for clinical use.      

基于骨架的人体识别

本文采用了人体骨架模型的方法,通过距离变换的算法对人体进行了骨架特征提取,同时用腐蚀和还原的算法对骨架的细小分支进行剔除,保留了骨架的主要特征,取得了较好的效果。     

基于三维卷积与双向LSTM的行为识别研究

准确识别视频中的内容是未来互联网应用发展的方向,视频中的行为识别是计算机视觉领域的研究重点。为充分利用视频中的信息,提高行为识别的准确程度,文中提出一种基于三维卷积与双向LSTM的行为识别算法。设计一种基于三维卷积的空间注意模块,可以关注空间区域的显著特征。为了更好地处理长时间视频,引入一种新的基于双向LSTM(长短时记忆网络)的时间注意模块,其目的在于关注关键视频而不是给定视频的关键视频帧,然后采用双中心loss(计算损失函数)优化网络对两阶段策略联合训练,使其能够同时探索空间和时间域的相关性。在HMDB-51和UCF-101数据集上测试证明,所提算法能够准确识别视频中的相似动作,行为识别的准确率得到提高,识别效果显著。     

基于局部小波矩的易混淆三维目标识别

使用多视点特性视图方法进行易混淆三维目标识别时,由于目标具有相似的轮廓,必须辅以局部特征提取以提高识别率.传统的小渡矩仅具有径向区间上的局部性,不具有角度区间上的局部性,因此提取的特征不能较好地分辨易混淆目标.利用信息采样方法,首先获得目标视图的先验信息,将目标视图分为若干个区域,由贝叶斯后验估计,分别计算基于这些区域的后验信息,然后比较其与先验信息的差异,即可获得视图中最具有分辨力的局部区域,计算这个区域上的小波矩,即可获得具有一定角度区间上的局部小波矩.研究表明,与传统的局部特征提取方法相比,这种新算法提取的局部特征具有更好的局部性,可以有效地应用于易混淆三维目标识别.     

Object trajectory-based activity classification and recognition using hidden Markov models.

Motion trajectories provide rich spatiotemporal information about an object's activity. This paper presents novel classification algorithms for recognizing object activity using object motion trajectory. In the proposed classification system, trajectories are segmented at points of change in curvature, and the subtrajectories are represented by their principal component analysis (PCA) coefficients. We first present a framework to robustly estimate the multivariate probability density function based on PCA coefficients of the subtrajectories using Gaussian mixture models (GMMs). We show that GMM-based modeling alone cannot capture the temporal relations and ordering between underlying entities. To address this issue, we use hidden Markov models (HMMs) with a data-driven design in terms of number of states and topology (e.g., left-right versus ergodic). Experiments using a database of over 5700 complex trajectories (obtained from UCI-KDD data archives and Columbia University Multimedia Group) subdivided into 85 different classes demonstrate the superiority of our proposed HMM-based scheme using PCA coefficients of subtrajectories in comparison with other techniques in the literature.     

基于隐马尔可夫模型的车牌自动识别技术

文中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方法识别车牌中的汉字,用伪二维隐马尔可夫模型（P2D－HMM）方法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,满足实用技术的要求。     

基于隐马尔可夫模型的连续语音同步识别系统

语音同步识别系统的发展方向是连续性的人机交互,采用传统系统易受到突发性噪声影响,致使识别效果较差,提出基于隐马尔可夫模型的连续语音同步识别系统。结合语音识别原理,设计系统硬件总体结构。利用JFET输入高保真运放的OPA604低通滤波器,保证信号处理结果的有效性。通过OMAP5912ZZG型号芯片对处理后的信号进行存储,使用矢量图缓冲音频,经由以太网接口移植相关语音识别序列,由此实现连续语音同步识别。由实验对比结果可知,该系统比传统系统识别效果最高值高出48%,推进了语音识别技术研究的快速发展。     

基于改进特征的序列图像三维重建

近年来序列图像三维重建技术得到了重视,提出了很多算法.本文对Harris特征提取算法进行了改进,并在在序列图像中进行特征跟踪,在特征的稀疏重建和稠密重建后,使用估计样本一致性算法实现三维重建,提高了序列图像三维重建的精度.