鼠标手势的工效学研究进展

鼠标手势作为鼠标输入和手势输入的结合体,在基于WIMP的人机交互环境中开始得到日益广泛的关注和应用。文章在简要回顾鼠标手势的发展历程基础上,重点介绍鼠标手势近年来用户体验相关研究成果,总结了典型的鼠标手势识别模型及算法,并探讨了鼠标手势的商业应用和发展前景。     

基于双重优化的连续手势识别方法

基于计算机视觉的连续手势识别因为其自然性和便捷性在大型互动娱乐、互动教育等方面得到了广泛应用.在连续手势识别过程中,解决手势分割问题的已有方案多存在计算量大效率低的缺点;解决独立手势识别问题的已有方案多存在训练参数设定过程复杂的缺点.针对这两个问题做出两点优化:其一,提出一种基于隐状态模式归一化的方法对连续手势进行分割,提高了手势分割的效率;其二,提出一种基于参数自反馈调节的独立手势训练和识别方法,降低了独立手势训练的难度,并提高了识别的精度.实验证明,提出的基于双重优化的连续手势识别方法与原有方法相比在精度和效率上都有较大提升.     

基于虚拟试验箱的康复手势识别算法研究

研究了基于自主研发的手部康复训练系统的康复手势识别方法。针对现有手势识别算法识别手势过于单一、不具备针对性的问题,通过对手部功能障碍患者的手部运动及控制力的分析,提出了一种新的基于虚拟试验箱的康复手势识别算法。该算法的核心是利用摄像头捕捉不同的康复手势,并通过辅助训练标志板来实现手和辅助康复器械定位。基于改进的形状上下文识别算法的处理器实现了康复手势识别功能,并进一步控制虚拟场景中的物体做出相应的反应。该算法可以完成推、拉、悬垂、托举、二指捏等典型康复手势的准确识别,并与现有的手势识别算法进行了准确的对比。实验结果表明,该算法在识别率上有一定的提高,并且在识别的手势上更具有针对性。     

融合批量再标准化和YOLOv3的手势识别研究

针对复杂场景下手势分割准确性低,手势细粒度特征描述不充分和手势识别实时性差的问题,提出融合批量再标准化和YOLOv3的手势识别算法。首先,在复杂背景及不同光照条件下采集20种手势,运用数据增广策略进行样本扩充并建立标准手势库;然后通过K均值维度聚类获取训练集手势锚点框,负责对不同尺度手势进行检测;最后利用迁移学习和微调方法训练得到手势识别模型。为解决YOLOv3网络在手势训练阶段和预测阶段进行规范化处理时数据间可能存在较大偏差问题,采用批量再标准化方法提高手势识别准确性。手势识别过程具有快速、准确、非接触的优势,实验表明在正常实验环境下,手势平均识别率为97.6%,对于复杂背景下干扰较大的手势平均识别率达到89.2%以上,单次手势识别速度为0.04s。     

基于平面电容传感器阵列的动态手势识别技术

针对人机交互中对于动态手势识别技术的需求,设计了一种新的平面电容传感阵列对动态手势进行检测。通过COMSOL Multiphysics对传感器阵列结构进行仿真设计,采用卡尔曼滤波算法对手部运动坐标数据进行滤波,并借鉴基于视觉的手势识别中的隐马尔可夫模型(HMM)方法对手部运动轨迹进行识别。该系统对动态手势图形的平均识别率高达95%,可用于简单的人机交互。     

手势识别技术及其在人机交互中的应用

手势是一种自然、直观、易于学习的人机交互手段。手势输入是实现自然，直接人机交互不可缺少的关键技术，目前的手势识别技术主要分为基于数据手套和基于视觉两种，这两种方法各有自己的长处，也都取得了一些研究成果，但都还不成熟，手势输入作为一种自然、丰富、直接的交互手段在人机交互技术右占有重要的地位。     

基于分块局部纹理描述的手势识别

提出了一种用于解决手势识别问题的新的纹理描述,该描述基于一种有效的纹理编码模式——局部二值模式(LBP)。该方法对图像中的每一个像素进行LBP变换,提取其邻域内的亮度变化以作为特征来刻画像素点。为了更好地保持图像的空间结构信息,采用一种分块策略以获取对手势的更加鲁棒的描述。该策略将每一小块对应的局部直方图连接起来,形成一个完整的向量,即生成具有很强描述能力的纹理特征。从本质上讲,该方法有效地将全局结构信息和局部纹理信息结合起来,共同作用得到非常好的结果。在Jochen Triesch数据库的实验结果表明,与其他方法相比,这种基于分块局部纹理描述的手势识别方案具有显著的优势。     

基于人体动作识别的类人机器人动作模仿

基于模仿学习以及人机交互技术,借助Kinect深度相机传感器对类人机器人上半身动作模仿问题进行了研究。首先,将改进的D-H模型应用于NAO机器人的手臂完成了手臂运动学模型的精确建立及求解,解决了传统建模方法两相邻关节平行时出现的奇异性问题。其次,提出了一种改进的基于深度图像的手势识别算法,完成了对于示教者手势的判定及模仿,与传统基于彩色图像的手势识别相比,不受光照影响的同时提升了识别准确率,改进算法的平均识别准确率达到96.2%。最后将NAO机器人作为试验平台的实验表明:NAO机器人对于示教者上半身动作的实时在线模仿运动轨迹平滑且稳定,并且在抓取实验中也显现出了较好的准确性。     

基于启发式错误驱动学习的中文时间表达式识别

提出了一种基于启发式错误驱动学习的中文时间表达式识别的新方法.该方法先采用依存分析方法以时间触发词为切入点递归地识别时间表达式,有效地解决了长距离依赖的问题,大大提高了识别效果;在此基础上,对比错误识别结果和人工标注,采用启发式A*算法搜索策略进行错误驱动学习,降低了规则学习的复杂度,并具有区分每条规则的有效性和规则间相容性的优点,使系统性能提高近6%.最终在封闭测试集和开放测试集上,F值分别达到了77.96%和77.92%.     

改进遗传算法在非线性热传导参数识别中的应用

建立了基于优化算法的估计材料热传导系数和边界条件的热传导反问题求解方法。该方法以观测的温度值与有限元计算模拟的温度值最小二乘极小化原理为基础,然后采用具有全局搜索能力的遗传算法求解。为了加快收敛速度和提高反演识别精度,采用了浮点编码的遗传算法。根据先验信息,建立了高斯变异策略。数值计算结果表明,所建立的数值反演方法可以用来解决未知的热传导系数和边界条件识别问题,并且具有良好的抗观测噪音能力。     

Improved VGG Model for Road Traffic Sign Recognition

Road traffic sign recognition is an important task in intelligent transportation system. Convolutional neural networks (CNNs) have achieved a breakthrough in computer vision tasks and made great success in traffic sign classification. In this paper, it presents a road traffic sign recognition algorithm based on a convolutional neural network. In natural scenes, traffic signs are disturbed by factors such as illumination, occlusion, missing and deformation, and the accuracy of recognition decreases, this paper proposes a model called Improved VGG (IVGG) inspired by VGG model. The IVGG model includes 9 layers, compared with the original VGG model, it is added max-pooling operation and dropout operation after multiple convolutional layers, to catch the main features and save the training time. The paper proposes the method which adds dropout and Batch Normalization (BN) operations after each fully-connected layer, to further accelerate the model convergence, and then it can get better classification effect. It uses the German Traffic Sign Recognition Benchmark (GTSRB) dataset in the experiment. The IVGG model enhances the recognition rate of traffic signs and robustness by using the data augmentation and transfer learning, and the spent time is also reduced greatly.     

基于全局注意力机制和LSTM的连续手语识别算法

目的 为提高连续手语识别准确率,缓解听障人群与非听障人群的沟通障碍。方法 提出了基于全局注意力机制和LSTM的连续手语识别算法。通过帧间差分法对视频数据进行预处理,消除视频冗余帧,借助ResNet网络提取特征序列。通过注意力机制加权,获得全局手语状态特征,并利用LSTM进行时序分析,形成一种基于全局注意力机制和LSTM的连续手语识别算法,实现连续手语识别。结果 实验结果表明,该算法在中文连续手语数据集CSL上的平均识别率为90.08%,平均词错误率为41.2%,与5种算法相比,该方法在识别准确率与翻译性能上具有优势。结论 基于全局注意力机制和LSTM的连续手语识别算法实现了连续手语识别,并且具有较好的识别效果及翻译性能,对促进听障人群无障碍融入社会方面具有积极的意义。     

基于多分类器的通信信号调制方式识别

为在恶劣接收条件下识别通信信号的调制样式,并实现识别范围的快速调整,运用决策层信息融合技术提出了一种基于多分类器的调制样式识别方法。它采用了常规特征以及三种可以快速建立的子分类器。其中“超盒”分类器用于界定各调制样式不同特征的取值范围。最近邻分类器用于分类,马氏距离分类器用于提高识别结果的可靠程度,该方法可以自动地在短时间内根据设定的识别范围建立对应的识别器,对另类调制样式或噪声有高的拒识能力。     

现代地铁导视系统的交互数字化研究

目的分析地铁导视系统存在的问题,结合交互设计的方法,提出解决方案。方法通过分析武汉地铁用户在站内的系列活动,得到用户在出行过程中存在的用户需求和痛点,用数字化思维对传统地铁行业进行再设计。结论构建全新交互式数字化移动地铁导视系统,将移动互联网手机APP移动端作为载体,改变传统地铁出行方式,结合移动数字化概念,对现阶段中国地铁导视系统提出切实可行的优化建议。     

Multilayer perceptions for detecting cyclic data on control charts

Cyclic data occur relatively frequently in manufacturing processes. Traditional approaches to detecting cyclic behaviour are mostly statistics-based, such as spectral analysis and time series analysis. In this paper, a special-purpose cyclic pattern recognition system applying neural networks is proposed. The system consists of multiple multilayer perceptrons with each perceptron dealing with cycles of a certain period. Thus, it incapable of identifying cycles of various periods. Multiple perceptrons may work concurrently, but a final decision is made through a unified decision rule. This type of special-purpose system is recommended when certain behaviour is known to exhibit more frequently in a given manufacturing process. Under the circumstances, an automatic assignable-cause interpretation system, which contains a special-purpose pattern recognition system as a core component, may be tuned to be more sensitive to this particular type of behaviour. Simulation indicates that a Special-purpose cyclic pattern recognizer performs comparably to a general-purpose pattern recognizer in detecting less noise-contaminated cycles, but performs superiorly in detecting cycles of higher noise and cycles of higher amplitudes.     

手语视觉转译的创新设计研究

目的 将听障人使用的视觉交流语言“手语”转译为更加直观易懂、有趣易学、易于传播的图形语言,从而帮助人们认知手语的语形和语义,促进手语的保护与传播。方法 在转译概念的基础上,运用符号学的基本原理,对手语视觉转译的符号传达逻辑进行深入的分析和阐释;借助语言学的直译与意译理论,对手语的视觉转译设计进行模型建构;运用图解设计方法,对手语视觉信息进行可视化设计。结论 手语视觉转译在当下还有许多亟待解决的问题;手语视觉转译的传达逻辑是从“抽象意义”到“手语视觉”再到“图形符号”的过程;手语的视觉转译应当从应用性和艺术性两方面提升手语视觉传达的效率;从语形的转译角度出发,划分出适于识别交流的“直译图解”;从语义的转译角度出发,划分出了适于阅读理解的“意译图解”;目前手语的直译图解有待改善,而相关的意译图解被严重忽视;手语视觉转译的图解设计应当考虑信息编码与信息解码的全过程;其创新设计应当加强多样化、准确性、功能性、艺术性、人文性、趣味性探索。     

Recognition of control chart concurrent patterns using a neural network approach

Pattern recognition is an important issue in Statistical Process Control, as unnatural patterns exhibited by control charts can be associated with specific assignable causes adversely affecting the process. Neural network approaches to recognition of control chart patterns have been developed by several researchers in recent years, but to date these have been focused on recognition and analysis of single patterns such as sudden shifts, linear trends or cyclic patterns. This paper investigates the detection of concurrent patterns where more than one pattern exists simultaneously. The topology and training of a Back-Propagation Network (BPN) system is described. Extensive performance evaluation has been carried out using simulated data to develop a range of average run length-related performance indices, including new performance indices that are proposed to describe concurrent patterns recognition performance. Two evaluation scenarios were evaluated: in the first, unnatural patterns are already present; while in the second, patterns may appear progressively at any time. Numerical results are provided that indicate that the pattern recognizer can perform very well in the first scenario, while it performs effectively but not without deficiencies for some specific pattern combinations in the second evaluation approach. Limitations and potential improvements in the concurrent pattern recognition scheme are also discussed.     

Improved SPC chart pattern recognition using statistical features

Increasingly rapid changes and highly precise manufacturing environments require timely monitoring and intervention when deemed necessary. Traditional Statistical Process Control (SPC) charting, a popular monitoring and diagnosis tool, is being improved to be more sensitive to small changes and to include more intelligence to handle dynamic process information. Artificial neural network-based SPC chart pattern recognition schemes have been introduced by several researchers. These schemes need further improvement in terms of generalization and recognition performance. One possible approach is through improvement in data representation using features extracted from raw data. Most of the previous work in intelligent SPC used raw data as input vector representation. The literature reports limited work dealing with features, but it lacks extensive comparative studies to assess the relative performance between the two approaches. The objective of this study was to evaluate the relative performance of a feature-based SPC recognizer compared with the raw data-based recognizer. Extensive simulations were conducted using synthetic data sets. The study focused on recognition of six commonly researched SPC patterns plotted on the Shewhart X-bar chart. The ANN-based SPC pattern recognizer trained using the six selected statistical features resulted in significantly better performance and generalization compared with the raw data-based recognizer. Findings from this study can be used as guidelines in developing better SPC recognition systems.     

商业空间中的导视系统设计探析

对成都市的百货商场中的导视系统应用进行了实地调研,指出了目前导视系统应用存在的主要问题,并对导视系统的设置、导视系统各视觉要素设计的功能性原则、系统化原则和统一性原则进行了初步的探讨,力图为设计实践提供理论依据。     

基于用户行为智能观测的导视系统设计

目的运用传感器与机器学习算法对不同文化背景的老年用户在地铁站内的寻路行为特征进行研究,进而为提高地铁站内导视系统的通用化设计水平提供数量化与科学化决策支持。方法应用Zig Bee人体智能传感器模块,以异地访京和本地老年人为被试进行了高频度追踪数据采集试验,分析其在地铁站内的寻路行为与差异。结果构建了基于Zig Bee智能模块的行为观测研究方法。结论为老年人提供快捷且直观的导视信息,能够节约寻路过程中所消耗的脑力与体力,提高寻路体验兼顾社会公正。