A two stream convolutional neural network with bi-directional GRU model to classify dynamic hand gesture

Dynamic hand gesture recognition is still an interesting topic for the computer vision community. A set of feature vectors can represent any hand gesture. A Recurrent Neural Network (RNN) can recognize these feature vectors as a hand gesture that analyzes the temporal and contextual information of the gesture sequence. Thus, we proposed a hybrid deep learning framework to recognize dynamic hand gestures. In the Hybrid model GoogleNet is pipelined with a Bidirectional GRU unit to recognize the dynamic hand gesture. Dynamic hand gestures consist of many frames, and features of each frame need to be extracted to get the temporal and dynamic information of the performed gesture. As RNN takes input as a sequence of feature vectors, we extract features from videos using pretrained GoogleNet. As Gated Recurrent Unit is one of the variants of RNN to classify the sequential data, we created a feature vector that corresponds to each video and passed it to the bidirectional GRU (BGRU) network to classify the gestures. We evaluate our model on four publicly available hand gesture datasets. The proposed method performs well and is comparable with the existing methods. For instance, we achieved 98.6% accuracy on Northwestern University Hand Gesture(NWUHG), 99.6% on SKIG, 99.4% on Cambridge Hand Gesture (CHG) datasets respectively. We performed our experiments on DHG14/28 dataset and achieved an accuracy of 97.8% with 14-gesture classes and 92.1% on 28-gesture classes. DHG14/28 dataset contains skeleton and depth data, and our proposed model used depth data and achieved comparable accuracy.     

Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals

Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals; these signals can be recorded, processed and classified into different hand movements, which can be used to control other IoT devices. Classification of hand movements will be one step closer to applying these algorithms in real-life situations using EEG headsets. This paper uses different feature extraction techniques and sophisticated machine learning algorithms to classify hand movements from EEG brain signals to control prosthetic hands for amputated persons. To achieve good classification accuracy, denoising and feature extraction of EEG signals is a significant step. We saw a considerable increase in all the machine learning models when the moving average filter was applied to the raw EEG data. Feature extraction techniques like a fast fourier transform (FFT) and continuous wave transform (CWT) were used in this study; three types of features were extracted, i.e., FFT Features, CWT Coefficients and CWT scalogram images. We trained and compared different machine learning (ML) models like logistic regression, random forest, k-nearest neighbors (KNN), light gradient boosting machine (GBM) and XG boost on FFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFT features gave the maximum accuracy of 88%.     

视觉文化语境下手绘插画与商业包装的交融

目的研究视觉文化时代下的手绘插画。方法从手绘插画和商业包装设计艺术形式的角度出发探讨手绘插画与商业包装的交融性。结论手绘插画具有独特的艺术魅力和浓厚的文化底蕴,可以有效传播品牌文化、塑造品牌形象。与商业包装相融的手绘插画能丰富商业包装的视觉语言、承载人们情感,使包装更具亲和力与艺术魅力。     

欠驱动手动态抓取控制的研究

基于手指动力学模型建立了速度观测器.该观测器用于欠驱动手的自适应轨迹跟踪,弥补了欠驱动手没有速度传感器的缺点,补偿了手指模型中欠驱动元件(如扭簧)造成的不确定因素.与PID和计算力矩法相比,其跟踪误差更小,动态控制效果更加理想.通过动力学模型建立了基于力的阻抗控制策略,在阻抗控制中使用速度观测器,实现了基关节的动态抓取力跟踪.动态轨迹跟踪与力跟踪相结合,使欠驱动手在抓握时具有良好的动态抓取性能.     

基于属性计算网络的动态手势识别的研究

基于视觉的动态手势识别既是当今人机交互系统研究的热点也是难点。该文提出了基于属性计算网络的识别方法对动态手势进行识别,与现有的识别方法相比,具有明显的特点和优点。首先利用肤色分离手部对象,然后跟踪手部对象,并对手部对象轮廓和运动轨迹抽取特征并建立对应的特征向量,最后将手部对象轮廓特征和运动轨迹特征进行加权组合,得出识别结果。本方法的特点和优点是识别分析速度较快,能够很好的满足人机交互系统实时性的需要,并且能够适应动态手势表达在速度和幅度上的差异性。     

融合深度与肤色特征的自适应手部跟踪算法

手部跟踪技术是实现自然人机交互的关键。针对现有跟踪方法易受光照、环境等影响及鲁棒性差的不足,提出一种融合深度与肤色特征的自适应手部跟踪算法。考虑手部运动过程的形变,该算法首先利用深度平滑连续性选取深度阈值以实现跟踪区域的自适应尺度变化,获得手部候选区域。在此基础上建立YCbCr空间肤色归一化直方图,在粒子滤波框架下将跟踪问题转换为贝叶斯估计问题,基于最大后验准则确定手部位置,并通过监测粒子重要性权值的方差解决跟踪失效问题,实现复杂观测环境下的鲁棒跟踪。实验结果表明,该跟踪算法可适应不同复杂环境,鲁棒性良好。     

Effects of auditory,haptic and visual feedback on performing gestures by gaze or by hand

Modern interaction techniques like non-intrusive gestures provide means for interacting with distant displays and smart objects without touching them. We were interested in the effects of feedback modality (auditory, haptic or visual) and its combined effect with input modality on user performance and experience in such interactions. Therefore, we conducted two exploratory experiments where numbers were entered, either by gaze or hand, using gestures composed of four stroke elements (up, down, left and right). In Experiment 1, a simple feedback was given on each stroke during the motor action of gesturing: an audible click, a haptic tap or a visual flash. In Experiment 2, a semantic feedback was given on the final gesture: the executed number was spoken, coded by haptic taps or shown as text. With simultaneous simple feedback in Experiment 1, performance with hand input was slower but more accurate than with gaze input. With semantic feedback in Experiment 2, however, hand input was only slower. Effects of feedback modality were of minor importance; nevertheless, semantic haptic feedback in Experiment 2 showed to be useless at least without extensive training. Error patterns differed between both input modes, but again not dependent on feedback modality. Taken together, the results show that in designing gestural systems, choosing a feedback modality can be given a low priority; it can be chosen according to the task, context and user preferences.     

Development of broadband forward‐wave directional couplers loaded by periodic patterned ground structure

In this paper two wideband Forward‐Wave Directional Couplers (FWDCs) with 0 dB and 3 dB coupling level are proposed. Using periodic patterned ground structure in a microstrip coupled lines by a new unit cell; even‐ and odd‐mode characteristic impedances of the couplers are equal over a wide frequency range. Moreover, it provides a constant phase difference between even and odd‐modes. The proposed cell is modeled using the equivalent circuit model and a design procedure is introduced for designing FWDCs for an arbitrary value of coupling level. The introduced couplers are numerically investigated and a prototype of both couplers is made. It is shown that for 0 dB coupling level, the measured coupling is 0.85 dB with 1 dB flatness over fractional bandwidth of 96% bandwidth. In case of 3 dB coupling, the measured coupling level is 3.5 dB at 7.42 GHz with 1 dB flatness over fractional bandwidth of 67.1%.     

基于LBP和多层次结构的异质手背静脉身份识别

针对多源异质的手背静脉异质图像的识别研究,提出了基于LBP和多层次结构的识别算法;首先对图像做适当的预处理,然后将LBP特征提取算法编码的手背静脉纹理特征图像作为多层次结构的输入,通过多层次结构的逐层由具体到抽象的特征提取,得到的特征具有更大的鲁棒性;最后该算法在多源异质的手背静脉图像库得到的识别率比传统的算法识别率高,达到96.57%;进一步表明该算法能够较好地解决由于多源异质问题对手背静脉识别所造成的识别率低的影响。     

一种德州扑克的牌力评估方法

德州扑克是机器博弈领域中一种很好的研究对象,与国际象棋、西洋棋等不同,其涉及了诸如不完整信息抽象与处理、多智能体竞争、风险评估与管理、对手建模等多方面的问题研究。其中,不完备信息抽象与处理是其他问题研究的基础,抽象与处理信息就是要对当前牌局进行评估以供后续工作使用。针对牌局信息不完整的情况,讨论了一些评估牌力方法,分析了这些方法的利弊,并提出了一种新的牌力评估方法,以提升计算速度,减少资源占用。实验结果表明,新的方法摒弃了查找表,达到了节省空间的目的,同时也具备较快的计算速度。