基于方向直方图矢量的手势识别

本文介绍了一种用于手势识别的新方法——图像的局部方向直方图矢量（OHV），利用图像的OHV作为手势的特征向量来进行手势的分类和识别。该方法时于光线和手势平移变化不敏感，具有较强的鲁棒性。在进行特征矢量匹配时，本文使用欧氏距离作为矢量闻匹配程度的度量算法。实验结果表明谊方法具有很高的识别率，并且简单，快速，可以用于实时的手势识别系统中。     

大尺寸集成多重触摸功能LCD

52in红外液晶显示模块图像传感器集成了多重触摸功能,可以探测普通基板周围的触摸。方法是通过放置一些红外图像传感器来探测触摸。触摸算法中用到三角剖分、图像处理技术以及模式识别。同时通过红外图像传感器、红外光源和LCD基板计算出触摸点位置。另外,触摸数量和触摸面积用于实现多重触摸功能。多重触摸LCD也可以探测人的手势。在这里,地图寻找程序使用了触摸和手势识别。结果是多重触摸LCD显示出它的有效性,可以用来替代传统的人机界面设备（如鼠标、键盘）。     

基于UKF的毫米波雷达手势识别算法研究

本文提出了一种基于无迹卡尔曼滤波（UKF）的毫米波雷达干扰抑制的手势识别方法。首先根据原始雷达信号设置的采样点与线性调频信号数量，估计了目标的距离与多普勒参数。之后针对实际场景中类目标干扰较多的情况，设计了一套完整的基于UKF的场景类目标抑制方法，接着利用卷积神经网络（CNN）对不同手势距离?多普勒特征谱图进行提取和识别。实验结果表明，该抑制方法有效地解决了类目标干扰给手势识别带来的困扰，手势识别的平均准确率为98.74%，经过抑制干扰算法后准确率相较于干扰抑制之前提升了7.29%。     

用于人机交互的静态手势识别系统

提出并实现一个用于人机交互的静态手势识别系统。基于皮肤颜色模型进行手势分割，并用傅里叶描述子描述轮廓。采用针对小样本特别有效且范化误差有界的支持向量机方法：最小二乘支持向量机（LS－SVM）作为分类器。提出了LS－SVM的增量训练方式，避免了费时的矩阵求逆操作。为实现多类手势识别，利用DAG（Directed Acyclic Graph)将多个两类LS－SVM结合起来。对26个字母手势进行识别，与多层感知器、径向基函数网络等方法比较，LS－SVM的识别率最高，为93．62％。     

升特扩展了4D-Touch平台对多点触摸的支持

升特公司（Semtech）发布了支持多点触摸的电阻式触摸屏控制器Sx8674／75／76／77／78．将为升特公司的4D—Touch平台带来对触摸缩放手势的识别能力。升特公司率先提供了针对标准电阻屏的接近探测与多点触摸支持,使制造商有机会将自己现有平台升级为支持流行的缩放手势,用于观看图片、互联网浏览．以及游戏应用等。     

空间上下文与时序特征融合的交警指挥手势识别技术

针对无人驾驶汽车快速准确识别交警指挥手势的需求,本文在分析交警指挥手势的关节铰接特征基础上,建立基于关节点和骨架的交警指挥手势模型;其次,引入卷积姿势机（Convolutional Pose Machine,CPM）提取交警指挥手势的关键节点,进而提取交警指挥手势中骨架的相对长度及其与重力加速度的夹角作为空间上下文特征,并引入长短时记忆网络（Long Short Term Memory,LSTM）提取交警指挥手势的时序特征;最后,设计了融合空间上下文和时序特征的交警指挥手势识别机（Chinese Traffic Police Gesture Recognizer,CTPGR）,创建了包含8种交警指挥手势、时长约2小时的交警指挥手势视频库对CTPGR进行训练验证,并通过实验将CTPGR与已有交警手势识别算法进行了对比分析.实验证明CTPGR可以快速准确地识别交警指挥手势,系统对复杂背景和动态交警指挥手势具有较强的适应能力.     

基于图像处理的智能家居系统设计

人脸和手势识别是人工智能技术的一种，通过采集人脸和手势的图像信息，对图像信息进行扫描识别。近年来，随着信息技术快速发展，智能家居应用成为研究的热点。通过采用OpenCV计算机视觉库和LBP-HOG算法搭建具有人脸识别、手势识别、邮箱远程异常提醒等功能的智能家居系统。该系统通过摄像头对图像进行人脸识别判断，通过简单邮件传输协议（Simple Mail Transfer Protocol,SMTP）实现远程异常提醒功能。在此基础上，增加手势识别功能，实现多功能的智能家居应用。实验结果表明，所设计系统的人脸识别准确率达98%以上，手势识别准确率达90%以上，具有广阔的应用前景。     

一种简单实用的手势识别算法

本文提出了一种简单实用的手势识别算法，即对预处理后的手势图像先采用投影法获取手势所在的矩形区域，然后在这个矩形区域内采用矩描绘子获取手势的重心，再采用多级菱形样板提取指尖的数量与具体位置，最后采用分类识别规则，得到最终的识别结果。采用本方法可以实现对手势1～10的正确识别。经过试验证明，本文提出的方法实现简单，识别时间短，而且识别正确率高。     

基于多边形凹凸点检测的远距离手势识别

手势识别是一种自然、直观的人机交互手段。文章提出了一种远距离手势识别算法。首先,对输入的手势序列图像进行双边滤波预处理;其次,运用肤色模型的方法对手势进行分割,获取手势区域;在识别阶段,文章提出一种基于多边形凹凸点检测的手势识别方法,识别分割出来的手势。实验结果表明该方法能够在较远距离下有效地识别多种手势,并且具有较好的实时性。     

基于3D体感技术的动态手势识别

提出了一种基于3D体感机Kinect的图像处理手势识别算法,通过深度图像和骨骼图像的方法实现动态手势识别。首先在Kinect提供的骨骼图像中20个骨点中,选取2个离手部最近的骨骼点,通过追踪这两个骨骼点的位置来实现对手部的追踪,再通过判断手部的深度（即其相对于摄像头的距离）的变化来实现动态手势识别。     

Research on key thechnologies of pornographic image/video recognition in compressed domain

Pornographic image/video recognition plays a vital role in network information surveillance and management. In this paper, its key techniques, such as skin detection, key frame extraction, and classifier design, etc, are studied in compressed domain. A skin detection method based on data-mining in compressed domain is proposed firstly and achieves the higher detection accuracy as well as higher speed. Then, a cascade scheme of pornographic image recognition based on selective decision tree ensemble is proposed in order to improve both the speed and accuracy of recognition. A pornographic video oriented key frame extraction solution in compressed domain and an approach of pornographic video recognition are discussed respectively in the end.     

Spotting and Recognition of Consonant-Vowel Units from Continuous Speech Using Accurate Detection of Vowel Onset Points

In this paper, we propose an efficient approach to spotting and recognition of consonant-vowel (CV) units from continuous speech using accurate detection of vowel onset points (VOPs). Existing methods for VOP detection suffer from lack of high accuracy, spurious VOPs, and missed VOPs. The proposed VOP detection is designed to overcome most of the shortcomings of the existing methods and provide accurate detection of VOPs for improving the performance of spotting and recognition of CV units. The proposed method for VOP detection is carried out in two levels. At the first level, VOPs are detected by combining the complementary evidence from excitation source, spectral peaks, and modulation spectrum. At the second level, hypothesized VOPs are verified (genuine or spurious), and their positions are corrected using the uniform epoch intervals present in the vowel regions. The spotted CV units are recognized using a two-stage CV recognizer. Two-stage CV recognition system consists of hidden Markov models (HMMs) at the first stage for recognizing the vowel category of a CV unit and support vector machines (SVMs) for recognizing the consonant category of a CV unit at the second stage. Performance of spotting and recognition of CV units from continuous speech is evaluated using Telugu broadcast news speech corpus.     

Intelligent RFID Tag Detection Using Support Vector Machine

RFID Tag detection/recognition is one of the most critical issues for successful deployment of RFID systems in diverse applications. The main factors influencing tag detection by RFID reader antenna include tag position, relative position of reader, read field length, etc. In this paper, we analyze the characteristics of tag detection for a carton box object on a wooden pallet by an experimental approach based on tag signal strength, and we propose a method for predicting detection related directly to the strength of tag signal using an intelligent machine learning technique called support vector machine (SVM). The use of the proposed method is able to save time and cost by quick prediction of tag detection. Extensive experiments showed that the proposed approach can predict tag recognition for a carton box object with an accuracy at 95% for various reader heights and read field lengths. The proposed approach is effective for determining the best tag detection influencing factor conditioned on the target object with the help of detectability prediction.     

Depth sensor assisted real-time gesture recognition for interactive presentation

In this paper, we present a gesture recognition approach to enable real-time manipulating projection content through detecting and recognizing speakers gestures from the depth maps captured by a depth sensor. To overcome the limited measurement accuracy of depth sensor, a robust background subtraction method is proposed for effective human body segmentation and a distance map is adopted to detect human hands. Potential Active Region (PAR) is utilized to ensure the generation of valid hand trajectory to avoid extra computational cost on the recognition of meaningless gestures and three different detection modes are designed for complexity reduction. The detected hand trajectory is temporally segmented into a series of movements, which are represented as Motion History Images. A set-based soft discriminative model is proposed to recognize gestures from these movements. The proposed approach is evaluated on our dataset and performs efficiently and robustly with 90% accuracy.     

Semi-supervised annotation of faces in image collection

The objective of this work is to correctly detect and recognize faces in an image collection using a database of known faces. This has applications in photo-tagging, video indexing, surveillance and recognition in wearable computers. We propose a two-stage approach for both detection and recognition tasks. In the first stage, we generate a seed set from the given image collection using off-the-shelf face detection and recognition algorithms. In the second stage, the obtained seed set is used to improve the performance of these algorithms by adapting them to the domain at hand. We propose an exemplar-based semi-supervised framework for improving the detections. For recognition of images, we use sparse representation classifier and generate seed images based on a confidence measure. The labels of the seed set are then propagated to other faces using label propagation framework by imposing appropriate constraints. Unlike traditional approaches, our approach exploits the similarities among the faces in collection to obtain improved performance. We conduct extensive experiments on two real-world photo-album and video collections. Our approach consistently provides an improvement of \({\sim } 4\)% for detection and \(5{-}9\)% for recognition on all these datasets.     

Detection of landmines and underground utilities from acoustic and GPR images with a cepstral approach

This paper introduces a cepstral approach for the automatic detection of landmines and underground utilities from acoustic and ground penetrating radar (GPR) images. This approach is based on treating the problem as a pattern recognition problem. Cepstral features are extracted from a group of images, which are transformed first to 1-D signals by lexicographic ordering. Mel-frequency cepstral coefficients (MFCCs) and polynomial shape coefficients are extracted from these 1-D signals to form a database of features, which can be used to train a neural network with these features. The target detection can be performed by extracting features from any new image with the same method used in the training phase. These features are tested with the neural network to decide whether a target exists or not. The different domains are tested and compared for efficient feature extraction from the lexicographically ordered 1-D signals. Experimental results show the success of the proposed cepstral approach for landmine detection from both acoustic and GPR images at low as well as high signal to noise ratios (SNRs). Results also show that the discrete cosine transform (DCT) is the most appropriate domain for feature extraction.     

低信噪比S模式应答信号相关检测方法

针对低信噪比条件下S模式应答信号检测概率低、虚警概率高的问题,提出一种基于数据包检测、单脉冲匹配滤波和报头多脉冲检测等三次相关检测的增强识别方法。该方法首先利用数据包相关检测得到数据包位置,从而确定报头脉冲的检测门限;然后利用单脉冲匹配滤波对信号进行降噪处理,确定脉冲位置;最后估计脉冲幅度和噪声功率,利用多脉冲相关检测识别信号报头。通过这三次相关处理,该识别算法可提高S模式应答信号的准确识别率。理论推导了检测概率的数学表达式,通过计算机仿真,在期望虚警概率下,本文方法基本达到了理论检测概率,同时对比了本文方法与传统的直接匹配滤波法、脉冲前沿检测法、基带归一化互相关法的识别性能,验证了本文算法性能的优越性。      

Tiny and Blurred Face Alignment for Long Distance Face Recognition

Applying face alignment after face detection exerts a heavy influence on face recognition. Many researchers have recently investigated face alignment using databases collected from images taken at close distances and with low magnification. However, in the cases of home‐service robots, captured images generally are of low resolution and low quality. Therefore, previous face alignment research, such as eye detection, is not appropriate for robot environments. The main purpose of this paper is to provide a new and effective approach in the alignment of small and blurred faces. We propose a face alignment method using the confidence value of Real‐AdaBoost with a modified census transform feature. We also evaluate the face recognition system to compare the proposed face alignment module with those of other systems. Experimental results show that the proposed method has a high recognition rate, higher than face alignment methods using a manually‐marked eye position.     

A hand gesture recognition technique for human–computer interaction

We propose an approach to recognize trajectory-based dynamic hand gestures in real time for human–computer interaction (HCI). We also introduce a fast learning mechanism that does not require extensive training data to teach gestures to the system. We use a six-degrees-of-freedom position tracker to collect trajectory data and represent gestures as an ordered sequence of directional movements in 2D. In the learning phase, sample gesture data is filtered and processed to create gesture recognizers, which are basically finite-state machine sequence recognizers. We achieve online gesture recognition by these recognizers without needing to specify gesture start and end positions. The results of the conducted user study show that the proposed method is very promising in terms of gesture detection and recognition performance (73% accuracy) in a stream of motion. Additionally, the assessment of the user attitude survey denotes that the gestural interface is very useful and satisfactory. One of the novel parts of the proposed approach is that it gives users the freedom to create gesture commands according to their preferences for selected tasks. Thus, the presented gesture recognition approach makes the HCI process more intuitive and user specific.     

Low Latency Fog-Centric Deduplication Approach to Reduce IoT Healthcare Data Redundancy

In today’s highly computerized society, detection and recognition of text present in natural scene images is complex and difficult to be properly recognized by human vision. Most of the existing algorithms and models mainly focus on detection and recognition of text from still images. Many of the recent machine translation systems are built using the Encoder-Decoder framework which works on the format of encoding the sequence of input and then based on the encoded input, the output is decoded. Both the encoder and the decoder use an attention mechanism as an interface, making the model complex. Aiming at this situation, an alternative method for recognition of texts from videos is proposed. The proposed approach is based on a single Two-Dimensional Convolutional Neural Network (2D CNN). An algorithm for extracting features from an image called the crosswise feature extraction is also proposed. The proposed model is tested and shows that crosswise feature extraction gives better recognition accuracy by requiring a lesser period of time for training than the conventional feature extraction technique used by CNN.