基于LabVIEW的乒乓球机器人设计

乒乓球机器人系统对实时性的要求非常高,其设计涉及到了运动物体的识别、三维定位、快速轨迹预测和机械臂精确控制.在LabVIEW中调用图像处理模块获取并处理图像,满足实时性要求;使用滑轨和机械手结合来去球的方案,扩大了击球范围;调用Modbus函数库简化了伺服电机驱动器的控制,制作的样机目前已经可以实现乒乓球三维定位、击球点预测以及几个回合的人机对打.     

Recovering ball motion from a single motion-blurred image

Motion blur often affects the ball image in photographs and video frames in many sports such as tennis, table tennis, squash and golf. In this work, we operate on a single calibrated image depicting a moving ball over a known background, and show that motion-blurred ball images, usually unwelcome in computer vision, bear more information than a sharp image. We provide techniques for extracting such information ranging from low-level image processing to 3D reconstruction, and present a number of experiments and possible applications, such as ball localization with speed and direction measurement from a single image, and ball trajectory reconstruction from a single long-exposure photograph.     

多颜色模型下的乒乓球快速检测与实时跟踪研究

乒乓球机器人视觉系统中的乒乓球检测主要有基于运动分析和基于单一颜色模型分割两种方法.基于运动分析的方法对运动背景干扰的鲁棒性较差,而基于单一颜色模型的分割方法会受到相近颜色及光照变化的干扰.为此,提出了多颜色模型下的乒乓球分割算法,结合RGB与HSV两种颜色模型的颜色表达特性提取出乒乓球区域,并利用质心法对乒乓球进行中心定位.在此基础上,提出基于前帧位置的感兴趣区域算法,对乒乓球进行实时跟踪.实验表明,该方法能够在复杂环境下对乒乓球进行快速精确定位,算法处理时间小于10 ms,定位误差小于20 mm,满足乒乓球机器人的击球需要.     

基于立体视觉的目标检测与轨迹预测研究综述

目前基于立体视觉信息的运动目标识别定位、跟踪及轨迹预测是机器视觉领域的研究热点.通过归纳整理相关文献,从双目立体视觉技术、运动目标检测技术、运动目标轨迹预测技术三个方面对基于立体视觉的运动目标检测及轨迹预测进行了概述,分别阐述了相机标定的常见方法、图像特征提取及立体匹配不同算法的适用场景、各运动目标检测方法的优缺点、常...     

Review of Plant Identification Based on Image Processing

Plant recognition is closely related to people’s life. The operation of the traditional plant identification method is complicated, and is unfavorable for popularization. The rapid development of computer image processing and pattern recognition technology makes it possible for computer’s automatic recognition of plant species based on image processing. There are more and more researchers drawing their attention on the computer’s automatic identification technology based on plant images in recent years. Based on this, we have carried on a wide range of research and analysis on the plant identification method based on image processing in recent years. First of all, the research significance and history of plant recognition technologies are introduced in this paper; secondly, the main technologies and steps of plant recognition are reviewed; thirdly, more than 30 leaf features (including 16 shape features, 11 texture features, four color features), and then SVM was used to evaluate these features and their fusion features, and 8 commonly used classifiers are introduced in detail. Finally, the paper is ended with a conclusion of the insufficient of plant identification technologies and a prediction of future development.     

基于体感识别技术的乒乓球握姿训练系统设计

为了提高乒乓球握姿训练的自动化水平,提出一种基于体感识别技术的乒乓球握姿训练系统,采用图像传感器进行乒乓球握姿的图像信息采集,对采集的乒乓球握姿图像信息进行体感特征监测,提取乒乓球握姿图像的体感动态特征量,采用支持向量机模型进行乒乓球握姿的体感特征分类识别。根据对乒乓球握姿的体感特征识别结果进行乒乓球握姿的自动调节,实现乒乓球握姿训练优化。仿真结果表明,采用该方法进行乒乓球握姿训练的提高特征检测能力较好,识别准确性较高,提高了乒乓球握姿的自动训练水平。     

High-order features of a single linear corneal laceration image are valuable biomarkers in an intelligent multimodal analytic strategy for corneal laceration reconstruction

BackgroundIn corneal lacerations, the absence of high-order image features as biomarkers to guide surgical strategy is a limiting factor. The absence of multimodal data restricts the development of automated reconstruction designs for corneal laceration. The present study is aimed at training and optimizing the model based on high-order features from corneal laceration images and real suture samples and completing the intelligent promotion of whole corneal laceration suture auxiliary decision-making with the two-step method of automatic wound identification and stitch position prediction.MethodsBased on the images of isolated corneal wound samples, a fully supervised U-Net learning method and consistent regular semisupervised learning method based on the mean-teacher model were used to identify the wounds. The DDice coefficient was used to evaluate the segmentation and recognition effect. Traditional image processing technology was used to predict the needle entry and exit points of wound sutures based on medical suture principles. The prediction effect was evaluated by viewpoint similarity.ResultsAfter training the wound recognition model based on 2400 corneal images and corresponding incision labels, the DDice coefficients of supervised U-Net with or without postprocessing results were 0.902 and 0.817, respectively. The Dice coefficients of the semisupervisedmean-teacher model with or without postprocessing were 0.921 and 0.843, respectively. The key point similarity of wound stitch position prediction was 0.872 ± 0.021.ConclusionThis new automated method for corneal laceration identification and stitch position generation based on novel biomarkers and multimodal data is expected to assist doctors treating corneal lacerations to quickly formulate a primary suturing strategy.     

基于深度学习的行人轨迹预测方法综述

为了规划合理的路径以规避行人,针对行人轨迹预测的研究具有广泛的应用价值.基于手工特征的传统方法难以预测复杂场景下的行人轨迹.深度学习以人工神经网络为架构,具有强大的学习能力,在各个领域取得了显著的效果.基于深度学习的行人轨迹预测方法已逐渐发展为一种趋势.为了宏观把握基于深度学习的行人轨迹预测的研究状况,首先,对不同方法进行组织与分类,比较不同方法的优缺点,讨论不同方法在行人轨迹预测领域的应用与发展;其次,根据行人轨迹预测模型的设计差异,对比不同算法对模型性能产生的影响;最后,针对行人轨迹预测中存在的问题,对基于深度学习的行人轨迹预测方法的未来发展进行了展望.     

Breast Mammogram Analysis and Classification Using Deep Convolution Neural Network

One of the fast-growing disease affecting women’s health seriously is breast cancer. It is highly essential to identify and detect breast cancer in the earlier stage. This paper used a novel advanced methodology than machine learning algorithms such as Deep learning algorithms to classify breast cancer accurately. Deep learning algorithms are fully automatic in learning, extracting, and classifying the features and are highly suitable for any image, from natural to medical images. Existing methods focused on using various conventional and machine learning methods for processing natural and medical images. It is inadequate for the image where the coarse structure matters most. Most of the input images are downscaled, where it is impossible to fetch all the hidden details to reach accuracy in classification. Whereas deep learning algorithms are high efficiency, fully automatic, have more learning capability using more hidden layers, fetch as much as possible hidden information from the input images, and provide an accurate prediction. Hence this paper uses AlexNet from a deep convolution neural network for classifying breast cancer in mammogram images. The performance of the proposed convolution network structure is evaluated by comparing it with the existing algorithms.     

中医舌象分割技术研究进展:方法、性能与展望

中医舌诊的客观化、定量化研究是中医现代化发展中的重要课题.数字化采集到的舌图像包括舌体及部分面部区域,为了便于后续舌象自动分析,需要首先将舌体部分从图像中分割出来,分割效果将直接影响后续舌象特征分析的准确性.基于传统方法的舌象分割技术虽然取得了很大进展,但其性能仅能达到半自动分割,对较难分割的图像往往需要借助人机交互来...     

低空运动目标的多传感器自动识别和实时跟踪

讨论了一种用于低空运动目标检测和跟踪的电视跟踪系统。为了提高系统自动跟踪和抗干扰能力,基于声—光—电多种传感器和测量装置如声波传感器、图像传感器和激光测距仪等,提出一种多传感器综合的自动目标识别和实时跟踪算法。该方法将被动声定位技术用于目标初定位,结合目标图像动静态特征和目标声源特征用于目标的特征提取和自动识别,根据视频跟踪和轨迹预测算法,得出期望的目标误差信号控制伺服机构进行精确跟踪。实验结果表明该算法简捷有效、精度和可靠性达到要求,验证了多传感器应用于全自动智能跟踪系统的可行性。     

掌纹图像的获取及其预处理

提出了一种无限制的获取掌纹图像的方法,并使用该方法构建了掌纹库.该掌纹库可以成为掌纹识别算法训练集和测试集的来源,也可以成为进一步推进掌纹识别研究与发展的基础.在该掌纹库的基础上,对掌纹库中的掌纹图像进行了预处理,将原始图像二值化后利用定位点自动检测技术检测出掌纹图像中两个关键的定位点,并以此为基础对掌纹图像进行旋转校正,最后切取一定区域的掌纹子图,为进一步提取掌纹特征打下了较好的基础.     

4D时空纵向分析在生物医学领域中的应用现状与趋势

随着成像技术的成熟，临床医生及实验人员能获得同时带有时间和空间信息的4D（3D+时间）数据，用于纵向研究疾病变化情况。由于缺乏合适的处理算法，导致明显的信息丢失。为解决这一问题，一些研究发挥人工智能在海量数据处理上的优势进行纵向医学图像分析，研究目标随时间的动态变化。本文对4D时空纵向分析在生物学运动目标追踪、医学影像分割、肿瘤生长预测、血管动力学和神经科学等应用进行综述，重点探讨了人工智能技术与传统分析方法在各应用场景的优劣，并从联合多模态异构数据进行关联分析及联邦学习辅助算法部署两个角度进行前瞻性的探索和可行性分析，突破2D影像处理瓶颈，推动4D设备广泛应用，并为未来时空纵向分析在生物医学领域中的方法学研究及应用场景探索提供思路。     

基于超宽带技术的运动目标跟踪高精度定位系统设计

为克服运动目标不断变化导致跟踪定位精度较低的问题,设计基于超宽带技术的运动目标跟踪高精度定位系统;图像采集模块由FPGA单元、VGA显示单元、帧缓存单元以及图像采集单元构成,以此实现运动目标跟踪与定位中的图像采集,在超宽带技术模块中,设计超宽带运动目标定位所需的天线、定位基站、移动节点,完成超宽带动态组网,实现硬件系统的设计;基于硬件系统采集到的图像,实施图像灰度化处理、形态学滤波处理,以增强图像中有用的信息,设计TLD运动目标跟踪算法,随着运动目标开始运动,TLD模型会不断学习跟踪的运动目标,获取目标在距离、景深、角度等层面的改变,并不断学习、识别,达到良好的跟踪效果,基于超宽带技术设计运动目标动态定位算法,依据跟踪结果实现运动目标的高精度定位,完成软件系统的设计;实验测试结果表明,该系统在中、远距离目标跟踪与定位实验中跟踪错误率低于0.60%、2.4%,沿着S型运动时,路线弯折处的定位误差较低,与实验运动目标的飞行路线相贴合,具有良好的定位能力。     

A review on automatic image annotation techniques

Nowadays, more and more images are available. However, to find a required image for an ordinary user is a challenging task. Large amount of researches on image retrieval have been carried out in the past two decades. Traditionally, research in this area focuses on content based image retrieval. However, recent research shows that there is a semantic gap between content based image retrieval and image semantics understandable by humans. As a result, research in this area has shifted to bridge the semantic gap between low level image features and high level semantics. The typical method of bridging the semantic gap is through the automatic image annotation (AIA) which extracts semantic features using machine learning techniques. In this paper, we focus on this latest development in image retrieval and provide a comprehensive survey on automatic image annotation. We analyse key aspects of the various AIA methods, including both feature extraction and semantic learning methods. Major methods are discussed and illustrated in details. We report our findings and provide future research directions in the AIA area in the conclusions     

无人机航拍图像中电力线检测方法研究进展

随着各大电力公司对无人机（unmanned aerial vehicle,UAV）巡检的大力推广,“机巡为主,人巡为辅”已成为我国电力巡检的主要运维模式。电力线检测作为电力巡检的关键技术,在无人机自主导航、低空避障飞行以及输电线路安全稳定运行等方面发挥着重要作用。众多研究者将输电线路的无人机航拍图像用于线路设备识别与故障诊断,利用机器视觉的方法在电力线检测技术研究中占据主导地位,也是未来的主要发展方向。本文综述了近10年来无人机航拍图像中电力线检测方法的研究进展。首先简述了电力线特征,阐明了电力线检测的传统处理方法的一般流程及所面临的挑战;然后重点阐述了使用传统图像处理方法及深度学习方法的电力线检测原理,前者包括基于Hough变换的方法、基于Radon变换的方法、基于LSD （line segment detector）的方法、基于扫描标记的方法及其他检测方法,后者根据深度卷积神经网络（deep convolutional neural network,DCNN）的结构不同分为基于DCNN的分类方法及基于DCNN的语义分割方法,评述各类方法的优缺点并进行分析与比较,与传统图像处理方法相比,深度学习方法能更有效地实现航拍图像中的电力线检测,并指出基于DCNN的语义分割方法在电力线目标智能识别与分析中发挥着重要作用;随后介绍了电力线检测的常用数据集及性能评价指标;最后针对电力线检测方法目前存在的问题,对下一步的研究方向进行展望。     

图像超分辨重建算法综述

图像超分辨重建是一种提升图像分辨率的图像处理技术,而超分辨问题是一个难解的欠定问题,近些年来研究人员主要采用基于学习的方法,从大量数据中学习图像先验信息,以实现对解空间的约束。本文介绍了近20年来主流的图像超分辨重建算法,主要分为基于传统特征的方法和基于深度学习的方法。对于传统的超分辨重建算法,阐述了基于邻域嵌入的方法、基于稀疏表示的方法以及基于局部线性回归的方法。对于基于深度学习的超分辨重建算法,详细总结了网络模型结构设计、上采样方式、损失函数形式以及复杂条件下的算法设计4个方面。此外,本文简要分析了超分辨重建技术在视频超分辨、遥感图像超分辨以及在视觉高层任务方面的应用。最后,本文展望了图像超分辨重建技术的未来发展方向。     

乒乓球的三维运动轨迹重建

主要基于图像序列对乒乓球的运动轨迹进行三维重建,并对乒乓球运动形态进行分析.首先对采集的图像进行立体校正,利用颜色识别和改进的霍夫圆检测算法提取出序列图像中乒乓球的圆心坐标;然后根据前后帧图像的特征点坐标差值在时间序列上匹配特征点;最后,利用三角测量法对匹配的特征点进行三维重建,并计算出乒乓球不同时刻的速度和加速度,实现了动态物体的三维运动重建.实验结果表明该三维运动重建方法提高了特征提取的准确性,有效地实现了时间序列上的匹配,获得了物体的三维运动数据.     

基于图像识别的乒乓球选手头部运动及健康分析

近年来由于生活和工作的不良习惯,颈椎病的发病率成上升趋势,严重影响着人们的正常工作和身心健康.乒乓球运动作为深受广大人民喜爱的一项运动,对颈椎病的预防和治疗有很大的疗效.介绍了一种基于图像识别的检测乒乓球选手头部运动的方法,以检测目标范围中的像素块为主要算法,为乒乓球运动有益颈椎健康提供了科学并准确的依据,增强其说服力,鼓励大家多多参加乒乓球运动.