光电混合处理系统在机器人视觉目标识别中的应用研究

分析了光电混合处理系统应用于机器人视觉识别的可行性和研究意义,评述了光电 混合处理系统在光学模式识别和机器人视觉识别领域中的研究现状．提出将JTC系统在机器 人视觉识别中的应用划分为五个研究层次,分析综述了与各类研究层次相关的形态学JTC和 三维JTC的代表性算法及JTC硬件结构,以此作为机器人视觉识别光电混合处理系统的研究基 础．     

微型直升机自主飞行辅助视觉系统研究

为了实现微型直升机自主飞行,论文设计了实时的计算机视觉系统。该系统包括机载上的硬件设备和软件算法,能够实现目标识别、特征点提取和位置估计。文中设计了特定图标,并主要依据颜色来进行快速识别。此外,通过检测4个共面的图像特征点,能够在单目单帧的基础上计算出直升机相对于特定目标的三维位置信息。视觉系统任务由位于机载上的IntelSitsang板来执行。由于受到板子运算能力的限制,每秒钟可以处理5帧160×120的彩色图像。飞行实验结果表明,该视觉系统的目标识别率高达93%,角点计算偏差小于3个像素,同时自身定位的三维位置坐标平均偏差为8cm、13cm和6cm。因此,该视觉系统通过精确自身定位,能够很好的辅助微型直升机完成自主悬停、起飞和降落等飞行任务。     

计算机视觉下的果实目标检测算法综述

基于计算机视觉的果实目标检测识别是目标检测、计算机视觉、农业机器人等多学科的重要交叉研究课题,在智慧农业、农业现代化、自动采摘机器人等领域,具有重要的理论研究意义和实际应用价值。随着深度学习在图像处理领域中广泛应用并取得良好效果,计算机视觉技术结合深度学习方法的果实目标检测识别算法逐渐成为主流。本文介绍基于计算机视觉的果实目标检测识别的任务、难点和发展现状,以及2类基于深度学习方法的果实目标检测识别算法,最后介绍用于算法模型训练学习的公开数据集与评价模型性能的评价指标,且对当前果实目标检测识别存在的问题和未来可能的发展方向进行讨论。     

An on-board vision sensor system for small unmanned vehicle applications

This paper describes an on-board vision sensor system that is developed specifically for small unmanned vehicle applications. For small vehicles, vision sensors have many advantages, including size, weight, and power consumption, over other sensors such as radar, sonar, and laser range finder, etc. A vision sensor is also uniquely suited for tasks such as target tracking and recognition that require visual information processing. However, it is difficult to meet the computing needs of real-time vision processing on a small robot. In this paper, we present the development of a field programmable gate array-based vision sensor and use a small ground vehicle to demonstrate that this vision sensor is able to detect and track features on a user-selected target from frame to frame and steer the small autonomous vehicle towards it. The sensor system utilizes hardware implementations of the rank transform for filtering, a Harris corner detector for feature detection, and a correlation algorithm for feature matching and tracking. With additional capabilities supported in software, the operational system communicates wirelessly with a base station, receiving commands, providing visual feedback to the user and allowing user input such as specifying targets to track. Since this vision sensor system uses reconfigurable hardware, other vision algorithms such as stereo vision and motion analysis can be implemented to reconfigure the system for other real-time vision applications.     

基于混合式协同训练的人体动作识别算法研究

人体动作识别是计算机视觉研究中备受关注的课题。现有的动作识别方法大多属于监督学习,需要大量的有标记数据来训练识别模型。然而,在现实应用中有标记的数据成本较高,而无标记数据很容易获取。提出一种基于混合式协同训练的新型人体动作识别算法——Co-KNN-SVM,该算法利用动作识别领域不同类型的方法来构建基分类器,并进行迭代的相互训练以提高泛化性能,可以降低标注成本,并实现不同识别方法的优势互补。此外,还改进了协同训练中对伪标记数据的选择方法和迭代训练策略,有效控制了伪标记数据的噪声影响,提高了协同训练的识别效果。实验结果表明,所提算法可以有效地识别视频中的人体动作。     

基于随机游走模型的物体识别

针对传统物体识别算法中只依赖于视觉特征进行识别的单一性缺陷,提出了一种结合先验关系的物体识别算法。在训练阶段,通过图模型结构化表示先验关系,分别构建了图像-图像、语义-语义两个子图以及两子图之间的联系,利用该图模型建立随机游走模型;在识别阶段,建立待识别图像与随机游走模型中的图像节点和语义节点的关系,在该概率模型上进行随机游走,将随机游走的结果作为物体识别的结果。实验结果证明了结合先验关系的物体识别算法的有效性;提出的物体识别算法具有较强的识别性能。     

A digital retina-like low-level vision processor

This correspondence presents the basic design and the simulation of a low level multilayer vision processor that emulates to some degree the functional behavior of a human retina. This retina-like multilayer processor is the lower part of an autonomous self-organized vision system, called Kydon, that could be used on visually impaired people with a damaged visual cerebral cortex. The Kydon vision system, however, is not presented in this paper. The retina-like processor consists of four major layers, where each of them is an array processor based on hexagonal, autonomous processing elements that perform a certain set of low level vision tasks, such as smoothing and light adaptation, edge detection, segmentation, line recognition and region-graph generation. At each layer, the array processor is a 2D array of k/spl times/m hexagonal identical autonomous cells that simultaneously execute certain low level vision tasks. Thus, the hardware design and the simulation at the transistor level of the processing elements (PEs) of the retina-like processor and its simulated functionality with illustrative examples are provided in this paper.     

图像纹理分类方法研究进展和展望

纹理分类是计算机视觉和模式识别领域的一个重要的基本问题,也是图像分割、物体识别、场景理解等其他视觉任务的基础.本文从纹理分类问题的基本定义出发,首先,对纹理分类研究中存在的困难与挑战进行阐述;接下来,对纹理分类方面的典型数据库进行全面梳理和总结;然后,对近期的纹理特征提取方法的发展和现状进行归类总结,并对主流纹理特征提取方法进行了详细的阐述和评述;最后,对纹理分类发展方向进行思考和讨论.     

采用预配置策略的可重构混合任务调度算法

在对可重构硬件资源进行抽象的基础上,采用软硬件混合任务有向无环图来描述应用,提出一种基于列表的混合任务调度算法.该算法通过任务计算就绪顺序及可重构资源状态确定硬件任务的动态预配置优先级,按此优先级进行硬件任务预配置,隐藏硬件任务的配置时间,从而获得硬件任务运算加速.实验结果表明,针对可重构系统中的软硬件混合任务调度,能够有效地降低配置时间对应用执行时间的影响.     

基于轻量型卷积神经网络的交通标志识别方法

交通标志识别设备的功耗和硬件性能较低,而现有卷积神经网络模型内存占用高、训练速度慢、计算开销大,无法应用于识别设备.针对此问题,为降低模型存储,提升训练速度,引入深度可分离卷积和混洗分组卷积并与极限学习机相结合,提出两种轻量型卷积神经网络模型:DSC-ELM模型和SGC-ELM模型.模型使用轻量化卷积神经网络提取特征后,将特征送入极限学习机进行分类,解决了卷积神经网络全连接层参数训练慢的问题.新模型结合了轻量型卷积神经网络模型内存占用低、提取特征质量好以及ELM的泛化性好、训练速度快的优点.实验结果表明.与其他模型相比,该混合模型能够更加快速准确地完成交通标志识别任务.     

CPU/FPGA混合架构上的硬件线程加速方法

CPU/FPGA混合架构是可重构计算的普遍结构,为了简化混合架构上FPGA的使用,提出了一种硬件线程方法,并设计了硬件线程的执行机制,以硬件线程的方式使用可重构资源.同时,软硬件线程可以通过共享数据存储方式进行多线程并行执行,将程序中计算密集部分以FPGA上的硬件线程方式执行,而控制密集部分则以CPU上的软件线程方式执行.在Simics仿真软件模拟的混合架构平台上,对DES,MD5SUM和归并排序算法进行软硬件多线程改造后的实验结果表明,平均执行加速比达到了2.30,有效地发挥了CPU/FPGA混合架构的计算性能.     

面向可重构系统的eCos拓展

针对可重构系统中任务模型灵活性差、硬件任务重构延时长、FPGA资源利用率低等问题,提出了将应用程序划分为软件任务和混合任务的划分模式,并在eCos的基础上,通过重构控制机制、混合任务管理机制、通信机制三方面的拓展,设计了支持可重构系统的嵌入式操作系统框架eCos4RC。仿真结果表明,eCos4RC实现了对混合任务的有效管理,在兼容eCos多线程机制的同时提高了应用程序执行速度和可重构资源利用率,为可重构计算平台提供了良好的运行环境支持。     

Fusion Strategies for Minimizing Sensing-Level Uncertainty in Manipulator Control

Humanoid robotic applications require robot to act and behave like human being. Following soft computing like approach human being can think, decide and control himself in unstructured dynamic surroundings, where a great degree of uncertainty exists in the information obtained through sensory organs. In the robotics domain also, one of the key issues in extracting useful knowledge from sensory data is that of coping with information as well as sensory uncertainty at various levels. In this paper a generalized fusion based hybrid classifier (ANN-FDD-FFA) has been developed and applied for validating on generated synthetic data from observation model as well as from real hardware robot. The fusion goal, selected here, is primarily to minimize uncertainties in robotic manipulation tasks that are based on internal (joint sensors) as well as external (vision camera) sensory information. The effectiveness of present methodology has been extensively studied with a specially configured experimental robot having five degrees of freedom and a simulated model of a vision guided manipulator. In the present investigation main uncertainty handling approach includes weighted parameter selection (of geometric fusion) by a trained neural network that is not available in standard manipulator robotic controller designs. These approaches in hybrid configuration has significantly reduce the uncertainty at different levels for faster and more accurate manipulator control as demonstrated here through rigorous simulations and experimentations.     

Development and characterization of a multi-camera 2D-vision system for enhanced performance of a drink serving robotic cell

A 2D-vision system is integrated into a drink-serving robotic cell, to enhance its flexibility. Two videocameras are used in a hybrid configuration scheme. The former is rigidly mounted on the robot end effector, the latter is fixed to the workplace. The robot cell is based on two Denso robots that interoperate to simulate real human tasks. Blob analysis, template matching and edge detection algorithms cooperate with motion procedures for fast object recognition and flexible adaptation to the environment. The paper details the system workflow, with particular emphasis to the vision procedures. The experimental results show their performance in terms of flexibility and robustness against defocusing, lighting conditions and noise.     

Survey of neural network technology for automatic targetrecognition

A review is presented of ATR (automatic target recognition), and some of the highlights of neural network technology developments that have the potential for making a significant impact on ATR are presented. In particular, neural network technology developments in the areas of collective computation, learning algorithms, expert systems, and neurocomputer hardware could provide crucial tools for developing improved algorithms and computational hardware for ATR. The discussion covers previous ATR system efforts. ATR issues and needs, early vision and collective computation, learning and adaptation for ATR, feature extraction, higher vision and expert systems, and neurocomputer hardware.     

Design of automated hybrid work stations: An evaluation of robot sensory systems for safety

Hybrid robotic systems necessitate a new integrated approach to the design of tasks and the performance requirements for human operators and robots. The presence of operators in hybrid work stations adds to the complexity and unpredictability of such design requirements. An important component of the hybrid system design is the integration of both human and robot sensory capabilities for task completion. A model for the integration of human and robot sensory information collection, processing, and action is presented. Robot sensory systems are evaluated with respect to the safety of operators within a hybrid work station. Four sensory technologies of optical (vision), sonar, capacitance, and infrared are compared. Optically-based and infrared sensors appear to be the most promising in terms of the safety and efficiency of hybrid work stations.     

Dynamic wires: An alanog VLSI model for object-based processing

The dynamic-wire methodology provides dedicated lines of communication among groups of pixels of an image which share common properties. In simple applications, object regions can be grouped together to compute the area or the center of mass of each object. Alternatively, object boundaries may be used to compute curvature or contour length. These measurements are useful for higher-level tasks such as object recognition or structural saliency. The dynamic-wire methodology is efficiently implemented in fast, low-power analog hardware. Switches create a true electrical connection among selected pixels, dynamically configuring wires or resistive networks on the fly. Dynamic wires provide a model for object-based processing. This approach is different from present early vision chips which are limited to pixel-based or image-based operations. Using this methodology, we have successfully designed and demonstrated a custom analog VLSI chip which computes contour length.     

视觉Transformer识别任务研究综述

Transformer模型在自然语言处理领域取得了很好的效果,同时因其能够更好地连接视觉和语言,也激发了计算机视觉界的极大兴趣。本文总结了视觉Transformer处理多种识别任务的百余种代表性方法,并对比分析了不同任务内的模型表现,在此基础上总结了每类任务模型的优点、不足以及面临的挑战。根据识别粒度的不同,分别着眼于诸如图像分类、视频分类的基于全局识别的方法,以及目标检测、视觉分割的基于局部识别的方法。考虑到现有方法在3种具体识别任务的广泛流行,总结了在人脸识别、动作识别和姿态估计中的方法。同时,也总结了可用于多种视觉任务或领域无关的通用方法的研究现状。基于Transformer的模型实现了许多端到端的方法,并不断追求准确率与计算成本的平衡。全局识别任务下的Transformer模型对补丁序列切分和标记特征表示进行了探索,局部识别任务下的Transformer模型因能够更好地捕获全局信息而取得了较好的表现。在人脸识别和动作识别方面,注意力机制减少了特征表示的误差,可以处理丰富多样的特征。Transformer可以解决姿态估计中特征错位的问题,有利于改善基于回归的方法性能,还减少了三维估计时深度映射所产生的歧义。大量探索表明视觉Transformer在识别任务中的有效性,并且在特征表示或网络结构等方面的改进有利于提升性能。     

Invariance analysis of modified C2 features: case study—handwritten digit recognition

Humans are very efficient in recognizing alphanumeric characters, even in the presence of significant image distortions. Recent advances in visual neuroscience have led to a solid model of object and shape recognition in the visual ventral stream which competes with the state-of-the-art computer vision systems on some standard recognition tasks. A modification of this model is also proposed by adding more biologically inspired properties such as sparsification of features, lateral inhibition and feature localization to enhance its performance. In this study, we show that using features proposed by the modified model results in higher handwritten digit recognition rates compared with the original model over English and Farsi handwritten digit datasets. Our analyses also demonstrate higher invariance of the modified model to various image distortions.     

基于迁移学习的类别级物体识别与检测研究与进展

类别级物体识别与检测属于计算机视觉领域的一个基础性问题,主要研究在图像或视频流中识别和定位出其中感兴趣的物体.在基于小规模数据集的类别级物体识别与检测应用中,模型过拟合、类不平衡和跨领域时特征分布变化等关键问题与挑战交织在一起.本文介绍了迁移学习理论的研究现状,对迁移学习理论解决基于小规模数据集的物体识别与检测中遇到的主要问题的研究思路和前沿技术进行了着重论述和分析.最后对该领域的研究重点和技术发展趋势进行了探讨.