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建立了一个基于嵌入式的智能视觉伺服系统,用于工业机器人对零件工位的精确定位。采用基于区域的匹配和形状特征识别相结合的图像处理方法,该方法经过阈值和形状判据,识别出物体特征;基于组件思想的软件系统具有可重构性。经实验验证,该方法能够快速准确地得到物体的边界和质心,进行数据识别和计算,再结合机器人运动学原理控制机器人实时运动以消除此误差,满足工业机器人跟踪定位的要求。 相似文献
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机器视觉系统是近年来的一个研究热点.现有研究成果在系统复杂度,价格和性能之间很难达到平衡。针对此问题,本文设计了一个以USB数码摄像头为图像采集设备。ARM为核心处理器的嵌入式机器视觉系统。该系统结构简单.可广泛应用在工业检测领域。 相似文献
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某种扁平细长带钩零件的形状不规则、结构精细,但一致性要求极高,人工检测其变形的效率低、精准度差,为此研发了一种基于机器视觉的检验系统.首先通过设计环面自动移位系统提高零件移位速度,采用高精度相机和远心镜头获得高质量图像;其次利用模板匹配算法定位目标,对模板进行形态学操作,提取轮廓ROI作为检验标准,将其经旋转、平移变换... 相似文献
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基于边缘的模板匹配在零件检测中的应用 总被引:1,自引:1,他引:0
对机器视觉在工业零件检测中的应用做了研究。在大批量的工业生产中,某个细微零件的缺失如果不应用机器视觉系统,将会浪费很大的人力物力,因此根据现阶段工业零件检测对智能零件缺失检测的需求,运用图像处理技术,在检测对象的标准样本中截取模板,利用零件的边缘特征,进行零件的模板匹配,进而检测出零件是否缺失。 相似文献
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针对传统的轴承人工测量方法效率低、易疲劳等问题,机器视觉具有精度高、速度快、非接触性等优点,在轴承检测中有着广阔的应用前景和研究价值。基于机器视觉原理构建轴承检测实验装置,通过图像预处理、拟合圆轮廓、模板匹配等图像处理算法,实现了轴承内外径轮廓提取和不同背景中目标轴承的识别,通过应用验证了在轴承自动检测方面有一定的工程应用意义。 相似文献
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介绍了应用数字图像处理和识别技术检测手机面板质量问题的技术和方法,结合特征提取等算法,通过适当的处理和分析,对手机面板图像中按键的目标文字进行自动检测与识别. 相似文献
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目的:为了实现自动检测血型,建立了基于嵌入式机器视觉的血型自动识别系统,并通过实验验证系统可行性和可靠性。方法:首先,依据血型检测的原理,以日本CCS同轴照明LED视觉检测光源、光源控制器、毛玻璃片、白色背景构成照明模块,以中星微301芯片的USB摄像头和mini2440开发板为核心,构建了集图像采集和分析于一体的嵌入式硬件模块。基于面向对象的思想,利用EVC(Embedded Visual C++)开发工具设计了系统的软件。然后,针对采集的微柱凝胶卡图像特点,通过试验分析,设计了具体的图像预处理算法。其中,运用小波变换完成图像对比度增强,运用遗传算法完成图像分割。在此基础上,设计了红细胞凝集物的特征参数提取算法。最后,利用BP神经网络构建了识别模型,完成对红细胞凝集物的类别判断,从而根据判别规则得到血型的检测结果。结果:多次试验结果表明:系统对-、+、++、+++、++++五类红细胞凝集物的判别正确率均为100%;能100%地正确检测出正常标本的ABO和Rh血型。结论:系统基本能满足临床对于血型检测自动化识别、准确性好、抗干扰能力强、稳定可靠等要求。 相似文献
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一种基于智能相机和机器视觉的电池极片检测系统的设计 总被引:1,自引:0,他引:1
介绍了一种基于智能相机和机器视觉的电池极片检测系统,以智能相机为平台,通过图像平滑、边缘提取、区域填充、数学形态学等多种方法处理,得到极片缺陷完整的几何特征,从而实现自动化检测。系统不受工艺要求影响,对空间要求较低,有较强的适用性,检测结果与人眼检测结果相符,不仅保证了检测的准确性,而且大大提高了极片检测的效率。 相似文献
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针对现有Linux系统实时性不足的特点,分析了实时性改造原理,采用了Linux+Xenomai构架的实时性改造方案,并根据系统任务实时性强弱规划了系统软件模块;最后,完成了对系统实时性能的实际测量.结果表明,此构架完全可以满足嵌入式数控系统的实时性要求. 相似文献
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作为监护终端的智能监护设备完成人体生理参数的智能检测与诊断,同时向远程监护中心连续传输各项与健康相关的生理参数信息,监护系统实时识别健康危险信息并发出报警信号.在设备的设计过程中,应用了基于嵌入式的Windows CE操作系统,采用PCM9375硬件平台,设计了基于IP通信的网络传输系统并在Visual Studio2005的智能设备开发环境下进行软件开发.系统对连续监测获得的生理参数进行实时分析处理,对不同年龄、性别、体质、疾病的监护对象建立因人而异的监护报警模型,实现监护的智能化. 相似文献
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Yong-Hwan Bae Seok-Hee Lee Ho-Chan Kim Byung-Ryong Lee Jaejin Jang Jay Lee 《The International Journal of Advanced Manufacturing Technology》2006,29(5-6):590-597
Modern manufacturing systems and their failure modes are very complex, and efficient fault diagnosis is essential for higher
productivity. However, traditional fault diagnostic systems that perform sequential fault diagnosis can fail during diagnosis
when fault propagation is very fast. This paper describes a real-time intelligent multiple fault diagnostic system (RIMFDS).
This system deals with multiple fault diagnosis, and is based on multiprocessing by using a strata hierarchical artificial
neural network (SHANN). If another fault occurs while an existing symptom is being diagnosed, the corresponding diagnosis
module is triggered, and the fault diagnosis module of the new faulty unit begins to diagnose the faults in real time. RIMFDS
can diagnose multiple faults with fast fault propagation and complex physical phenomena. The system consists of two main parts.
One is a personal computer for remote signal generation and transmission, and the other is a host system for multiple fault
diagnosis. The signal generator generates various faulty signals and image information and sends them to the host. The host
has various modules and agents for efficient multiple fault diagnosis. A SUN workstation is used as a host for multiple fault
diagnosis and graphic representation of the results. 相似文献
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A real-time intelligent multiple fault diagnostic system 总被引:3,自引:0,他引:3
Yong-Hwan Bae Seok-Hee Lee Ho-Chan Kim Byung-Ryong Lee Jaejin Jang Jay Lee 《The International Journal of Advanced Manufacturing Technology》2006,29(5):590-597
Modern manufacturing systems and their failure modes are very complex, and efficient fault diagnosis is essential for higher
productivity. However, traditional fault diagnostic systems that perform sequential fault diagnosis can fail during diagnosis
when fault propagation is very fast. This paper describes a real-time intelligent multiple fault diagnostic system (RIMFDS).
This system deals with multiple fault diagnosis, and is based on multiprocessing by using a strata hierarchical artificial
neural network (SHANN). If another fault occurs while an existing symptom is being diagnosed, the corresponding diagnosis
module is triggered, and the fault diagnosis module of the new faulty unit begins to diagnose the faults in real time. RIMFDS
can diagnose multiple faults with fast fault propagation and complex physical phenomena. The system consists of two main parts.
One is a personal computer for remote signal generation and transmission, and the other is a host system for multiple fault
diagnosis. The signal generator generates various faulty signals and image information and sends them to the host. The host
has various modules and agents for efficient multiple fault diagnosis. A SUN workstation is used as a host for multiple fault
diagnosis and graphic representation of the results. 相似文献
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主要研究利用机器视觉的非接触式测量方法实现对运动物体特征点的轨迹、速度和加速度三个运动参数的测定。通过在运动物体上设定一个特征点,采用基于运动估计的模板匹配技术实时跟踪特征点在各个时间点的位置。通过对动态图像序列的分析和处理从而在线得到运动物体特征点的三个运动参数。为了实现实时在线测量,采用了基于运动估计的图像匹配算法加速了图像匹配算法的计算速度,实现实时在线测量。此算法计算一次位置点需要的时间为20ms,图像标定的精度为1μs,达到了实时在线测量的要求。测试结果表明:研究的基于运动估计的运动物体特征点测量算法运算速度快、可靠以及测试简单等特点。 相似文献
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In this paper, an innovative dual-field measurement system is proposed for measuring the real-time material flow on the conveyor belt. The system consists of two light sources to illuminate the upper and the lower surface of the conveyor belt, respectively, and two binocular cameras to capture the dual-field contour images. The contour curves are extracted from the images by the contour acquisition algorithm and fitted with linear interpolation functions for the calculation of instantaneous cross-sectional area of material flow. Then the real-time volume of material flow is obtained according to the belt speed. Compared with conventional visual methods, the proposed method is no need to preliminarily acquire the data of the empty belt as well as hardly affected by belt deformation. Some measured objects are prepared for both sectional area and volume measurement. The results show that the accuracy of the proposed system can achieve up to 96.3% and 96.05% for the volume measurement of regular materials and coals, respectively, which is superior to the conventional visual method. The proposed measurement method has strong robustness and low construction cost, which is expected to generalize and apply in the bulk material transport field. 相似文献