基于ARM-linux的显微镜自动对焦系统研究

本文介绍了一种在ARM-linux平台下实现显微镜自动对焦的方法.给出了以s3c2410处理器为核心,采用通用型USB摄像头进行采集图像、步进电机模块作为传动装置的系统软硬件方案及其实现步骤,并对如何在Linux平台下编写CMOS图像采集、图像清晰度评价函数等核心程序进行了讨论.     

自动对焦显微镜系统设计及仿真

设计了一种基于条纹投影的自动对焦显微镜系统。通过红外光源将光栅掩膜板投射到被测物表面,用于对焦判断。通过白光光源进行系统照明。使用带通滤光片分离出由红外光源投射的光栅像,并由线阵CCD采集,用于离焦量和离焦方向的判断。由白光光源照射得到被测物的像,并由面阵CCD接受用于直接观测。使用Tracepro对设计的系统进行仿真,分析得到适用于线阵CCD采集的光栅图的一维清晰度评价函数,并使用系统对不同被测物进行仿真对焦实验与误差分析。仿真结果表明,本系统可对不同被测物进行自动对焦。     

望远镜跟踪系统的自动对焦方法研究

为了满足望远镜跟踪系统的发展需求,研究了基于图像处理的自动对焦技术在望远镜跟踪系统中的应用。介绍了几种图像清晰度评价函数,包括梯度函数以及统计函数等,并通过计算机仿真和实验验证的方式分析了各种评价函数在此自动对焦系统中的应用和性能。研究结果表明自相关函数、能量梯度函数和Sobel函数都是有效的清晰度评价函数,其中自相关函数相对其他函数性能更加优越。     

机器视觉疲劳裂纹扩展试验摄像头自动聚焦方法

在机器视觉疲劳裂纹扩展试验中,为了能够满足对裂纹宏观观察裂纹和准确定位裂纹尖端的要求,需要采用变焦镜头放大和缩小采集图像范围,针对于摄像头变焦后的聚焦问题,提出了摄像头的自动聚焦方法。首先建立图像采集系统,采集裂纹图像至计算机,通过中值滤波对图像预处理,去除噪声干扰,选取裂纹区域作为聚焦窗口,采用Laplace算子法作为清晰度评价函数,并提出一种变步长穷举法进行聚焦搜索。最后设计了以ARM7为核心的摄像头运动控制器。实验表明,所提出的方法能够实现疲劳裂纹扩展试验中摄像头在各种情况下的准确自动聚焦,为下一步精确测量疲劳裂纹扩展参数奠定基础。     

基于机器视觉技术的棒材自动计数系统的研究与应用

对图像处理和机器视觉技术在我厂轧钢生产线上棒材自动计数和分钢控制系统中的应用进行了研究并对使用情况进行了介绍.该系统不但能满足实时性的要求,而且能够实现棒材的准确计数和分离.     

机器视觉系统组成研究

机器视觉检测系统解决了传统人工检测中的许多问题，在很多行业中得到广泛的应用。文章介绍了机器视觉系统的组成及一些著名的机器视觉产品生产厂家，并且介绍了一套完整的机器视觉系统的组成方案。     

机器视觉技术在智能锁封自动加封系统的应用

机器视觉技术以其快速性、实时性、非接触性等特点,成为了工程中备受关注的领域之一。现如今,机器视觉在工业生产中的运用逐渐增加,在新型智能锁封自动加封系统中,多处都应用了机器视觉技术。分析了机器视觉技术在智能锁封自动加封系统生产线中的功能和应用,以期为相关工作提供借鉴。     

基于机器视觉的珍珠光洁度在线自动分级

为代替耗时的传统人工分选,设计了一种基于机器视觉的珍珠光洁度在线自动分级系统。首先,该系统在珍珠自由落体时对珍珠进行全方位成像;然后对图像进行实时分析,计算珍珠的有效特征;最后,根据多图的有效特征确定珍珠的光洁度等级。实践证明,该系统不但提高了珍珠光洁度的分选效率,而且节省了企业的生产成本。     

机器视觉中的图像采集技术

文章介绍了图像采集系统的构成以及在机器视觉中的重要性，阐述了图像采集系统中关于摄像机、镜头、图像采集卡和光源等方面的诸多实用技术，论述了设计图像采集系统时要注意的问题和原则。     

基于DSP的自动对焦系统

介绍了一种基于DSP芯片TMS320F206进行数值计算和实施控制的自动对焦系统。给出了系统的硬件构成和软件设计。该系统不仅充分发挥了DSP芯片的数值计算优势,而且拓展了其在人机对话和电机控制等输入输出方面的应用。     

异纤在线分拣系统机器视觉技术的应用

棉花中夹带的异性纤维比重虽小,危害却很大。本文针对异纤的检测问题,开发了一种基于机器视觉技术的分拣系统。通过严格的理论证明,建立了图像处理的数学模型,对其算法进行了深入的分析和研究,提出了基于计算机图形学理论中三维旋转坐标变换的图像处理算法,并利用紫外线的荧光效应区分两者的特征差别。实验表明,系统的可靠性、速度等指标均满足工业要求,所提出的数学模型和算法满足系统精确性和实时性的要求。     

Automatic target scoring system using machine vision

This paper reports on the development of a machine vision system for assessing targeting accuracy of ballistic, projectile-firing, weapon systems. The machine vision system developed here can monitor the target continuously, report precise quantitative targeting information, and simultaneously provide a color-coded display of impacts. The only other automated target scoring system on the market today is based on the analysis of acoustic signatures of projectiles using a heavily instrumented target. The proposed vision-based technique, in contrast, is inherently passive, requiring little or no modification of the existing set up.     

A sequential machine vision procedure for assessing paper impurities

We present a sequential, two-step procedure based on machine vision for detecting and characterizing impurities in paper. The method is based on a preliminary classification step to differentiate defective paper patches (i.e., with impurities) from non-defective ones (i.e., with no impurities), followed by a thresholding step to separate the impurities from the background. This approach permits to avoid the artifacts which occur when thresholding is applied to paper samples that contain no impurities. We discuss and compare different solutions and methods to implement the procedure and experimentally validate it on a datasets of 11 paper classes. The results show that a marked increase in detection accuracy can be obtained with the two-step procedure in comparison with thresholding alone.     

机器视觉实现方便面破损在线检测的研究

提出一种适于实时在线检测方便面面块破损的方法，即通过建立计算机视觉系统，获取面块图像．针对方便面特点利用“围剿算法”分割出面块图像，然后通过“削切算法”除去面块边缘毛刺，以便于求得面块的“真边界”，然后求取面块与其外接矩形面积的比值对方便面的破损进行快速判断，本实验另辟蹊径，其优势十分明显，识别率高，速度快。     

Electric contacts inspection using machine vision

Machine vision is an excellent tool for inspecting a variety of industrial items such as textiles, printed circuit boards, electric components, labels, integrated circuits (IC), machine tools and fruits. In this paper, we propose machine vision-based inspection system for electric contact (EC), which are popularly used in switches, breakers and relays, and are important components in the electrical industry. The proposed system consists of three sub-systems, which inspect the top, side, and bottom surfaces of electric contact for different types of defects respectively. The system acquires the digital image of three views and classifies the surface defects including cracks, breaks, and scratches. For each view, this study develops different image pre-processing and feature extraction methods to enhance and detect the surface defects. The proposed system was implemented and verified using 229 samples collected from the EC production lines. Experimental results show the proposed system is effective and efficient in identifying EC defects.     

机器视觉技术中图像投影的算法

机器视觉技术在社会各行业生产和检测中的应用越来越多,那么对于机器视觉系统采集到得图像的真实性的要求也就越高,本文利用图像坐标使图像翻转产生投影,并在该算法的基础上利用图像的渐变透明度提高图像的真实性,算法本身的复杂度非常低,大大提高了程序的运行速度,使得机器视觉技术采集的图像处理得更快更具真实效果。     

A machine vision approach to the grading of crushed aggregate

The grading of crushed aggregate is carried out usually by sieving. We describe a new image-based approach to the automatic grading of such materials. The operational problem addressed is where the camera is located directly over a conveyor belt. Our approach characterizes the information content of each image, taking into account relative variation in the pixel data, and resolution scale. In feature space, we find very good class separation using a multidimensional linear classifier. The innovation in this work includes (i) introducing an effective image-based approach into this application area, and (ii) our supervised classification using wavelet entropy-based features.     

Identification of milling inserts in situ based on a versatile machine vision system

This paper proposes a novel method for in situ localization of multiple inserts by means of machine vision techniques, a challenging issue in the field of tool wear monitoring. Most existing research works focus on evaluating the wear of isolated inserts after been manually extracted from the head tool. The method proposed solves this issue of paramount importance, as it frees the operator from continuously monitoring the machining process and allows the machine to continue operating without extracting the milling head for wear evaluation. We use trainable COSFIRE filters without requiring any manual intervention. This trainable approach is more versatile and generic than previous works on the topic, as it is not based on, and does not require, any domain knowledge. This allows an automatic application of the method to new machines without the need of specific knowledge on machine vision. We use an experimental dataset that we published to test the effectiveness of the method. We achieved very good performance with an F₁ score of 0.9674, in the identification of multiple milling head inserts. The proposed approach can be considered as a general framework for the localization and identification of machining pieces from images taken from mechanical monitoring systems.     

机器视觉技术研究进展及展望

摘 要：机器视觉是建立在计算机视觉理论工程化基础上的一门学科,涉及到光学成像、 视觉信息处理、人工智能以及机电一体化等相关技术。随着我国制造业的转型升级与相关研究 的不断深入,机器视觉技术凭借其精度高、实时性强、自动化与智能化程度高等优点,成为了 提升机器人智能化的重要驱动力之一,并被广泛应用于工业生产、农业以及军事等各个领域。 在广泛查阅相关文献之后,针对近十多年来机器视觉相关技术的发展与应用进行分析与总结, 旨在为研究学者与工程应用人员提供参考。首先,总结了机器视觉技术的发展历程、国内外的 机器视觉发展现状;其次,重点分析了机器视觉系统的核心组成部件、常用视觉处理算法以及 当前主流的机器视觉工业软件;然后,介绍了机器视觉技术在产品瑕疵检测、智能视频监控分 析、自动驾驶与辅助驾驶与医疗影像诊断等 4 个典型领域的应用;最后分析了当前机器视觉技 术所面临的挑战,并对其未来的发展趋势进行了展望,为机器视觉技术的发展和应用推广发挥 积极作用。