Automated visual inspection for surface appearance defects of varistors using an adaptive neuro-fuzzy inference system

The purpose of this study is to develop an automated visual inspection system for analysis of the surface appearance of ring varistors based on an adaptive neuro-fuzzy inference system (ANFIS). Known image patterns of the six types of ring varistors are used in a training process to establish Sugeno FIS rules, and the input-output data are then set to train the ANFIS to tune the membership function. Feature extraction reduces image complexity using two-dimensional edge detection, calculated within divided rectangular region. The ANFIS combines the neural network adaptive capabilities and fuzzy logic qualitative to train a classification system for six different types of components. The performance of the ANFIS is evaluated in terms of training performance and classification accuracy. The results confirm that the proposed ANFIS is capable of classifying the six types of ring varistors with an accuracy of 98.67%. This paper has not been published elsewhere nor has it been submitted for publication elsewhere.     

基于白色与黑色像素区域相间明显的快速边界获取算法

在实现工业机器视觉系统时,边界获取成为必不可少的一环,为了提高边界获取的速度和生产效率,介绍一种边界获取的简单有效的新方法,该方法是基于白色与黑色像素区域相间明显的条件下提出的。其主要思路是通过计算连续黑白像素宽度及相关处理,获得边界。该方法检测目标时快速,稳定;通过选取不同的像素宽度阀值,可以方便地检测出相应的不同的边界。此方法适用于处理速度要求非常高的工业机器视觉中特征边界的边界获取。     

Real-time biscuit tile image segmentation method based on edge detection

In this paper we propose a novel real-time Biscuit Tile Segmentation (BTS) method for images from ceramic tile production line. BTS method is based on signal change detection and contour tracing with a main goal of separating tile pixels from background in images captured on the production line. Usually, human operators are visually inspecting and classifying produced ceramic tiles. Computer vision and image processing techniques can automate visual inspection process if they fulfill real-time requirements. Important step in this process is a real-time tile pixels segmentation. BTS method is implemented for parallel execution on a GPU device to satisfy the real-time constraints of tile production line. BTS method outperforms 2D threshold-based methods, 1D edge detection methods and contour-based methods. Proposed BTS method is in use in the biscuit tile production line.     

基于提升小波的颤振图像边缘检测方法研究

这里基于提升小波理论重点研究了图像边缘检测问题，提出了一种新的图像多尺度边缘检测方法，该检测方法适用于多种线性或非线性双正交小波，具有运算速度快、检测精度高等特点；并基于此边缘检测技术，研究了飞机结构颤振的边界预测问题，给出了一种新的稳定性参数估计方法。     

一种检测光学元件面形的新方法

随着光学元件的广泛应用,对光学元件面形检测提出了更严格的要求。目前常用的检测光学元件面形的方法有数字刀口检测技术和干涉检测技术,比较这两种方法的检测原理及优缺点,提出了一种适用于工厂在线检测的三维检测方法——投影检测技术。用该方法的检测原理和关键技术对光学元件进行实验验证,证明了投影检测技术这一新方法具有实际应用价值。     

机械零件尺寸图象测量中的边缘检测算法

这里提出了一种应用于机械零件图象测量领域的边缘检测算法：使用基于Sobel算子的改进的方向算子,综合利用图像灰度信息和灰度梯度信息对目标边缘进行检测和亚像素精确定位。在文章的最后,用实例说明了本算法的可行性。     

Non-contact inspection for the detection of internal surface defects in hollow cylindrical work-pieces

The objective of this paper is to document part of a collaborative research program undertaken by the Centre for Computer Integrated Manufacture (CIM Centre) at Swinburne University of Technology in Hawthorn, Victoria, Australia and Australian Defence Industries (ADI) Ltd in the field of non-contact inspection. This research program is one of two collaborative programs between the CIM Centre and ADI Ltd, related to inspecting the quality of components in an automated fashion, without the use of contacting sensors.There are a number of techniques currently being investigated at the CIM Centre, including lasers, vision, acoustic emission and X-ray based methods. This particular paper focuses on the research work undertaken by the first author in the detection of internal surface defects in forged, hollow cylindrical workpieces. The case study presented for consideration and discussion herein is related to the detection of cavity defects in forged pressure vessels. The paper provides a background into the range of alternative non-contact inspection techniques that are available (including lasers, ultrasonics, etc.) and the reasons why some of these failed to provide the functionality that was offered by the vision approach ultimately adopted.There is nothing unique about the application of vision systems in the detection of surface defects. However, in this research program, a number of factors have considerably complicated the application. These include lighting problems and the difficulties encountered in acquiring images within a confined cylindrical space. This paper documents the techniques that have been used to resolve some of these practical image acquisition and processing problems and the relative merits of each approach. The paper also examines some of the algorithmic problems involved in detection of particular surface anomalies in components through a range of techniques and concludes that the one most suited to the surfaces in the pressure vessel case-study is the so-called region-growing approach.     

基于一种新边缘算法的轴承直径测量

边缘检测是特征提取、图像处理与分析的关键技术,针对现有边缘检测算法检测图像中的单边边缘多数会出现双边响应的问题,提出一种新的非边缘点抑制算法,并将这种算法应用于轴承径向尺寸测量中.实验表明,该算法可以有效地解决上述问题并获得良好的边缘.     

A solderability inspection system

The solderability in leads for electronics components is currently visually inspected only on the basis of the inspector's perception of the percentage of the lead's surface which is covered by solder. The subjectivity of this inspection criteria makes the consistency and reliability of the inspection questionable. The application of computer vision for the evaluation of solderability as an alternative to the current inspection method is presented in this paper. The proposed microcomputer-based vision system along with the computer vision algorithms are described. The testing of the system indicates that computer vision techniques are technically and economically feasible for solderability inspection.     

A robust approach to edge detection of scanned point data

In reverse engineering, segmentation is used to divide a point data set into subsequent regions according to its shape. It is vital for interpretation of discrete scanned data since surface reconstruction can be accomplished one-by-one on a given region. Edge detection is crucial to the segmentation process. The level of edge detection depends on the complexity of the part, and it determines the eventual success or failure of the reverse engineering (RE) process. This paper proposes a novel approach to the edge detection of 3D points based on a region growing technique. The proposed algorithm consists of two parts. First, polygonal meshes are generated to the scanned point data using the Delaunay triangulation algorithm. Second, the normal vector and the area of a polygonal mesh are checked to find boundary meshes using cost criteria (angle criterion and area criterion) based upon a region growing technique. The region growing technique aggregates meshes into a region until the area of aggregated meshes reaches an area threshold from a series of seed meshes. The proposed edge detection method is found to be effective when compared with other methods.     

The use of local entropy measures in edge detection for cytological image analysis

Accurate edge detection is a fundamental problem in the areas of image processing and pattern recognition/classification. The lack of effective edge detection methods has slowed the application of image processing to many areas, in particular diagnostic cytology, and is a major factor in the lack of acceptance of image processing in service orientated pathology. In this paper, we present a two-step procedure which detects edges. Since most images are corrupted by noise and often contain artefacts, the first step is to clean up the image. Our approach is to use a median filter to reduce noise and background artefacts. The second operation is to locate image pixels which are ‘information rich’ by using local statistics. This step locates the regions of the image most likely to contain edges. The application of a threshold can then pin-point those pixels forming the edge of structures of interest. The procedure has been tested on routine cytologic specimens.     

圆轴直径的自动视觉精密检测

介绍一种圆轴直径的自动视觉精密检测方法。首先通过对圆轴图像边缘像素进行线性拟合,把圆轴两条边缘定位到亚像素精度;然后运用细化技术,求得圆轴的中轴骨架;最后通过从中轴线上的一点引出垂线求得交于两条边缘交点的方法计算直径并与标准值比较。实验表明,本系统精度高,稳定性强,速度快。     

基于动态域值的鞋帮图像边缘提取方法

图像边缘提取是图像拼接中常用的一个重要的特征。随着CAD技术的发展,鞋帮图像库变得越来趣大．使得特征的自动提取显得越来越重要。然而在大图像库中．每幅图像的前景和背景间的变化均不相同,自动提取边缘过程中.不同图像边缘的阈值选取非常关键。为实现鞋帮太图像库中图像边缘的自动提取,阈值的动态调整是很重要的。本文提出的动态阈值调整法．能够自动提取各种变化不同的图像边缘。     

微小型结构件显微图像边缘的自动识别

在前一阶段的研究工作中,针对微小型结构件的显微图像边缘提取,作者提出了一种基于工艺匹配的显微图像边缘提取算法（Micro Processing Technology Matching based Micro Image Edge Detection, MPTM-MIED）。使用MPTM-MIED提取图像边缘之前,需要手工从零件显微图像中选取边缘过渡区域,这一操作将极大影响微小型零件的检测速度,从而使MPTM-MIED不能应用在实时自动检测中。针对MPTM-MIED的这一不足,本文利用BP神经网络技术重新设计并实现了MPTM-MIED,提出了一种新的自动提取显微图像边缘的方法（An Automated Micro Image Edge Detection Method,AMIED）。为了验证该方法的有效性,本文还利用AMIED对4种工艺微小型结构件显微图像的边缘进行了提取,并对线切割工艺零件的尺寸进行了测量。边缘提取的分析结果表明：AMIED提取出的显微图像边缘与MPTM-MIED提取出的基本一致;与常用的边缘检测算法相比,AMIED提取出的显微图像的边缘线形连接程度较好。测量尺寸的分析结果表明：MPTM-MIED和AMIED测量的尺寸基本相同,与Canny测量的相比,更接近万能工具显微镜测得的尺寸。     

基于Canny边缘检测和加权最小二乘法的气泡水平仪实时检测方法

针对气泡水平仪的自动标定问题,对机器视觉领域中的边缘提取算法、最小二乘法、轮廓跟踪算法等方面进行了研究。提出了一种基于Canny边缘检测和加权最小二乘法的气泡水平仪实时自动检测方法,以提高检测气泡水平仪的准确度和效率。引入了Canny边缘提取算法对工业摄像机所拍摄的水准柱侧面图像进行处理,以得到参考线和气泡的边缘信息。引入了一种自适应选取Canny边缘提取算法的阈值的方法,以克服工业现场光照变化的影响。同时针对本应用场合,采用了加权最小二乘法对左右平行参考线进行拟合,并结合二分搜索算法对水平尺上的气泡位置进行了搜索,从而实现了气泡水平仪的高准确度实时检测。研究结果表明,该方法能够准确、快速地对气泡进行定位,能够较好地适应光照变化。     

融合分数阶微分边缘特征的自适应跟踪

为提高跟踪方法对背景信息、光照变化的抗干扰能力,提出融合分数阶微分边缘特征信息的改进跟踪方法。将R-L分数阶微分边缘检测算子与Laplacian边缘检测算子进行融合,构造出对高、中频信息提升,而对低频信息能够非线性保留的混合边缘检测算子,并利用其实现目标模板及场景图像的边缘特征信息检测。基于目标色度特征和边缘特征两种直方图模型,分别建立场景图像的反向概率投影值,根据背景信息的动态变化,以抑制背景干扰信息为目的,建立自适应融合的反向概率投影图,提高算法对不确定环境变化信息的鲁棒性。实验结果表明,混合边缘检测算子能够提高边缘图像的信噪比,改善边缘提取的效果。边缘、色度特征信息自适应融合的跟踪方法单帧跟踪时间小于20 ms,满足实时性要求。该方法能够在光照变化明显和与目标颜色相似背景等情况下有效实现目标识别与跟踪功能。     

微小尺寸零件复杂边缘识别算法研究

目的：为了实现对微小尺寸零件复杂边缘的识别,根据微小尺寸零件加工特性,提出一种微小尺寸零件复杂边缘显微图像识别算法。方法：该算法通过对不同工件的边缘过渡区进行提取,建立不同工件边缘过渡区的数学模型,再从数学模型中根据加工特性确定边缘点精确位置。结果：实验结果表明：精研磨工艺的零级1mm标准量块的测量误差为0.395μm。结论：通过对精研磨、冲压、线切割三种常见微细加工方法建模分析得出：加工特性对微小型结构件边缘区域影响较大,边缘精确识别时应考虑加工特性的影响。该算法思想上考虑了实际加工特性的影响,算法上加入统计学方法,通过建立过渡区数学模型使边缘检测结果达到亚象素级。     

一种新型镶圈活塞自动检测机械装置设计

介绍了一种新型镶圈活塞自动检测机械装置的设计。探头调节机构具有4个自由度,滚轴转动采用圈弧型同步带传动技术,并通过脉宽调制技术实现滚轴转动速度的无级调节。该检测设备已投入现场使用,效果良好。     

基于小波变换的零件图像融合与边缘检测

提出基于小波变换的零件图像数据融合和边缘检测的方法,对图像进行分解,将高频区域中的绝对值较大的系数作为重要小波系数;在低频区域,对逼近系数进行加权平均得到新的逼近系数,然后进行小波重构实现图像数据融合。应用小波变换对融合图像进行多尺度边缘检测,获取图像边缘,或对图像进行小波多尺度边缘检测,然后融合边缘。     

表面贴装晶圆视觉检测技术的研究

视觉检测是半导体贴装设备的核心技术之一,该文重点阐述了视觉检测系统的总体设计,并提出一种检测晶圆偏转角度的有效算法.利用这些图像处理技术对芯片边缘点进行采集,并拟合成直线,从而计算出晶圆的偏转角度.实验结果表明,该系统具有较高的准确度,并且满足系统的实时性要求.