Scale-space line curvature estimation for straight line and circle detection

A scale-space algorithm for estimating the local curvature of lines (edges or isolines) is presented. Two variants of edge curvature estimation based on differential invariants are suggested and compared. The first variant uses the edge curvature formula derived in the paper and the second is based on image preprocessing, which allows one to use the isoline curvature formula for edge curvature estimation. An analysis of scale selection needed to reach a desired accuracy is presented. Also, noise influence analysis has been performed. The application of curvature estimation to detection of straight lines and circles is suggested and implemented. Curvature information usage in parametric curve detection speeds up search algorithms and makes the results more stable.     

使用多弦长曲率多项式的角点检测算法

在弦到点的距离累加(CPDA)技术和曲率积的基础上,提出了多弦长曲率多项式的角点检测算法。首先利用Canny边缘检测器抽取边缘,然后对于不同弦长下边缘轮廓曲率局部极大值点,计算曲率的和;对于非极值点,计算曲率的积。该方法不仅可以显著增强曲率极值点的峰值,而且避免了曲率积对一些角点平滑。最后,为了降低人为设定门限带来的错检或漏检,利用局部自适应阈值去判别角点。实验结果表明,与其他的角点检测算法相比,该方法具有很强的鲁棒性,它的平均检测准确率提高了14.5%,而且在角点数重复率准则上平均性能提高了12.6%。     

基于局部曲率函数的角点检测

角点检测是计算机图像处理领域的基本问题之一,在全局曲率函数角点检测的基础上,提出一种基于局部曲率函数的快速角点检测算法.通过Canny算子提取图像的边缘,在边缘的基础上计算轮廓的局部曲率函数,由于Canny算子首先用高斯滤波器对图像平滑处理,尽可能的去除了噪声而不损失角点,计算局部曲率函数的最大峰值,通过设定阈值检测出角点.对算法进行仿真,试验结果表明该算法可以稳定的检测角点,并对旋转、尺度和噪声具有较强的鲁棒性.     

基于自适应链码的棉种周长测量

在棉种实时检测系统中,为了识别破损棉种,必须提取棉种的边缘并计算其周长。传统的边缘提取方法有Sobel、Robert等微分算子,以及数学形态学方法和小波变换方法。但是在实时系统中,图像处理速度是影响实时性的关键因素。Freeman链码是图像处理中表示轮廓形状的方法之一,由提取的链码可以得到曲线（包括直线）的曲率、曲线的特征点等,而这些特征是图像压缩、图像重建、图像匹配的依据。由Freeman链码方法提取的图像边缘是单像素的,因此采用这种方法来计算棉种的周长。通过与Sobel、Robert等边缘提取方法进行比较,可以证明运用链码方法求周长,其搜索时间短,计算准确,尤其适用于以快速准确为目标的实时系统。     

Robust image corner detection through curvature scale space

This paper describes a novel method for image corner detection based on the curvature scale-space (CSS) representation. The first step is to extract edges from the original image using a Canny detector (1986). The corner points of an image are defined as points where image edges have their maxima of absolute curvature. The corner points are detected at a high scale of the CSS and tracked through multiple lower scales to improve localization. This method is very robust to noise, and we believe that it performs better than the existing corner detectors An improvement to Canny edge detector's response to 45° and 135° edges is also proposed. Furthermore, the CSS detector can provide additional point features (curvature zero-crossings of image edge contours) in addition to the traditional corners     

基于改进Canny算法的噪声图像边缘检测

针对传统Canny边缘检测算法对噪声图像的去噪效果不佳,以及双阈值需要预先设定的问题,提出了一种基于改进Canny算法的噪声图像的边缘检测。首先构建自适应高斯滤波器对曲度算子进行改进,得到优化的二值边缘图;然后基于最大类间方差法构建了灰度梯度映射函数,确定最佳的双阈值;最后对二值边缘图进行双阈值检测以及边缘连接。实验结果表明,改进算法与现有Canny算法相比,在不同类型噪声和不同浓度噪声的环境下,改进算法提高了对噪声图像边缘检测的性能,其中PSNR值平均提高了1.9%,MSE值平均降低了1.6%,且具有自适应性强、运行效率高的优点。     

基于曲率估计的Canny边缘检测算法

为了解决传统Canny边缘检测算法对噪声敏感的问题,针对噪声边缘弯曲度大且弯曲空间范围小的二维空间特性,构造了基于曲线曲率估计的边缘曲度算子,并利用大尺度Canny算法边缘检测结果对边缘曲度算子进行修正,使该算子能够准确地表征噪声强度在二维空间中的分布情况,在此基础上,提出了一种在边缘检测过程中加入边缘曲度算子进行噪声衰减的边缘检测算法. 实验结果表明,算法在有效抑制噪声的同时,保留了变化丰富的细节边缘及变化缓慢的轮廓边缘,检测结果较好地反映了图像的原始结构特征.     

Zernike矩和曲率的圆形中心亚像素定位

提出了一种基于Zernike矩和曲率不变的圆形标记椭圆图像中心的亚像素精确定位方法。首先采用多结构元多尺度形态学边缘检测算子提取椭圆图像的像素级边缘并滤除噪声,其次构造出椭圆图像的Zernike矩求解模型并结合曲率不变性计算出椭圆的亚像素边缘,最后利用最小二乘拟合对椭圆中心进行精确定位。实验结果表明：该方法具有计算速度快、定位精度高的优点,可用于高精度视觉测量。     

Edge detection and curvature calculation in the visual system

A new model of edge detection in a visual system is presented, where the edge locations are assumed to be at the place of negative peaks of ▿²G * I. Related schemes of detecting edges and calculating the curvature of the edge locus are also presented. It is assumed that the locations of receptive field centers of geniculate cells move with an input image. The arrangement of the receptive fields is given as a solution of an optimization problem. An edge detection algorithm based on the arrangement is presented. The edge detection algorithms are applied to three examples, i.e., Helmholtz irradiation, a face image, and a circular one.This work was presented, in part, at the 9th International Symposium on Artificial Life and Robotics, Oita, Japan, January 28–30, 2004     

面向惯性约束聚变实验靶图像的快速椭圆检测

目的：针对惯性约束核聚变实验中靶图像轮廓模糊、亮度不均匀等问题,并从提高图像处理实时性角度出发,提出了一种高可靠性和高精度的快速椭圆检测方法。方法：首先利用椭圆边缘点在它与圆心相连方向上具有较大灰度变化率这一特点,以预估中心点为极点建立极坐标系,通过从极点出发的射线上灰度变化率极值点搜索实现椭圆边缘点检测,极值点搜索在图像局部范围进行保证边缘点检测的有效性和实时性;其次利用基于RANSAC的自适应椭圆参数提取算法得到最终椭圆参数,该方法利用椭圆参数空间聚类分析选取最优椭圆参数,从而实现了一致样本集的自适应选择,保证了椭圆参数拟合精度的同时提高了算法的适应性和鲁棒性。结果：采用本文算法检测一幅图像的平均时间约为110ms,与常用椭圆检测方法相比检测速度有显著提高。结论：对比实验表明,本文提出的椭圆检测方法与其他方法相比具有更高的精度、更快的实时性和更强的鲁棒性。     

基于卡方检验的SAR图像道路检测算法

为有效解决SAR图像道路检测算法中阈值优化的问题,提出了一种不需要固定阈值的检测方法.该方法由不同阈值组合的Tupin边缘检测算法得到边缘检测结果图集,并逐像素统计得到边缘密度图;引入卡方检验计算边缘密度图与边缘检测结果图集之间的相似性度量参数,优化得到最佳边缘相关等级;根据最佳等级得到最终的道路边缘检测结果.实验结果表明,该方法有较强的开放性与目标适应性,自动化程度高,有很强的工程实用性.     

一种基于边缘轮廓线的LoG角点检测

角点是数字图像中目标的重要局部特征,提供了目标的低层次视觉特性。将Laplace of Gaussian变换引入到平面曲线,提出了一种基于边缘轮廓的LoG角点检测算子。深入分析LoG变换的几何特征,建立了边缘轮廓曲率和LoG范数间的等价度量关系。角点响应函数被定义为边缘点的LoG变换范数,边缘轮廓上LoG范数的局部极值点被认为是角点。并针对?祝-角点模型和圆周曲线模型,验证了这种定义的合理性。实验结果表明,相比经典角点检测算子,该文算法具有高效、稳定的优点。     

Randomized circle detection with isophotes curvature analysis

Circle detection is a critical issue in image analysis and object detection. Although Hough transform based solvers are largely used, randomized approaches, based on the iterative sampling of the edge pixels, are object of research in order to provide solutions less computationally expensive. This work presents a randomized iterative work-flow, which exploits geometrical properties of isophotes in the image to select the most meaningful edge pixels and to classify them in subsets of equal isophote curvature. The analysis of candidate circles is then performed with a kernel density estimation based voting strategy, followed by a refinement algorithm based on linear error compensation. The method has been applied to a set of real images on which it has also been compared with two leading state of the art approaches and Hough transform based solutions. The achieved results show how, discarding up to 57% of unnecessary edge pixels, it is able to accurately detect circles within a limited number of iterations, maintaining a sub-pixel accuracy even in the presence of high level of noise.     

Edge curvature and convexity based ellipse detection method

In this paper, we propose a novel ellipse detection method for real images. The proposed method uses the information of edge curvature and their convexity in relation to other edge contours as clues for identifying edge contours that can be grouped together. A search region is computed for every edge contour that contains other edge contours eligible for grouping with the current edge contour. A two-dimensional Hough transform is performed in an intermediate step, in which we use a new ‘relationship score’ for ranking the edge contours in a group, instead of the conventional histogram count. The score is found to be more selective and thus more efficient. In addition, we use three novel saliency criteria, that are non-heuristic and consider various aspects for quantifying the goodness of the detected elliptic hypotheses and finally selecting good elliptic hypotheses. The thresholds for selection of elliptic hypotheses are determined by the detected hypotheses themselves, such that the selection is free from human intervention. The method requires a few seconds in most cases. So, it is suitable for practical applications. The performance of the proposed ellipse detection method has been tested on a dataset containing 1200 synthetic images and the Caltech 256 dataset containing real images. In both cases, the results show that the proposed ellipse detection method performs far better than existing methods and is close to the ideal results, with precision, recall, and F-measure, all very close to 1. Further, the method is robust to the increase in the complexity of the images (such as overlapping ellipses, occluded ellipses), while the performance of the contemporary methods deteriorates significantly.     

多孔零件二维几何量高精检测系统的研究

为了提高工业零件检测效率和测量精度,研究了基于图像检测技术的多孔零件二维几何量的测量问题.先对CCD获取的零件图像进行分析处理以改善图像的可识别性,采用基于二次曲线拟合的快速亚像素边缘定位算法快速提取零件的精确边缘,从而提高系统的测量精度,并运用Hough变换计算被测零件的几何参数.实测结果表明,该系统的测量精度和准确度高,边缘定位精度达0.1 pixel,检测精度达101μm,最大相对误差为0.4%.     

板类结构动力检测与控制中的一种新方法

曲率模态在结构动力检测中具有对动力结构损伤部位非常敏感的特性，传统方法主要是运用中心差分法求解曲率模态，由于中心差分法的计算精度依赖于测点分布的紧密程度，这样就使动力检测结果具有很大的误差，本文利用函数的契贝雪夫多项式的展开式具有很高的逼近特性，提出了板类结构动力检测的曲率模态算法——契贝雪夫多项式算法，构造出了板类结构振型的契贝雪夫多项式函数，对该函数进行求二阶偏导得到x和y方向的曲率模态，进而求出结构损伤前后的曲率模态差，该方法可为结构损伤检测提供可靠的数据，从而达到良好的检测控制效果。     

橡胶密封圈几何尺寸图像检测技术研究

目前油冷散热器件密封圈尺寸检测使用万工显人工目测方法,工作强度高,测量数据稳定性差。针对这种情况,提出了使用万工显、CCD相机、光栅和计算机搭建自动测量系统,实现了密封圈几何尺寸的检测。采用CCD相机替代万工显的目镜,由光学系统将被测密封圈的边缘成像于CCD靶面上,数字图像通过USB口传入计算机,实现图像采集,由固定于测量平台的光栅尺读取平台移动数据。采集密封圈6幅边缘图像,同时把光栅读数读入计算机。经过中值滤波、阈值分割、边缘检测等图像处理方法,结合光栅读数,经过坐标转换,得到了同坐标系下的所有图像边缘坐标,所有边缘坐标数据采用最小二乘法拟合圆,得出密封圈的半径值。实验数据表明,CCD测量系统均方差为0.0001mm,低于目测系统的均方差值0.0005mm, CCD万工显测量系统测量数据稳定性好。     

An image processing-based crack detection technique for pressed panel products

Crack detection is an important step in assessing the quality of pressed panel products. This paper presents a fast and non-invasive crack detection technique which involves extracting the outline of the captured object and applying a unique edge line evaluation. This technique is robust against environmental condition changes and only require a low-cost web camera. After capturing an image immediately following the press process, a clear one-pixel edge line is extracted by applying a light control and a series of pre-image processing algorithms, including a valley-emphasis Otsu method and percolation-based shape recognition. Next, the initial detection at low resolution is applied to search for every possible crack using unique edge line and curvature evaluation. Finally, at high resolution, the windowed image of every possible crack is individually analyzed to detect existing cracks using a more specific evaluation process. All of these steps are completed within 0.5 s, thus allowing for the technique to be applied in real-time on a highly automated manufacturing line. To demonstrate the performance of the proposed technique, experiments are conducted on an aluminum plate with different patterns and the pressed panel products. The results show that the proposed technique can detect surface cracks on pressed panels with stable performance as well as high accuracy and efficiency.     

基于改进Zernike方法实现圆形边缘亚像素检测

在图像测量中,图像边缘的精确定位与检测是影响测量精度的关键。为了实现快速、高精度的图像边缘定位与检测,提出了一种改进的Zernike方法,采用四个方向模版Sobel算子对图像初处理,利用被测物的几何信息,只使用零阶矩实现对边缘的亚像素定位。实验结果表明改进算法比原算法具有更高的精度,而运行的时间不到原算法的1/4。该算法具有良好的处理效率,能够满足一般具有几何特征图像边缘实时、亚像素精度定位与检测的要求,具有广阔的应用前景。     

基于机器视觉的公差检测系统算法研究与应用——自适应边缘检测与修正Hough变换

介绍了基于机器视觉的几何量和位置公差检测中常见的计算模型、图像处理和直线拟合一般算法，进一步介绍了一种自适应的边缘检测方法。着重分析了传统的Hough变换的优势和缺点，在此基础上提出了适应形位公差检测特点的修正Hough变换算法。自适应边缘检测方法被应用于检测实际工程的图像边缘，检测到的边缘区域在亚像素之内；修正的Hough变换算法也被运用于相应工程的直线拟合，与传统的Hough算法相比，结果表明其速度更快、精度更高，抗干扰能力更强，其拟合直线的极径精度可达到0．1个像素，极角精度可达0．01°。