基于Hough变换的圆检测方法

总结了圆检测的几种常用方法,如经典HT、随机HT和广义HT.结合几种方法的优缺点,提出了一种基于经典HT的改进Hough变换圆检测方法.该方法先对图像进行预处理,如灰度化、去噪滤波、边缘检测以及运用数学形态学等,然后进行Hough变换.其主要思想是用多维数组来代替经典的循环过程.把Hough变换应用到织物防水性能自动测试的真实图像中,通过对经典Hough变换与改进后的Hough变换的比较,可以看出检测速度有所提高,检测精度也达到了令人满意的程度.     

Hough transform for feature detection in panoramic images

Omni-directional sensors are useful in obtaining a 360° field-of-view. With a radially symmetric mirror and conventional lens system this can be achieved with a single camera. There are several proposed profiles for the mirror, but most violate the single viewpoint (SVP) criteria necessary to allow functional equivalence to the standard perspective projection, posing challenges that have not yet been addressed in the literature. Such a imaging system with a non-SVP optical system do not benefit from the affine quality of straight line features being represented as collinear points in the image plane. To utilize these non-SVP mirrors, a new method to recognize such features is required. This work describes an approach to detecting features in panoramic non-SVP images using a modified Hough transform. A mathematical model for this feature extraction process is given. Experimental results are presented to validate this model and show robust performance in identifying line features with only estimated calibration.     

用两步Hough变换检测圆

Hough变换在图像处理中占有重要地位,是一种检测曲线的有效方法。但使用传统的Hough变换来检测圆,具有存储空间大计算时间长的缺点。为此提出了采用两步Hough变换的圆检测方法,利用圆的斜率特性,降低了Hough参数空间的维度,提高了运算效率,并推广到椭圆的检测之中。     

Accurate and efficient curve detection in images: the importance sampling Hough transform

The Hough transform is a well known technique for detecting parametric curves in images. We place a particular group of Hough transforms, the probabilistic Hough transforms, in the framework of importance sampling. This framework suggests a way in which probabilistic Hough transforms can be improved: by specifying a target distribution and weighting the sampled parameters accordingly to make identification of curves easier. We investigate the use of clustering techniques to simultaneously identify multiple curves in the image. We also use probabilistic arguments to develop stopping conditions for the algorithm. Results from applying our method and two popular versions of the Hough transform to both simulated and real data are shown.     

Generalized Fuzzy Hough Transform for Detecting Arbitrary Shapes in a Vague and Noisy Image

A generalized Hough transform is an effective method for an arbitrary shape detection in a contour image. However, the conventional generalized Hough transform is not suitable for a noisy and blurred image. This paper describes a generalized fuzzy Hough transform which is derived by fuzzifying the vote process in the Hough transform. The present generalized fuzzy Hough transform enables a detection of an arbitrary shape in a very noisy, blurred, and even distorted image. The effectiveness of the present method has been confirmed by some preliminary experiments for artificially produced images and for actual digital images taken by an ordinary digital camera     

一种用Hough变换检测圆的快速算法

为了克服传统Hough变换检测圆时耗时巨大的缺陷,给出了一种新的基于Hough变换检测圆的快速算法.新算法与传统的方法相比具有以下特点:计算量少,提高了检测的速度;保留了传统Hough变换识别率高、抗噪性强、对不完整边缘具有鲁棒性等所有优点;不需要任何特殊的限定条件.实验表明,新的快速算法可以快速进行目标识别,在实时目标识别系统中具有良好的表现.     

Extended Hough transform for linear feature detection

Improving the accuracy of line segment detection reduces the complexity of subsequent high-level processing common in cartographic feature detection. We developed a new extension to the Hough transform and reported on its application to building extraction. We expanded the Hough space by a third parameter, the horizontal or vertical coordinate of the image space, to provide incremental information as to the length of the lineal feature being sought. Using this extended HT transform allowed us to more accurately detect the true length of a line segment. In addition, we used a Bayesian probabilistic approach to process our extended Hough space that further increased the accuracy of our extended Hough transform.     

利用Hough变换的匹配对提纯

目的 针对传统的匹配对提纯算法存在容错性差、效率低等问题,提出了一种利用Hough变换的匹配对提纯算法。方法 假设正确的匹配对一致性地服从一个变换模型。首先,为两幅图像的变换关系选择一个合适的数学模型,利用Hough变换确定模型方程参数的解。然后检验原始匹配对,保留符合模型方程的匹配对,从而达到提纯的目的。结果 与传统的RANSAC(random sample consensus)等算法相比,本文算法具有更高的容错率、召回率与更优的运行效率,且是稳定的。实验结果表明,在误配率低于85%时算法能完全剔除误匹配,且误配率高达95%时依然有50%的可能性成功剔除误匹配。结论 把Hough变换引入到匹配对提纯的应用中,该算法在所选模型准确或近似准确的情况下能鲁棒地提纯匹配对。由于模型方程参数个数决定参数空间维数,维数高导致投票及搜索最大值点的时间、空间复杂度大,因此该算法适用于模型参数较少(不大于4)的情况。     

Use of the Hough transform for image data compression

It is shown here that the Hough transform may be used for encoding of line curves and waveforms that consist of the concatenation of curves from an underlying set of families of curves. Several properties of the transform are given in this context.     

一种新的用于检测直线的快速Hough变换

在分析现有常用的改进Hough变换算法思想的基础上,设计了一种新的用于直线检测的快速Hough变换,通过与现有改进Hough变换算法的比较实验,新设计算法具有更好的实时性。     

基于Hough变换的车道检测改进算法研究

车道检测是车道侧偏检测的前提,是车辆辅助驾驶系统的重要组成部分,针对这一特点,提出了利用Hough变换来实现对车道检测的直道检测,而对于弯道部分的检测,则是该算法的一个创新之处.通过统计较小区域的目标像素数目,设置感兴趣区域(ROI)来判断车道的弯曲方向,并在该方向上进行弯道像素的搜索,采用多段折线的方法对弯道部分进行重建.实验结果表明该算法具有运算速度快、准确性高、鲁棒性较好的特点.     

基于弦中点Hough变换的同心圆检测方法

给出了一种基于Hough变换的同心圆检测方法,其核心思想是降低经典Hough变换圆检测中的Hough累积维数,将经典Hough变换圆检测时的三维参量统计,转变成基于弦中点的圆心和半径的参量统计。根据圆上所有点的内切圆必经过圆中心的事实,提取黑白边缘图像来建立二维数组累加器,依照像素点分布扫描边缘图像,累加器计数,计算出圆心位置,对每个圆心进行半径累积,确定同心圆半径集。与经典的Hough变换检测方法相比,该方法降低了计算复杂度。     

一种基于Hough变换的文档图像倾斜纠正方法

在对文本扫描输入的过程中,文本图像不可避免地会发生倾斜,倾斜校正将为图文分割、文字识别等后续处理工作创造良好的条件。提出了一种基于Hough变换的检测图像倾斜度的方法,为了克服Hough变换计算量大的缺点,该方法首先选取局部代表性子区域并提取其图像水平边缘,然后对提取的水平边缘进行两级Hough变换,从而实现了准确性与快速性的很好结合。     

采用Hough变换的道路边界检测算法

基于计算机视觉的路况信息识别是当前智能交通领域的热点课题,对道路边界的检测能够有效去除路况图像中与路况特征无关的大面积区域,是提高路况信息获取实时性的关键技术之一,提出一种适用于一般场景中直线道路和弯曲道路的边界检测算法.该算法采用直方图均衡和Otsu二值分割增强道路边界特征,使用均值滤波降噪和二值形态学方法获取特征边界,最终利用分空间的Hough变换方法实现道路边界检测.实验结果表明,该方法能够准确地检测道路边界线并进行道路区域分割,为后续的路况分类工作提供了有效保障.     

旧电影中基于Hough变换直线划痕的检测

为保护旧电影和国家档案影片,研究了旧电影自动修复与保护的方法.为了有效检测旧电影中常见的直线划痕,分析了直线划痕产生的原因、基本特征,检测方法.在分析Hough原理的基础上,提出了一种基于Hough变换的直线划痕检测方法.实验结果表明,该算法能有效地检测旧电影中的直线划痕,并可为旧电影中直线划痕的自动修复提供理论依据和实现基础.     

Parallel algorithms for plane detection using an adaptive Hough transform

Two variations of an adaptive Hough transform for plane detection have been implemented on a multipleinstruction-multiple data architecture based on transputers. In each case, process and processor allocation is data-dependent. Load balancing is achieved by a mixture of geometric decomposition and the processor farm paradigm. Evaluation of the algorithms on synthetic and real image data show their viability in terms of plane detection and increased speed of parallel computation.     

基于Hough变换的煤岩界面识别方案

根据煤层与岩石层分界明显且呈线状的煤岩图像特征,提出一种基于Hough变换的煤岩界面识别方案。首先对煤岩图像进行去噪与增强预处理,然后去除煤与岩石边缘的孤立点与虚假边缘,最后采用Hough变换识别煤岩分界线。实验结果表明,该方案能够较快、较准确地检测出煤岩分界线。     

A method for detection of circular arcs based on the Hough transform

The circular arc is a very useful feature for object detection and recognition in industrial environments. In this paper, a method for detection of circular arcs is described that is based on the Hough transform. The method estimates all five arc parameters and is robust in the presence of a moderate amount of noise. It has a computational and memory complexity ofO(n·m·R) andO(n·m) respectively, wheren andm are the sizes in thex andy directions andR is the maximum expected arc radius in pixels. Arcs as small as 45 degrees and radii down to 4 pixels can be detected. The computing time is almost independent of the number of circular arcs in the image.This work was supported by the Swedish Board for Technical Development, Grant No. 87-01954P.     

一种快速的随机Hough变换圆检测算法

随机Hough变换是检测圆的一种有效方法,但在处理复杂图像时随机采样带来的大量无效积累会导致计算量过大。提出一种快速的随机Hough变换圆检测算法,对证据积累的计算从三方面进行研究,有效地提高了计算速度,具有较好的应用价值。