图像中网格直线的检测方法的研究

首先分析了图像边缘特性以及Laplacian算子检测图像边缘的基本原理,提出了一种新的边缘检测算法,能准确地检测出图像中的目标边缘;在确定直线参数时,先使用Hough变换检测第一条最为明显的直线,然后去掉该直线以及附近的点,然后再次对图像进行Hough变换,并重复此过程,直到找到所有的直线;使用此改进后的Hough变化能够准确地检测到图像中构成网格的直线的参数.     

Detecting line segments in an image – a new implementation for Hough Transform

The conventional Hough Transform is a technique for detecting line segments in an image. The conventional Hough Transform transforms image points into lines in the parameter space. If there are collinear image points, the lines transformed from the points intersect at a point in the parameter space. Determining the intersection is generally carried out through the “voting method”, which partitions the parameter space into squared meshes. A problem with the voting method involves determining the resolution required for partitioning the parameter space. In this paper, we present a solution to this problem. We propose to transform an image point into a belt, whose width is a function of the width of a line in the image. We then determine the intersection of numerous belts to detect a line segment. An iterated algorithm based the transformation for detecting line segments is presented in this paper.     

The adaptive hough transform

We introduce the Adaptive Hough Transform, AHT, as an efficient way of implementing the Hough Transform, HT, method for the detection of 2-D shapes. The AHT uses a small accumulator array and the idea of a flexible iterative "coarse to fine" accumulation and search strategy to identify significant peaks in the Hough parameter spaces. The method is substantially superior to the standard HT implementation in both storage and computational requirements. In this correspondence we illustrate the ideas of the AHT by tackling the problem of identifying linear and circular segments in images by searching for clusters of evidence in 2-D parameter spaces. We show that the method is robust to the addition of extraneous noise and can be used to analyze complex images containing more than one shape.     

Real-time precise detection of regular grids and matrix codes

The traditional approach in detecting sets of concurrent and/or parallel lines is to first detect lines in the image and then find such groups of them which meet the concurrence condition. The Hough Transform can be used for detecting the lines and variants of HT such as the Cascaded Hough Transform can be used to detect the vanishing points. However, these approaches disregard much of the information actually accumulated to the Hough space. This article proposes using the Hough space as a 2D signal instead of just detecting the local maxima and processing them. On the example of QRcode detection, it is shown that this approach is computationally cheap, robust, and accurate. The proposed algorithm can be used for efficient and accurate detection and localization of matrix codes (QRcode, Aztec, DataMatrix, etc.) and chessboard-like calibration patterns.     

基于分级的快速霍夫变换直线检测

在分析标准霍夫变换、两点表决霍夫变换以及多级霍夫变换直线检测的基础上，针对算法的不足，结合他们的优点，设计并实现了基于分级的快速霍夫变换直线检测方法，对算法进行了详细描述和分析，并通过实验证明了算法的有效性，实验表明：所设计的直线检测运算速度快，检测精度高，鲁棒性强，有一定的应用价值。     

A convolution approach to the circle Hough transform for arbitrary radius

The Hough transform is a well-established family of algorithms for locating and describing geometric figures in an image. However, the computational complexity of the algorithm used to calculate the transform is high when used to target complex objects. As a result, the use of the Hough transform to find objects more complex than lines is uncommon in real-time applications. We describe a convolution method for calculating the Hough transform for finding circles of arbitrary radius. The algorithm operates by performing a three-dimensional convolution of the input image with an appropriate Hough kernel. The use of the fast Fourier transform to calculate the convolution results in a Hough transform algorithm with reduced computational complexity and thus increased speed. Edge detection and other convolution-based image processing operations can be incorporated as part of the transform, which removes the need to perform them with a separate pre-processing or post-processing step. As the Discrete Fourier Transform implements circular convolution rather than linear convolution, consideration must be given to padding the input image before forming the Hough transform.     

结合LTS和Hough变换的直线检测算法

为提高Hough变换检测直线的精度,提出一种结合Hough变换与截断最小二乘法的直线检测算法。利用Hough变换确定图像中直线所在的大致区域,提取候选区域内的特征点集,采用截断最小二乘法拟合得到精确的直线参数。实验结果表明,该算法的检测率和检测精度较高,对Hough变换的分辨率要求较低,整体空间开销较小。     

一种在图象中检测网格直线的新方法

设计了一个扩展Robert算子,该算子能够在有噪声的情形下对图象中各种宽度的线条进行检测。当使用Hough变换确定直线的参数时,首先只用Hough变换检测第一条最为显著的直线,随后去掉该条直线及其附近的点,然后再次对图象进行Hough变换,并重复此过程,直到找到所有直线或者Hough变换后参数平面上的值都小于某个阈值为止。使用此改进后的Hough变换能够准确地检测到图象中构成网格的直线的参数。给出了具体的检测例子。     

快速霍夫变换算法

二值图像的直线检测过程中,标准霍夫变换算法的计算量为O（N^3)。该文提出一种快速霍夫变换算法,其计算量仅为O（N^2log2N)。该快速算法可以并行实现;处理器阵列规模为O（N^2)时,计算量为O（log2N)。文中还分析得到快速算法的误差上界,并提出一种改进的快速霍夫变换算法以获得更高的计算精度。最后,给出算法的数值算例。理论分析及数值算例都表明,该文的快速霍夫变换算法在直线检测过程中有着更高的计算效率,并且具有良好的计算精度。     

一种新的直线提取算法研究

研究了一种快速的图像直线提取算法。将边缘按方向划分成若干子边缘,利用判据从子边缘提取出局部直线,然后依据准则合并相应子边缘得到全局直线及参数。准确提取出图像中的直线特征及端点和方向等参数。实验结果表明,算法比Hough变换更准确、快速、可靠地提取图像的直线特征。     

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。     

基于霍夫变换的图像运动模糊角度识别法的改进

运动模糊图像的复原主要依赖于对运动模糊参数的估计。针对R.Lokhande等人提出的基于霍夫变换的运动模糊角度估计法进行改进,通过增加预处理步骤,并利用霍夫变换得到更多合适直线,然后取加权均值得到最后估计角度值。实验表明,改进方法能得到比原方法更精确的角度值,并且更加稳定。     

基于运动约束二步聚类Hough变换航迹起始算法

针对基于减法聚类的Hough变换航迹起始法存在的虚假航迹起始率高、目标航迹点缺失严重的问题,提出了一种基于运动二步约束聚类的Hough变换航迹起始法;该算法首先采用直观法滤除部分杂波点;随后利用Hough变换进行低阈值筛选;然后利用减法聚类得到多个聚类中心;最后由最近邻法判断出每个样本点所归属于聚类中心,得到目标航迹的数目以及参数;仿真表明,在密集杂波环境下,文章提出的算法的航迹起始概率近似是多级Hough变化航迹起始算法的1.2倍,同时虚假航迹起始概率是多级Hough变化航迹起始算法的三分之一,结果表明该算法改善了航迹起始的性能,特别适合于密集杂波下低信噪比目标的目标检测问题。     

基于蚁群相似度加权霍夫变换的航迹起始

在雷达、红外等传感器的目标航迹起始时,目标的机动性以及数据的批处理会导致目标运动轨迹上偏离点的漏检。针对该问题,首先在Hough变换基础上,根据目标运动速度、幅度等信息置信区间与杂波干扰的不同,加上权重因子进行累积,然后进行目标的航迹起始,最后利用蚁群相似度检测起始轨迹上的偏离点迹。仿真结果表明,较之传统Hough变换,采用加权Hough变换算法能有效检测到目标航迹上的偏离点迹,提高目标检测概率。     

HT-LSM的欠定混合盲信号分离方法的研究

利用Hough变换可以检测观测空间中的直线方向从而确定混叠矩阵的方法,提出了欠定盲源分离中估计混叠矩阵的一种新算法——HT-LSM算法。该算法在介绍欠定盲信号分离基本原理的基础上,介绍基于Hough变换的盲信道估计算法,并将改进后的Hough变换与最小二乘法相结合,在不影响检测结果速度的同时又进一步提高了检测精度,应用到欠定语音信号分离中,取得了良好的实验效果。     

Image Based NC Programming of Plane Contour Machining

Simplified Hough Transform (HT) and Adaptive Including Box (AIB) approach are proposed. The size of AIB is controlled according to the precision requirement of machining. The image data of part contour is turned into a set of line segments and then NC program for machining the contour is generated .     

基于像素组合的快速哈夫变换

直线参数检测是图像分析和计算机视觉中一个基本问题,哈夫变换能很好地解决这个问题,然而哈夫变换的巨大计算量使它难于实时应用。因为一个图像点对应参数空间的一条曲线,图像点与量化后的参数区间是一对多的关系,造成了标准哈夫变换的计算复杂性。而两点确定一条直线,两个图像点对应一个参数点,利用这个原理能大幅度减少哈夫变换的计算量。     

基于最小二乘修正的随机Hough变换直线检测

利用Hough变换进行直线检测时,由于直线在参数空间中的映射容易受到邻近目标、噪声以及本身非理想状态的干扰,算法中的投票过程较易出现无效累积,进而导致虚检、漏检及端点定位不准等问题.针对传统方法的上述缺陷,提出了一种基于 ρ-θ 域最小二乘拟合修正的随机Hough变换的直线检测方法.首先, 在随机抽样时利用像素-长度比值对抽样的有效性进行判定,剔除不在直线上的抽样点对;然后, 对邻域相关点进行 ρ-θ 域的最小二乘拟合,得到修正后的直线参数用于累加投票,投票过程中设定累加阈值,通过检测峰值点逐次检出疑似长直线;最后, 通过设定断裂阈值对每条长直线进行筛选和分段,定位出直线段的端点.仿真实验表明,所提方法在投票时有效抑制了复杂环境对局部最大值的干扰,使直线检测的准确率得到显著提升.     

An extension to the randomized hough transform exploiting connectivity

Finding global curve segments in an image is an important task. For such a task, a new branch of Hough Transform algorithms, called probabilistic Hough Transforms, has been actively developed in recent years. One of the first was a new and efficient probabilistic version of the Hough Transform for curve detection, the Randomized Hough Transform (RHT). In this paper, a novel extension of the RHT, called the Connective Randomized Hough Transform (CRHT), is suggested to improve the RHT for line detection in complex and noisy pictures. The CRHT method combines the ability of the Hough Transform for global feature extraction with curve fitting techniques by exploiting the connectivity of local edge image points. Tests demonstrate the high speed and low memory usage of the CRHT, as compared both to the Standard Hough Transform and the basic RHT.