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.     

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.     

Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform

Spatial information captured from optical remote sensors on board unmanned aerial vehicles (UAVs) has great potential in automatic surveillance of electrical infrastructure. For an automatic vision-based power line inspection system, detecting power lines from a cluttered background is one of the most important and challenging tasks. In this paper, a novel method is proposed, specifically for power line detection from aerial images. A pulse coupled neural filter is developed to remove background noise and generate an edge map prior to the Hough transform being employed to detect straight lines. An improved Hough transform is used by performing knowledge-based line clustering in Hough space to refine the detection results. The experiment on real image data captured from a UAV platform demonstrates that the proposed approach is effective for automatic power line detection.     

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.     

Generalizing the Hough transform to detect arbitrary shapes

The Hough transform is a method for detecting curves by exploiting the duality between points on a curve and parameters of that curve. The initial work showed how to detect both analytic curves^(1,2) and non-analytic curves,⁽³⁾ but these methods were restricted to binary edge images. This work was generalized to the detection of some analytic curves in grey level images, specifically lines,⁽⁴⁾ circles⁽⁵⁾ and parabolas.⁽⁶⁾ The line detection case is the best known of these and has been ingeniously exploited in several applications.^(7,8,9)We show how the boundaries of an arbitrary non-analytic shape can be used to construct a mapping between image space and Hough transform space. Such a mapping can be exploited to detect instances of that particular shape in an image. Furthermore, variations in the shape such as rotations, scale changes or figure ground reversals correspond to straightforward transformations of this mapping. However, the most remarkable property is that such mappings can be composed to build mappings for complex shapes from the mappings of simpler component shapes. This makes the generalized Hough transform a kind of universal transform which can be used to find arbitrarily complex shapes.     

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。     

Detection of doors using a genetic visual fuzzy system for mobile robots

Doors are common objects in indoor environments and their detection can be used in robotic tasks such as map-building, navigation and positioning. This work presents a new approach to door-detection in indoor environments using computer vision. Doors are found in gray-level images by detecting the borders of their architraves. A variation of the Hough Transform is used in order to extract the segments in the image after applying the Canny edge detector. Features like length, direction, or distance between segments are used by a fuzzy system to analyze whether the relationship between them reveals the existence of doors. The system has been designed to detect rectangular doors typical of many indoor environments by the use of expert knowledge. Besides, a tuning mechanism based on a genetic algorithm is proposed to improve the performance of the system according to the particularities of the environment in which it is going to be employed. A large database of images containing doors of our building, seen from different angles and distances, has been created to test the performance of the system before and after the tuning process. The system has shown the ability to detect rectangular doors under heavy perspective deformations and it is fast enough to be used for real-time applications in a mobile robot.     

Fuzzy cell Hough transform for curve detection

In this paper a new variation of Hough Transform is proposed. It can be used to detect shapes or contours in an image, with better accuracy, especially in noisy images. The parameter space of Hough Transform is split into fuzzy cells which are defined as fuzzy numbers. This fuzzy split provides the advantage to use the uncertainty of the contour point location which is increased when noisy images are used. By using fuzzy cells, each contour point in the spatial domain contributes in more than one fuzzy cell in the parameter space. The array that is created after the fuzzy voting process is smoother than in the crisp case and the effect of noise is reduced. The curves can now be detected with better accuracy. The computation time that is slightly increased by this method, can be minimized in comparison with classical Hough Transform, by using recursively the fuzzy voting process in a roughly split parameter space, to create a multiresolution fuzzily split parameter space.     

Detection of linear and planar structures in 3D subsurface images by iterative dimension reduction

A unified framework for detecting both linear and planar structures in three-dimensional (3D) images is developed. The method uses an iterative detection and removal strategy. The dimension reduction scheme reduces the search space for lines by first finding 2D planes and then searching for lines in the selected planes only. Thus the computational time of the method is lower than the 3D Hough Transform (HT) for lines. The proposed method is tested using experimental Ground Penetrating Radar (GPR) data taken over buried pipes, however the method is general enough to be applied to any situation where linear or planar structures need to be identified in 3D data.     

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。     

Compressive sensing of underground structures using GPR

Feature detection in sensing problems usually involves two processing stages. First, the raw data collected by a sensor, such as a Ground Penetrating Radar (GPR), is inverted to form an image of the subsurface area. Second, the image is searched for features like lines using an algorithm such as the Hough Transform (HT), which converts the problem of finding spatially spread patterns in the image space to detecting sparse peaks in the HT parameter space. This paper exploits the sparsity of features to combine the two stages into one direct processing step using Compressive Sensing (CS). The CS framework finds the HT parameters directly from the raw sensor measurements without having to construct an image of the sensed media. In addition to skipping the image formation step, CS processing can be done with a minimal number of raw sensor measurements, which decreases the data acquisition cost. The utility of this CS-based method is demonstrated for finding buried linear structures in both simulated and experimental GPR data.     

体育视频中场地自动检测方法

主要针对虚拟广告系统设计了简单而有效的算法来实现体育视频中场地检测,首先通过基于颜色空间直方图统计的方法实现了场地主区域的自动提取,再通过Top-Hat变换和改进的最大类间方差法实现了场地边缘检测,最后通过Hough直线检测和最小二乘拟合相结合的方法精确检测到所有的场地线。大量的实验表明,本文提出的场地检测方法对于羽毛球场地,网球场地,乒乓球场地等都有很好的检测效果,提取结果可用于摄像机定标和比赛场地的重建。     

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.     

文档图像中书写线的检测与去除

采用快速的Hough变换检测文档图像中的书写线,从图像中抽取少量特征点,将其分成两个子集,每次从两个子集中各取一个上点计算变换窨内的对应参数,当变换空间的累加值达到预先设定的阈值就认为已成功书写线,使Hough变换的速度大大加快,具有很强的实用价值,根据书写线与字符笔画的位置关系去除书写线,对书写线和相交的区域依据书写线的宽度和相交情形确定不同的结构元素,进行数学形态学的开运算,去除书写线的同时,较好地保持了字符笔画,实验结果表明,对信封图像上书写线的检测和去除有满意的处理效果。     

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。     

Randomised hough transform with error propagation for line and circle detection

In this paper, we introduce a new Randomised Hough Transform aimed at improving curve detection accuracy and robustness, as well as computational efficiency. Robustness and accuracy improvement is achieved by analytically propagating the errors with image pixels to the estimated curve parameters. The errors with the curve parameters are then used to determine the contribution of pixels to the accumulator array. The computational efficiency is achieved by mapping a set of points near certain selected seed points to the parameter space at a time. Statistically determined, the seed points are points that are most likely located on the curves and that produce the most accurate curve estimation. Further computational advantage is achieved by performing progressive detection. Examples of detection of lines using the proposed technique are given in the paper. The concept can be extended to non-linear curves such as circles and ellipses. ID="A1"Correspondance and offprint requests to: Q. Ji, Department of Electrical, Computer and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA. E-mail: qji@ecse.rpi.edu     

On the inverse Hough transform

In this paper, an inverse Hough transform algorithm is proposed. This algorithm reconstructs correctly the original image, using only the data of the Hough transform space and it is applicable to any binary image. As a first application, the inverse Hough transform algorithm is used for straight-line detection and filtering. The lines are detected not just as continuous straight lines, which is the case of the standard Hough transform, but as they really appear in the original image, i.e., pixel by pixel. To avoid the quantization effects in the Hough transform space, inversion conditions are defined, which are associated only with the dimensions of the images. Experimental results indicate that the inverse Hough transform algorithm is robust and accurate     

基于最大连通区域的QR码提取和校正方法

研究了基于最大连通区域的QR码提取和校正方法,使用区域面积描述标记最大连通区域并放大获得QR码大致所在区域,运用闭运算取其轮廓,用Hough变换检测轮廓线并计算交点信息,利用图像几何变换实现坐标值和像素值变换以消除几何失真。实验结果证明该方法能够快速定位的有效性。     

Determination of three-dimensional object location and orientationfrom range images

A technique for determining the distortion parameters (location and orientation) of general three-dimensional objects from a single range image view is introduced. The technique is based on an extension of the straight-line Hough transform to three-dimensional space. It is very efficient and robust, since the dimensionality of the feature space is low and since it uses range images directly (with no preprocessing such as segmentation and edge or gradient detection). Because the feature space separates the translation and rotation effects, a hierarchical algorithm to detect object rotation and translation is possible. The new Hough space can also be used as a feature space for discriminating among three-dimensional objects     

Point-to-line mappings as Hough transforms

In 1962 [US Patent 3069654], Hough used a linear point-to-line mapping (PTLM) to detect large sets of collinear points in an image, by mapping the points into concurrent lines and detecting peaks where many lines intersect. In 1972, Duda and Hart [Commun. ACM 15 (1972) 11] pointed out that Hough's method is not practical, because the peaks need not lie in a bounded region. They (and others after them) therefore developed methods of detecting sets of collinear points using nonlinear point-to-curve mappings that map collinear points into concurrent curves whose intersections do lie in a bounded range. In this paper we show that any PTLM that maps collinear points into concurrent lines must be linear, and that no such PTLM can map all the sets of collinear points in an image into peaks that lie in a bounded region; thus Duda and Hart's objection applies to any PTLM-based Hough transform.