基于Hough圆卷积变换的孔组检测

为降低孔组测量的成本,提高测量的精确度,提出一种基于Hough国卷积的孔组检测方法.对孔组图像进行中值滤波、拉普拉斯锐化增强和边缘检测;根据孔组半径大小的变化范围,对Hough圆锥进行改进;在此基础上,利用改进的Hough圆锥与边缘检测后的图像进行三维卷积,得到Hough参数空间,计算出孔组的坐标和半径值;采用快速傅立叶变换法代替三维卷积,减少算法的计算时间.实验结果表明,该方法的检测精度高、实时性强,可以用于孔组的自动化检测.     

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.     

A Bayesian approach to the Hough transform for line detection

This paper explains how to associate a rigorous probability value to the main straight line features extracted from a digital image. A Bayesian approach to the Hough Transform (HT) is considered. Under general conditions, it is shown that a probability measure is associated to each line extracted from the HT. The proposed method increments the HT accumulator in a probabilistic way: first calculating the uncertainty of each edge point in the image and then using a Bayesian probabilistic scheme for fusing the probability of each edge point and calculating the line feature probability.     

改进的随机Hough变换圆检测算法

针对随机Hough变换会产生大量无效累积的问题,提出了一种改进的随机Hough变换算法来检测圆,该算法利用梯度来预先判断随机采样的三个点是否在同一个圆上,从而大大减少了无效累积;另外,该算法还在圆参数的计算、阈值的确定、候选圆的确认等方面进行了改进.实验结果表明,该算法精度高,速度快,检测性能有了较大提高.     

A new algorithm for ball recognition using circle Hough transform and neural classifier

A large number of methods for circle detection have been studied in the last years for several image processing applications. The context application considered in this work is the soccer game. In the sequences of soccer images it is very important to identify the ball in order to verify the goal event. This domain is a challenging one as a great number of problems have to be faced, such as occlusions, shadows, objects similar to the ball, real-time processing and so on. In this work a visual framework trying to solve the above-stated problems, mainly considering real-time computational aspects, has been developed. The ball detection algorithm has to be very simple in terms of time processing and also has to be efficient in terms of false positive rate. Our framework consists of two sequential steps for solving the ball recognition problem: the first step uses a modified version of the directional circle Hough transform to detect the region of the image that is the best candidate to contain the ball; in the second step a neural classifier is applied on the selected region to confirm if the ball has been properly detected or a false positive has been found. Some tricks like background subtraction and ball tracking have been applied in order to maintain the search of the ball only in limited areas of the image. Different light conditions have been considered as they introduce strong modifications on the appearance of the ball in the image: when the image sequences are taken with natural light, as the light source is strictly directional, the ball, due to self-shades, appears as a spherical cap; this case has been taken in account and the search of the ball has been modified in order to manage this situation. A large number of experiments have been carried out showing that the proposed method obtains a high detection score.     

A real-time processor for the Hough transform

The Hough transform method for recognition can cope effectively with noisy backgrounds and gaps in boundaries. However, long computation time and large memory requirements have prevented it from being used in real-time applications. An architecture devised to solve those problems, with a focus on detecting straight lines, is presented. Examples are given of the use of an experimental hardware model for automatic inspection and measurement of objects in factories and laboratories. It took less than one second from picture input to straight line parameter reproduction for these examples     

A new convolution approach for the time-delay identification of systems with arbitrary entries

In this paper, an online delay identification approach of continuous-time linear systems with unstructured entries is achieved via a new algebraic technique. The arbitrary input and output trajectories are chosen with close and abundant crossing zero. Initial conditions and static disturbances are taken into account in the design of the identification approach. The proposed method is based on a distributional algebraic technique and a convolution approach. A proposed theorem is hence enounced to identify a single time-delay of such systems. The effectiveness of the proposed approach is demonstrated by an illustrative example. The obtained results show the high performances of the proposed time-delay identification approach in severe operation conditions of the considered system.     

Hough transform from the radon transform

An appropriate special case of a transform developed by J. Radon in 1917 is shown to have the major properties of the Hough transform which is useful for finding line segments in digital pictures. Such an observation may be useful in further efforts to generalize the Hough transform. Techniques for applying the Radon transform to lines and pixels are developed through examples, and the appropriate generalization to arbitrary curves is discussed.     

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     

The Hough transform versus the UpWrite

This paper compares the Hough transform and the UpWrite for the detection of lines, circles, and ellipses. Both ideal and noisy images are tested. The UpWrite is found to be more robust for images containing perturbation noise. For ideal images and images with speckle noise, the results are found to depend on the complexity of the object being detected, with more complex objects favoring the UpWrite     

并行Hough变换快速航迹起始

Hough变换在航迹起始领域得到广泛应用,但在扫描次数较少时起始效果不佳。通过转变Hough变换处理结构和改变计数器累加方式,提出了一种并行Hough变换快速航迹起始算法。利用Hough变换将不同时刻的量测集合分别映射到参数空间,继而将空间中具有相同索引的各次累加结果构成累加向量,统计其非零元素的个数,如大于预先设定的门限,则用向量各元素求和作为累加结果,否则置零。将利用该方法获得最终的累加结果进行门限检测来确定是否起始航迹。仿真实验表明,该算法可在密集环境下快速准确地起始航迹。     

Discretization errors in the Hough transform

Straight line-segments in a two-dimensional image can be detected with the Hough transform by searching peaks in a parameter space. The influence on the Hough transform of the quantization of the parameter space, the quantization of the image and the width of the line-segment is investigated in this paper.

The Hough transform was improved by O'Gorman and Clowes by taking into account the gradient direction. The resulting scatter of the peaks can be reduced by using a weighting function in the transform. Examples of asbestos preparations are given.     

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.     

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.     

基于Hough变换的植物叶脉检测新方法

针对植物叶脉的特点,提出了利用灰度拉伸、Hough变换与边缘生长、图像腐蚀与膨胀进行植物叶脉检测的新方法。在该方法中,Hough变换检测植物叶脉图像的同时也较好的消除了图像噪声,该方法应用到植物叶脉检测中效果较好。     

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.     

A method for calculating the tangent on the basis of the Hough transform

A method for calculating the tangent direction for a digital curve on the basis of the Hough transform is proposed. The initial data for the Hough transform are taken from tables of angular intervals obtained from a catalog of digital curves. Such a catalog is constructed for a chosen size of the local window on the basis of a specially developed indexation of curves. When determining the intervals of tangent directions for the catalog of curves, several methods of parameterization are used: straight line, elliptic or sinusoidal arc, and superposition of harmonics. The curve selection criterion is the maximum curvature of the corresponding smooth parametric curves. This makes it possible to adapt the method to a class of initial curves. Comparison with earlier-developed methods has demonstrated a significant increase in accuracy, especially with an increase in the curvature.     

一种基于Hough变换求解无人机滚转角的方法

提出了一种在无人机进场着陆段中根据机器视觉所得的数字图像获取无人机滚转角的方法。首先对摄像机所获得图像进行一系列预处理(中值滤波、边缘检测等),然后利用Hough变换获取图像中的直线,并根据其他的约束条件从这些直线中获得地平线,最后通过最小二乘法获得无人机滚转角。     

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.