局部PCA参数约束的Hough多椭圆分层检测算法

针对随机Hough变换(RHT)在复杂图像中检测圆及椭圆时随机采样所造成的大量无效采样、无效累积以及运算时间长等问题,提出基于局部PCA感兴趣参数约束Hough多椭圆分层检测思路。首先利用边缘检测算子获得边缘信息并去除边缘交叉点,在边缘图像中标记并提取出满足一定长度的连续曲线段;其次利用线段PCA方向分析确定是否属于有效曲线段;然后,对所有感兴趣曲线段按照标记顺序依次利用椭圆拟合办法初步得到感兴趣椭圆粗略参数,根据拟合结果进而模糊约束Hough变换参数搜索范围,得到精确椭圆参数;最后利用检测结果更新图像空间,删除已经检测到的椭圆,依次进行,直到所有椭圆检测完毕。实验结果表明,该算法在计算、存储消耗上均大大减少。     

基于随机Hough变换的零基线正弦曲线检测

研究零基线正弦曲线的随机Hough变换的最小点集、收敛映射和动态链接表结构3个基本问题,提出改进的三点拟合零基线正弦曲线的方法,给出零基线正弦曲线的随机Hough变换检测算法,并分析算法的计算性能和存储量性能。仿真实验表明该方法的有效性。     

基于随机Hough变换的深度图像分割

提出了基于随机Hough变换的深度图像分割算法,该算法采用随机Hough变换在深度图像中寻找平面,具有对噪声不敏感的优点．通过对一常用深度图像数据库(ABW图像库)的分割实验,并将实验结果同4种经典的深度图像分割算法在同一数据库中的分割结果作了比较分析,表明该算法对噪声不敏感,分割性能优于4种经典算法。     

基于随机Hough变换的人头检测

传统的人头检测方法多为基于人脸和头发的检测,误差较大。为此,提出一种基于随机Hough变换(RHT)的人头检测方法。根据人头部轮廓近圆的特征,采用Canny算子提取图像边缘,得到目标轮廓。利用RHT算法对独立的曲线进行圆检测,并对人头进行标识。实验结果表明,与现有方法相比,该方法的识别率较高、速度较快、适用范围较广。     

基于模糊随机广义霍夫变换的目标匹配

为了提高工业检测中图像匹配精度和速度,提出了一种用于二维目标匹配的新算法--模糊随机广义霍夫变换(FRGHT)。此算法结合了模糊推理系统(FIS)和随机广义霍夫变换(RGHT)。模糊推理系统引入模糊集合概念,计算待配准图像中边缘点对配准参数的投票,从而可以抑制噪声,解决扭曲问题,提高了匹配精度;随机抽取待配准图像中边缘点进行投票,实现了多对一的映射,从而减少了内存需求,提高计算速度。实验表明,该方法计算速度快,匹配精度高,不受噪声污染、扭曲、遮挡、混乱等情况的影响。     

Randomized Hough Transform (RHT): Basic Mechanisms,Algorithms, and Computational Complexities

Recently, a new curve detection approach called the randomized Hough transform (RHT) was heuristically proposed by the authors, inspired by the efforts of using neural computation learning techniques for curve detection. The preliminary experimental results and some qualitative analysis showed that in comparison with the Hough transform (HT) and its variants, the RHT has advantages of fast speed, small storage, infinite range of the parameter space, and high parameter resolution, and it can overcome several difficulties encountered with the HT methods. In this paper, the basic ideas of RHT are further developed into a more systematic and theoretically supported new method for curve detection. The fundamental framework and the main components of this method are elaborated. The advantages of RHT are further confirmed. The basic mechanisms behind these advantages are exposed by both theoretical analysis and detailed experimental demonstrations. The main differences between RHT and some related techniques are elucidated. This paper also proposes several improved algorithms for implementing RHT for curve detection problems in noisy images. They are tested by experiments on images with various kinds of strong noise. The results show that the advantages of RHT are quite robust. Moreover, the implementations of these algorithms are modeled by a generalized Bernoulli process, allowing probability analysis on these algorithms to estimate their computational complexities and to decide some important parameters for their implementations. It is shown quantitatively that the complexities are considerably smaller than those of the HT.     

基于随机森林的目标检测与定位

为解决复杂图像中的目标检测与定位问题,提出一种基于随机森林的目标检测与定位算法。采用SIFT局部特征构造随机森林分类器,以一个决策树中的全部叶子节点构成一个树型结构的判别式码本模型,从而获得更可靠的概率Hough投票,加快目标检测速度。实验结果证明,该算法效率较高,可用于复杂场景下的目标检测与定位。     

Fast and Robust Normal Estimation for Point Clouds with Sharp Features

This paper presents a new method for estimating normals on unorganized point clouds that preserves sharp features. It is based on a robust version of the Randomized Hough Transform (RHT). We consider the filled Hough transform accumulator as an image of the discrete probability distribution of possible normals. The normals we estimate corresponds to the maximum of this distribution. We use a fixed‐size accumulator for speed, statistical exploration bounds for robustness, and randomized accumulators to prevent discretization effects. We also propose various sampling strategies to deal with anisotropy, as produced by laser scans due to differences of incidence. Our experiments show that our approach offers an ideal compromise between precision, speed, and robustness: it is at least as precise and noise‐resistant as state‐of‐the‐art methods that preserve sharp features, while being almost an order of magnitude faster. Besides, it can handle anisotropy with minor speed and precision losses.     

一种提取直线的随机方法

基于Hough变换提取直线的方法，由于要预先量化参数空间，因此需要很大的存储量和计算量．基于RHT(Randomized Hough Transform)提取直线的方法是通过随机选取两个点得到直线的参数，而后在参数空间对相应的参数进行累加、判断，该方法虽然无需预先量化参数空间，但是其在直线检测时，收敛速度慢．为此提出一种新的随机检测直线(Random Line Detection)的方法，在图象边缘点构成的数据空间中随机选取3个点，根据距离准则获得一条可能的直线，然后在数据空间中进一步判断直线的真实性，实验证实了该方法能有效的减少存储空间并降低计算量。     

Old and new straight-line detectors: Description and comparison

Straight-line detection is important in several fields such as robotics, remote sensing, and imagery. The objective of this paper is to present several methods, old and new, used for straight-line detection. We begin by reviewing the standard Hough transform (SHT), then three new methods are suggested: the revisited Hough transform (RHT), the parallel-axis transform (PAT), and the circle transform (CT). These transforms utilize a point-line duality to detect straight lines in an image. The RHT and the PAT should be faster than the SHT and the CT because they use line segments whereas the SHT uses sinusoids and CT uses circles. Moreover, the PAT, RHT, and CT use additions and multiplications whereas the SHT uses trigonometric functions (sine and cosine) for calculation. To compare the methods we analyze the distribution of the frequencies in the accumulators and observe the effect on the detection of false local maxima. We also compare the robustness to noise of the four transforms. Finally, an example with a real image is given.     

用于形状检测的极标编码多分辨率Hough变换

本文提出一种利用Hough变换作形状检测的方法,称为极标编码多分辨率Hough变 换,它将模板和图象都用极坐标表示为一维序列,这不但简化了Hough变换的映射运算,而 且借此可以构成图象空间和参数空间对等的多分辨率描述,使检测可由低分辨率向高分辨率 以一种类似树搜索的方式高效地实现.文中给出了实验结果.     

A new and efficient transform for curve detection

A new transform for curve detection, called the Curve-Fitting Hough Transform (CFHT), is proposed. In the conventional Hough Transform (HT) and its variants, both storage and computation grow exponentially with the number of parameters. The CFHT is advantageous over the conventional HT and variants in its high speed, small storage, arbitrary parameter range, and high parameter resolution. This is achieved by fitting a segment of the curve to be detected to a small neighborhood of edge points. If the fitting error is less than a given threshold, the parameters obtained from curve fitting are used to map an edge element to a single point in the parameter space. A multidimensional ordered parameter list is used to accumulate the occurrences of the curve to be detected.     

Detection of incomplete ellipse in images with strong noise by iterative randomized Hough transform (IRHT)

An iterative randomized Hough transform (IRHT) is developed for detection of incomplete ellipses in images with strong noise. The IRHT iteratively applies the randomized Hough transform (RHT) to a region of interest in the image space. The region of interest is determined from the latest estimation of ellipse parameters. The IRHT “zooms in” on the target curve by iterative parameter adjustments and reciprocating use of the image and parameter spaces. During the iteration process, noise pixels are gradually excluded from the region of interest, and the estimation becomes progressively close to the target. The IRHT retains the advantages of RHT of high parameter resolution, computational simplicity and small storage while overcoming the noise susceptibility of RHT. Indivisible, multiple instances of ellipse can be sequentially detected. The IRHT was first tested for ellipse detection with synthesized images. It was then applied to fetal head detection in medical ultrasound images. The results demonstrate that the IRHT is a robust and efficient ellipse detection method for real-world applications.     

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。     

Randomized or probabilistic Hough transform: unified performance evaluation

Rapid computation of the Hough transform is necessary in very many computer vision applications. One of the major approaches for fast Hough transform computation is based on the use of a small random sample of the data set rather than the full set. Two different algorithms within this family are the randomized Hough transform (RHT) and the probabilistic Hough transform (PHT). There have been contradictory views on the relative merits and drawbacks of the RHT and the PHT. In this paper, a unified theoretical framework for analyzing the RHT and the PHT is established. The performance of the two algorithms is characterized both theoretically and experimentally. Clear guidelines for selecting the algorithm that is most suitable for a given application are provided. We show that, when considering the basic algorithms, the RHT is better suited for the analysis of high quality low noise edge images, while for the analysis of noisy low quality images the PHT should be selected.     

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.     

A New Definition of the Hough Transform

This paper‘s main contributions are three-fold.Firstly,it is shown that the two existing template matching-like definitions of the Hough transform in the literature are inadequate.Secondly,an inherent probabilistic aspect of the Hough transform embedded in the transformation process from image space to parameter space is clarified.Thirdly,a new definition of the Hough transform is proposed which takes into account both the intersection scheme between the mapping curve(or mapping surface) and accumulator cells and the inherent probabilistic characteristics.     

An inherent probabilistic aspect of the Hough transform

In this paper,a new property of the Hough transform is discovered,namely an inherent probabilistic aspect which is independent of the input image and embedded in the transformation process from the image space to the parameter space.It is shown that such a probabilistic aspect has a wide range of implications concerning the specification of implementation schemes and the performance of Hough transform.In particular,it is shown that in order to make the Hough transform really meaningful,an appropriate curve(surface)density function must be,either explicitly or implicitly,supplied during its implementation process,and that the widely used approach to uniformly discretizing parameter space in the literature is generally inadequate.     

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.     

A neural implementation of the Hough transform and the advantages of explaining away

The Hough transform (HT) is widely used for feature extraction and object detection. However, during the HT individual image elements vote for many possible parameter values. This results in a dense accumulator array and problems identifying the parameter values that correspond to image features. This article proposes a new method for implementing the voting process in the HT. This method employs a competitive neural network algorithm to perform a form of probabilistic inference known as “explaining away”. This results in a sparse accumulator array in which the parameter values of image features can be more accurately identified. The proposed method is initially demonstrated using the simple, prototypical, task of straight line detection in synthetic images. In this task it is shown to more accurately identify straight lines, and the parameter of those lines, compared to the standard Hough voting process. The proposed method is further assessed using a version of the implicit shape model (ISM) algorithm applied to car detection in natural images. In this application it is shown to more accurately identify cars, compared to using the standard Hough voting process in the same algorithm, and compared to the original ISM algorithm.