一种基于随机Hough变换的椭圆检测算法研究

提出了一种有效的基于随机Hough变换的椭圆检测算法(RED).该算法首先在图像中随机选择三个边缘点,并分别以这三个点为中心选取相同大小的窗口.利用最小二乘法对这三个窗口中的所有边缘点进行椭圆拟合,然后在图像中随机选取第四个边缘点,以判断图像中是否存在一个可能的椭圆.在找到一个可能的椭圆后,通过证据收集以进一步验证这个可能的椭圆是否真实存在.仿真实验和实际图像的实验表明本文算法比其它的算法具有速度快和精度高的优点.     

改进的随机Hough变换的头部区域检测算法

介绍了一种基于随机Hough变换(RHT)的圆检测的改进算法。该算法利用梯度方向信息来确定采样的3点是否进行累积,并在获得真实圆之后采取部分删减的策略来更新边缘点集。此方法不仅解决了传统RHT中由于随机采样而造成的大量无效累积问题,并且有效改善了传统RHT方法中与梯度方向Hough变换(GHT)算法相比最优拟合结果丢失的情况。该方法在降噪后的图像上做了实验,最优拟合结果出现概率明显提高,检测性能有了显著的改善。     

一种基于随机Hough变换圆检测的改进算法

介绍了一种基于随机Hough变换(RHT)的圆检测的改进算法。该算法利用梯度方向信息来确定采样的三点是否进行累积,然后再利用确定候选圆范围的方法来缩小所要搜索的像素点的范围。此方法较好地解决了传统RHT中由于随机采样而造成的大量无效累积问题,并且改进后的算法使运行速度得到进一步的提高,检测性能也有较大的改善。该算法分别在加噪和不加噪的人工图像上做了实验,检测性能和处理速度方面都比传统的RHT有明显的改善和提高。     

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。     

改进随机Hough变换在虹膜定位中的应用

针对随机Hough变换(RHT)中随机采样造成的大量无效采样和无效积累问题，提出了基于连续曲线段检测的RHT(CRHT)算法并应用于虹膜定位中，它利用了边缘图像的连续特性来减少无效采样的数量。实验结果证明，改进的CRHT变换提高了虹膜定位速度。     

一种基于随机Hough变换圆检测的改进算法

介绍了一种基于随机Hough变换(RHT)的圆检测的改进算法.该算法利用梯度方向信息来确定采样的三点是否进行累积,然后再利用确定候选圆范围的方法来缩小所要搜索的像素点的范围.此方法较好地解决了传统RHT中由于随机采样而造成的大量无效累积问题,并且改进后的算法使运行速度得到进一步的提高,检测性能也有较大的改善.该算法分别在加噪和不加噪的人工图像上做了实验,检测性能和处理速度方面都比传统的RHT有明显的改善和提高.     

基于梯度的随机Hough快速圆检测方法*

针对随机Hough变换(RHT)在复杂图像中检测圆时产生随机采样的大量无效累积,提出了一种改进的RHT用于圆检测,方法利用梯度方向信息来判定是否对采样到的三点进行参数累积,从而较好地解决了无效累积问题。实验表明改进后的算法比原算法计算速度快,占用的内存小,检测性能有较大提高。     

基于中心提取的RHT在椭圆检测中的应用

充分利用椭圆的几何性质,借助椭圆的形状控制点约束和弦端点法向约束,大幅降低随机Hough变换(RHT)的无效采样和累积次数,并采用基于视觉感知聚类的模糊置信度对由同一个形变椭圆引入的多个虚假候选椭圆进行有效去除.实验结果表明:该算法与基于RHT的其他椭圆检测方法相比,具有检测速度快、精度高、抵抗椭圆的部分缺失和形变能力强等优点.     

噪声环境下基于不确定性度量的随机HT直线检测

含噪图像中直线的自动检测是机器视觉和图像处理中的热点问题之一。基于Hough变换的直线检测算法中采用硬投票方案,在噪声环境下检测精度下降且占用内存大。为了提高检测算法的抗噪性和降低算法的计算复杂性,提出了一种新的将边缘点不确定性度量和随机Hough变换相结合的直线检测算法。该算法在所建立的点属于某直线上不确定性度量概率模型基础上,根据随机选择的两点间直线参数,按照Bayesian法则用基于不确定度量的参数空间软投票提高了检测算法的抗噪能力。实验结果表明,算法在较高的噪声（方差大于0.03）时,检测误差小于1‰,检测时间是单纯不确定度量直线检测方法的1/2,比传统Hough变换算法快10-15倍。     

快速随机Hough变换多圆检测算法

随机Hough变换是检测圆的一种有效方法,但在处理多圆复杂图像时随机采样带来的大量无效累积会导致计算量过大。文中提出一种基于随机Hough变换的快速多圆检测算法,除去三类噪声点,通过随机采样到的一点按照一定规则搜索另外两点来确定候选圆,用原始图像对候选圆进行证据积累以判断是否为真圆。理论分析和实验结果表明:该算法较其他算法能更快地检测出图像中的多个圆,具有较好的应用价值。     

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.     

A multi-population genetic algorithm for robust and fast ellipse detection

This paper discusses a novel and effective technique for extracting multiple ellipses from an image, using a genetic algorithm with multiple populations (MPGA). MPGA evolves a number of subpopulations in parallel, each of which is clustered around an actual or perceived ellipse in the target image. The technique uses both evolution and clustering to direct the search for ellipses—full or partial. MPGA is explained in detail, and compared with both the widely used randomized Hough transform (RHT) and the sharing genetic algorithm (SGA). In thorough and fair experimental tests, using both synthetic and real-world images, MPGA exhibits solid advantages over RHT and SGA in terms of accuracy of recognition—even in the presence of noise or/and multiple imperfect ellipses in an image—and speed of computation.     

Multisets mixture learning-based ellipse detection

We develop an ellipse detection algorithm based on the multisets mixture learning (MML) that differs from the conventional Hough transform perspective. The algorithm developed has potential advantages in terms of noise resistance, incomplete ellipse detection, and detecting a multitude of ellipses.     

Adaptive fuzzy c-shells clustering and detection ofellipses

Several generalizations of the fuzzy c-shells (FCS) algorithm are presented for characterizing and detecting clusters that are hyperellipsoidal shells. An earlier generalization, the adaptive fuzzy c-shells (AFCS) algorithm, is examined in detail and is found to have global convergence problems when the shapes to be detected are partial. New formulations are considered wherein the norm inducing matrix in the distance metric is unconstrained in contrast to the AFCS algorithm. The resulting algorithm, called the AFCS-U algorithm, performs better for partial shapes. Another formulation based on the second-order quadrics equation is considered. These algorithms can detect ellipses and circles in 2D data. They are compared with the Hough transform (HT)-based methods for ellipse detection. Existing HT-based methods for ellipse detection are evaluated, and a multistage method incorporating the good features of all the methods is used for comparison. Numerical examples of real image data show that the AFCS algorithm requires less memory than the HT-based methods, and it is at least an order of magnitude faster than the HT approach.     

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.     

基于边界曲线弧分割的多椭圆检测

不同于传统的Hough变换算法,提出一种基于边界弧分割的椭圆检测方法。首先将边界从交点处分割成弧段,将得到弧段划分为长弧和短弧两组并按长度降序排序,然后从两组中找出属于某个椭圆的若干弧段,利用最小二乘法拟合得到候选椭圆并验证是否为真正椭圆。实验表明该算法能快速检测出图中椭圆,运行时间远小于采用随机Hough变换算法,在具有噪声、椭圆残缺的情况下仍能有较好的检测结果。     

基于中心估计的椭圆检测

椭圆检测在图像理解中有重要的作用。为克服标准Hough变换对时空需求高的缺点,设计了一种改进算法。通过椭圆对称特性估计图像中可能存在的椭圆中心,利用长轴确定椭圆中心及夹角参数只需一维累积数组对椭圆短轴的投票,采用聚类分析技术将检测到的虚椭圆归类到对应的真实椭圆。对合成图像和实际图像的实验表明算法的正确和高效。