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在常规圆检测算法中,Hough变换、随机Hough变换以及随机圆检测算法的检测效率低,导致难以适用于复杂场景或者对检测速度有较高要求的情况。为了提高圆检测的效率,本文从采样点的选取、候选圆的确定以及真圆的确认3个阶段进行分析,结合这3个阶段的优化方法,提出一种结合多阶段优化的圆检测算法。人工图像和实际图像的实验结果表明:该算法较其他算法有效地提高了圆检测的速度,并且具有较好的检测鲁棒性和检测精度。 相似文献
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改进的随机Hough变换圆检测算法 总被引:2,自引:0,他引:2
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. 相似文献
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一种新的对随机Hough变换改进的检测圆的方法 总被引:8,自引:0,他引:8
从数字图像中检测出圆在计算机视觉中具有很重要的地位。随机Hough变换是检测圆的一种有效变换,但在处理复杂图像时,由于随机采样会引入大量的无效采样和积累。文章中提出一种在Teh-ChuanChenandKuo-LiangChung[4]的改进算法基础上,对随机Hough变换改进的检测圆的方法。 相似文献
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一种快速的随机Hough变换圆检测算法 总被引:4,自引:0,他引:4
随机Hough变换是检测圆的一种有效方法,但在处理复杂图像时随机采样带来的大量无效积累会导致计算量过大。提出一种快速的随机Hough变换圆检测算法,对证据积累的计算从三方面进行研究,有效地提高了计算速度,具有较好的应用价值。 相似文献
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广义Hough变换:多个圆的快速随机检测 总被引:17,自引:0,他引:17
以随机采样到的2个图像点及在此2点的中垂线上搜索第3个图像点来确定候选圆.当随机采样2个图像点时,通过剔除孤立、半连续噪声点减少了无效采样;当搜索候选圆的第3点时,剔除上述2种噪声点、非共圆点并给出快速确认候选圆是否为真圆的方法,尽可能减少无效计算.数值实验结果表明:文中算法能快速检测多个圆.在检测多个圆并且具有噪声的情况下,与随机圆检测算法相比,其检测速度快一个数量级. 相似文献
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针对多椭圆检测问题提出了一种快速随机检测算法。该算法利用在图像中随机采样到的一个边缘点和局部搜索到的两个边缘点以及这三个点的邻域信息确定候选椭圆,再将候选椭圆变换为对应圆,通过确认真圆来确认真椭圆。在确定候选椭圆时,最大限度地减少随机采样点数﹑剔除更多的非椭圆点,降低了无效采样,减少了无效计算。数值实验结果表明:该算法具有良好的鲁棒性,其检测速度比同类算法快。 相似文献
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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。 相似文献
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特征检测是图像处理和模式识别中非常重要的问题,其检测效果直接影响模式识别和分类。基于多尺度几何分析(MGA)的思想,提出了一种圆检测方法―圆特征域上奇异点算法。该算法首先将圆特征曲线变换到圆特征域上,然后在圆特征域上进行小波分析以找出奇异点,奇异点坐标即为待检圆的坐标。该方法克服了Hough变换对灰度图像圆检测需要考虑灰度阈值或梯度的限制,可直接对二值图像或灰度图像进行检测。最后分析、比较了该算法与Hough算法的不同。 相似文献
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针对传统Hough变换进行圆检测,计算量过大、检测同心圆精度不高、自动化程度低等缺点,提出一种基于连通区域标记算法的圆检测算法。该算法首先通过连通区域标记算法对图像进行处理得到一个圆,解决了传统Hough变换计算量过大的问题,再根据圆的特性确定其圆心及半径,从而避免了检测同心圆精度不高的问题。最后,分别取圆心的8邻域像素为圆心做圆,找到最优圆并将其与检测得出的圆进行比较来确定最终的圆,以达到自动化的目的。实验结果表明,提出的算法可以正确地检测出圆并具有很高的检测精度同时比Hough变换计算量小、自动化程度较高。 相似文献
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为了提高离焦模糊图像复原清晰度,提出一种基于频谱预处理与改进霍夫变换的 离焦模糊盲复原算法。首先改进模糊图像频谱预处理策略,降低了噪声对零点暗圆检测的影响。 然后改进霍夫变换圆检测算法,在降低算法复杂度的同时,增强了模糊半径估计的准确性。最 后利用混合特性正则化复原图像模型对模糊图像进行迭代复原,使复原图像的边缘细节更加清 晰。实验结果表明,提出的模糊半径估计方法较其他方法平均误差更小,改进的频谱预处理策 略更有利于零点暗圆检测,改进的霍夫变换圆检测算法模糊半径估计精度更高,所提算法对已 知相机失焦的小型无人机拍摄的离焦模糊图像具有更好的复原效果。针对离焦模糊图像复原, 通过理论分析和实验验证了改进的模糊半径估计方法的鲁棒性强,所提算法的复原效果较好。 相似文献
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Erik Cuevas Valentín Osuna-Enciso Fernando Wario Daniel Zaldívar Marco Pérez-Cisneros 《Expert systems with applications》2012,39(1):713-722
Hough transform (HT) has been the most common method for circle detection, exhibiting robustness but adversely demanding a considerable computational load and large storage. Alternative approaches for multiple circle detection include heuristic methods built over iterative optimization procedures which confine the search to only one circle per optimization cycle yielding longer execution times. On the other hand, artificial immune systems (AIS) mimic the behavior of the natural immune system for solving complex optimization problems. The clonal selection algorithm (CSA) is arguably the most widely employed AIS approach. It is an effective search method which optimizes its response according to the relationship between patterns to be identified, i.e. antigens (Ags) and their feasible solutions also known as antibodies (Abs). Although CSA converges to one global optimum, its incorporated CSA-Memory holds valuable information regarding other local minima which have emerged during the optimization process. Accordingly, the detection is considered as a multi-modal optimization problem which supports the detection of multiple circular shapes through only one optimization procedure. The algorithm uses a combination of three non-collinear edge points as parameters to determine circles candidates. A matching function determines if such circle candidates are actually present in the image. Guided by the values of such function, the set of encoded candidate circles are evolved through the CSA so the best candidate (global optimum) can fit into an actual circle within the edge map of the image. Once the optimization process has finished, the CSA-Memory is revisited in order to find other local optima representing potential circle candidates. The overall approach is a fast multiple-circle detector despite considering complicated conditions in the image. 相似文献
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Automatic circle detection on digital images with an adaptive bacterial foraging algorithm 总被引:2,自引:1,他引:1
Sambarta Dasgupta Swagatam Das Arijit Biswas Ajith Abraham 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2010,14(11):1151-1164
This article presents an algorithm for the automatic detection of circular shapes from complicated and noisy images without
using the conventional Hough transform methods. The proposed algorithm is based on a recently developed swarm intelligence
technique, known as the bacterial foraging optimization (BFO). A new objective function has been derived to measure the resemblance
of a candidate circle with an actual circle on the edge map of a given image based on the difference of their center locations
and radii lengths. Guided by the values of this objective function (smaller means better), a set of encoded candidate circles
are evolved using the BFO algorithm so that they can fit to the actual circles on the edge map of the image. The proposed
method is able to detect single or multiple circles from a digital image through one shot of optimization. Simulation results
over several synthetic as well as natural images with varying range of complexity validate the efficacy of the proposed technique
in terms of its final accuracy, speed, and robustness. 相似文献