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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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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。 相似文献
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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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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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随机Hough变换是一种检测圆的有效方法.为了进一步提升随机Hough变换圆检测算法的执行速度和抗噪声能力,提出一种基于有效继承的随机Hough变换圆检测累计加速算法.该算法在每次成功检测圆后不清空参数空间的累计值,继承了上次的有效采样,对没有通过验证的参数单元设定负累计值;通过数理统计分析,采用伯努利试验模型解释了加速原理,得出该算法可以减少总采样次数并节省清空参数空间所需时间的结论.实验结果表明,加速原理的理论分析是正确的,文中算法的加速效果是显著的,且具备更强的抗噪声能力. 相似文献
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角膜地形图仪作为角膜检测的重要仪器,采集和分析人眼的Placido图像,检测图像亮环中心线的坐标信息,绘制反映角膜表面形状的角膜地形图.对角膜地形图仪的图像处理技术进行了深入研究,提出一种基于高斯核函数的Placido图像处理方法,用离散的高斯卷积核描述连续高斯核函数,用泰勒多项式描述灰度曲线表达式,建立亮环检测的判别表达式,该方法获得了精确的亚像素点坐标信息.利用标准球验证算法精度,屈光度平均误差小于0.25D,满足人眼角膜检测的要求. 相似文献
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利用Harris特征点并结合图像的归一化原理提出了一种新的数字水印方案。由于Harris算子的算法简单,稳健性较好,图像的特征点在经过几何攻击后仍然可以保持。而且,归一化的图像对图像的旋转不太敏感,所以首先对每一个以特征点为圆心的互不重叠的圆归一化以确定水印的嵌入点,然后把水印嵌入到原来的图像中,这样,可以很好地解决水印嵌入和检测的同步问题。实验证明,该算法能很好地抵抗如旋转,缩放,剪切等形式的几何攻击与常规信号处理攻击。 相似文献
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Alleviating the computational load of the probabilistic algorithms for circles detection using the connectivity represented by graph 总被引:1,自引:0,他引:1
The probabilistic algorithms are effective and widely used to recognize the curves in machine vision and image processing.
In this paper, a novel algorithm for detecting circles is presented. It is based on the observation that the connectivity
can help to alleviate the computational load of the probabilistic algorithm. A graph model is introduced to express connectivity
in the detected edges, and a modified depth-first-search algorithm is developed to segment the whole graph into connected
subgraphs and then partition the complex subgraph into simple paths. Then, four pixels are randomly selected from the sampling
set, consisting of one proper path or several consecutive paths, to detect circles. The connectivity constraint is further
employed to verify the candidates of circles to eliminate the pseudo ones. The experiments, comparing the proposed algorithm
with the randomized Hough transform and the efficient randomized circle detection algorithm, show that it has the advantages
of computational efficiency and robustness. 相似文献
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Erik Cuevas Felipe Sención-Echauri Daniel Zaldivar Marco Pérez-Cisneros 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2012,16(2):281-296
Hough transform has been the most common method for circle detection, exhibiting robustness, but adversely demanding considerable
computational effort and large memory requirements. Alternative approaches include heuristic methods that employ iterative
optimization procedures for detecting multiple circles. Since only one circle can be marked at each optimization cycle, multiple
executions ought to be enforced in order to achieve multi-detection. This paper presents an algorithm for automatic detection
of multiple circular shapes that considers the overall process as a multi-modal optimization problem. The approach is based
on the artificial bee colony (ABC) algorithm, a swarm optimization algorithm inspired by the intelligent foraging behavior
of honeybees. Unlike the original ABC algorithm, the proposed approach presents the addition of a memory for discarded solutions.
Such memory allows holding important information regarding other local optima, which might have emerged during the optimization
process. The detector uses a combination of three non-collinear edge points as parameters to determine circle candidates.
A matching function (nectar-amount) determines if such circle candidates (bee-food sources) are actually present in the image.
Guided by the values of such matching functions, the set of encoded candidate circles are evolved through the ABC algorithm
so that the best candidate (global optimum) can be fitted into an actual circle within the edge-only image. Then, an analysis
of the incorporated memory is executed in order to identify potential local optima, i.e., other circles. The proposed method
is able to detect single or multiple circles from a digital image through only one optimization pass. Simulation results over
several synthetic and natural images, with a varying range of complexity, validate the efficiency of the proposed technique
regarding its accuracy, speed, and robustness. 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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The problem of detecting precise pupil border in eye image given its initial circular approximation is addressed with circular shortest path method. Brightness gradient direction is employed to choose image pixels, which may belong to pupil boundary. Using initial approximate circles allows the method to work in a narrow ring, which contains only single pupil contour. Under these conditions the method allows to correctly handle almost all images used for iris recognition tasks and appears to be more precise than human expert in marking the pupil border. The method was tested with public domain iris databases, containing more than 80000 images totally. Experiments show that refinement of pupil border increases precision of iris recognition. 相似文献
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线性特征是图像的一种重要局部特征,它常常决定图像中目标的形状。线性特征的提取在图像匹配、目标描述与识别以及运动估计、目标跟踪等领域具有十分重要的意义。常用的线性特征检测方法有Radon变换和Hough变换,但检测曲线复杂度会很高。本文提出一种多尺度几何分析的线性特征检测方法,该方法以finite ridgelet理论为基础,结合正交小波变换对线性特征进行提取。Finite ridgelet变换对于含有直线奇异的多变量函数具有良好的逼近特性,能够获得连续空间函数的稀疏表达,同时具有区域平滑性、很好的可逆性和去冗余性。实验结果表明,本方法即使在背景复杂的环境下也具有良好的检测效果。 相似文献
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根据B样条多分辨率表示的特点提出了一种盲数字水印方法,把原图像作二次小波分解,在低频子图像上用Harris特征点检测算法选取特征点,在以特征点为圆心的同心圆上选点的灰度值作为控制顶点建立B样条曲线,对B样条曲线作多分辨率分解,在低分辨率系数中嵌入水印。由于小波分解后选特征点以及Harris特征点具有几何变换不变性,实验表明该方法对JPEG压缩、缩放、旋转、平移、剪切、滤波等具有较强的抵抗力。 相似文献