Efficient detection of ellipses from an image by a guided modified RANSAC
Efficient detection of ellipses from an image by a guided modified RANSAC
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DOI：
10.1117/12.805891
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发表时间：
2009-02
期刊：
影响因子：
8
通讯作者：
Yingdi Xie;J. Ohya
中科院分区：
文献类型：
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作者：
Yingdi Xie;J. Ohya
在本文中，我们提出了一种基于改进的 RANSAC 的新型椭圆检测方法，具有边缘方向差曲线的自动采样引导。霍夫变换家族是最流行的形状检测方法之一，但如果参数空间的维数变高，标准霍夫变换就会失去计算效率。随机霍夫变换是标准霍夫变换的改进版本，由于其随机采样过程，很难从复杂、杂乱的场景中检测形状。作为随机选择五个像素用于构建椭圆方程的预处理，我们提出了一种两步算法：（1）通过均值平移算法进行区域分割和轮廓检测（2）基于从每个区域的轮廓获得的边缘方向差异曲线进行轮廓分割。在步骤(2)得到的每个轮廓段中，随机选择5个像素，对这5个像素应用改进的RANSAC，从而得到精确的椭圆模型。实验结果表明，该方法在检测图像中的多个椭圆时可以实现较高的准确度和较低的计算成本。
In this paper, we propose a novel ellipse detection method which is based on a modified RANSAC, with automatic sampling guidance from the edge orientation difference curve. Hough Transform family is one of the most popular and methods for shape detection, but the Standard Hough Transform loses its computation efficiency if the dimension of the parameter space gets high. Randomized Hough Transform, an improved version of Standard Hough Transform has difficulty in detecting shapes from complicated, cluttered scenes because of its random sampling process. As a pre-process for random selection of five pixels to be used to build the ellipse's equation, we propose a two-step algorithm: (1) region segmentation and contour detection by mean shift algorithm (2) contour splitting based on the edge orientation difference curve obtained from the contour of each region. In each contour segment obtained by step (2), 5 pixels are randomly selected and the modified RANSAC is applied to the 5 pixels so that an accurate ellipse model is obtained. Experimental result show that the proposed method can achieve high accuracies and low computation cost in detecting multiple ellipses from an image.