特征弦约束随机Hough变换在椭圆检测中的应用
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长春理工大学光电工程学院长春130022

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TP391

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国家自然科学基金(61605016)、吉林省科技发展计划(20160520018JH)项目资助


Application of RHT based on character string constraint in ellipse detection
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School of Photoelectric Engineering, Changchun University of Science and Technology, Changchun 130022, China

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    摘要:

    为了提高复杂图像中椭圆/类椭圆的检测效率及精度,提出了一种基于特征弦约束的随机Hough变换(RHT)思想的椭圆检测改进方法,借助特征弦的几何约束及特征弦端点的法向约束,大幅度降低RHT的无效采样和累积次数。通过对边缘图像中像素点的有效分布进行分析,建立用于存储特征弦的端点信息的二维数组累加器,在边界点提取之前利用椭圆幂剔除虚假椭圆中心的干扰,不仅能够提高空间采样点的可靠性,同时降低无效采样点的累积概率。实验表明,该算法具有更高的运行速度和检测精度,同时对于形变较大,轮廓缺失严重及噪声具有较强的抵抗能力。

    Abstract:

    In order to improve the detection efficiency and accuracy of ellipse/similar ellipse in complex image, an improved ellipse detection method is proposed based on the randomized Hough transform (RHT) with character string constraint. The noneffective samplings and number of accumulations of RHT are greatly reduced with the help of the character string geometric constraint and the normal constraint at the endpoints of the character string. Through analyzing the effective distribution of the pixels in edge image, the twodimensional array accumulator for storing the character string endpoint information is established, then the ellipse power is used to eliminate the interference of the false ellipse centers before extracting the boundary points, which not only improves the reliability of space sampling points, but also decreases the accumulation probability of noneffective sampling points. Experiments indicate that the proposed algorithm possesses high operation speed and detection accuracy in ellipse detection, as well as has strong resistance to large deformation, severely contour missing and noise.

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李艳荻,徐熙平,钟岩.特征弦约束随机Hough变换在椭圆检测中的应用[J].仪器仪表学报,2017,38(1):50-56

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  • 在线发布日期: 2017-07-20
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