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Extracting 3D Parametric Curves from 2D Images of Helical Objects

机译:从螺旋对象的2D图像中提取3D参数曲线

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Helical objects occur in medicine, biology, cosmetics, nanotechnology, and engineering. Extracting a 3D parametric curve from a 2D image of a helical object has many practical applications, in particular being able to extract metrics such as tortuosity, frequency, and pitch. We present a method that is able to straighten the image object and derive a robust 3D helical curve from peaks in the object boundary. The algorithm has a small number of stable parameters that require little tuning, and the curve is validated against both synthetic and real-world data. The results show that the extracted 3D curve comes within close Hausdorff distance to the ground truth, and has near identical tortuosity for helical objects with a circular profile. Parameter insensitivity and robustness against high levels of image noise are demonstrated thoroughly and quantitatively.
机译:螺旋物体出现在医学,生物学,化妆品,纳米技术和工程领域。从螺旋物体的2D图像中提取3D参数曲线具有许多实际应用,尤其是能够提取诸如曲折度,频率和螺距的度量。我们提出了一种能够拉直图像对象并从对象边界中的峰值导出鲁棒的3D螺旋曲线的方法。该算法具有少量稳定参数,几乎不需要调整,并且针对合成数据和实际数据验证了曲线。结果表明,提取的3D曲线在距地面真相的Hausdorff距离之内,并且对于具有圆形轮廓的螺旋对象具有几乎相同的曲折度。全面,定量地证明了对高水平图像噪声不敏感的参数和鲁棒性。

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