面向质量控制的三维曲线自适应演化

H. Martinsson, F. Gaspard, A. Bartoli, J. Lavest
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引用次数: 2

摘要

在视觉质量控制领域,三维曲线的重建是检测和量化可能的异常的方便工具。尽管存在其他允许我们描述表面元素的方法,但轮廓方法将被证明是有用的,可以重建接近不连续的物体,例如孔或边缘。提出了一种基于固定复杂度模型的三维参数曲线重构算法,该算法嵌入在控制点插入的迭代框架中。自由度的连续增加提供了良好的精度,同时避免了模型的过度参数化。通过在多视图设置中适应3D NURBS蛇的投影来重建曲线。曲线的采样作为不同视图中局部可见性的函数进行调整。曲线的优化使用基于梯度的能量最小化方法相对于控制点进行,而插入过程依赖于曲线到图像边缘距离的计算。
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Adaptive Evolution of 3D Curves for Quality Control
In the area of quality control by vision, the reconstruction of 3D curves is a convenient tool to detect and quantify possible anomalies. Whereas other methods exist that allow us to describe surface elements, the contour approach will prove to be useful to reconstruct the object close to discontinuities, such as holes or edges. We present an algorithm for the reconstruction of 3D parametric curves, based on a fixed complexity model, embedded in an iterative framework of control point insertion. The successive increase of degrees of freedom provides for a good precision while avoiding to over-parameterize the model. The curve is reconstructed by adapting the projections of a 3D NURBS snake to the observed curves in a multi-view setting. The sampling of the curve is adjusted as a function of the local visibility in the different views. The optimization of the curve is performed with respect to the control points using an gradient-based energy minimization method, whereas the insertion procedure relies on the computation of the distance from the curve to the image edges.
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