Detection of ridges and ravines on range images and triangular meshes
Detection of ridges and ravines on range images and triangular meshes
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DOI：
10.1117/12.404815
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发表时间：
2000-10
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影响因子：
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通讯作者：
A. Belyaev;Y. Ohtake;K. Abe
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文献类型：
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作者：
A. Belyaev;Y. Ohtake;K. Abe
表面折痕、山脊和沟壑为我们提供了有关3D对象形状的重要信息，并且可以直观地定义为曲面上的曲线，曲面沿着该曲面急剧弯曲。我们对山脊和山沟的数学描述是基于对曲面法线的尖锐变化点的研究，或者相当于主曲率沿其曲率线的极值的研究。我们探索图像强度边缘(图像强度的尖锐变化点)与3D曲面的曲率极值之间的相似性。它允许我们采用一种基本的边缘检测技术来检测由多边形网格近似的距离图像和光滑表面上的山脊和沟壑。由于山脊和山沟具有高阶差分性质，因此需要进行仔细的平滑，以实现对感知上突出的山脊和山沟的稳定检测。为了检测深度图像上的山脊和沟壑，我们使用了一个作用于图像强度表面法线的非线性扩散过程。为了检测三角形网格上的山脊和沟壑，我们使用了网格法线和顶点的耦合非线性扩散。我们论证了山脊和山沟用于分割和形状识别的可行性。
Surface creases, ridges and ravines, provide us with important information about the shapes of 3D objects and can be intuitively defined as curves on a surface along which the surface bends sharply. Our mathematical description of the ridges and ravines is based on the study of sharp variation points of the surface normals or equivalently, extrema of the principal curvatures along their curvature lines. We explore similarity between image intensity edges (sharp variation points of an image intensity) and curvature extrema of a 3D surface. It allows us to adopt a basic edge detection technique for detection of the ridges and ravines on range images and smooth surfaces approximated by polygonal meshes. Because the ridges and ravines are of high-order differential nature, careful smoothing is required in order to achieve stable detection of perceptually salient ridges and ravines. To detect the ridges and ravines on a range image we use a nonlinear diffusion process acting on the image intensity surface normals. To detect the ridges and ravines on a triangular mesh we use a coupled nonlinear diffusion of mesh normals and vertices. We demonstrate feasibility of the ridges and ravines for segmentation and shape recognition purposes.