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Contribution of edges and regions to range image segmentation

机译:边缘和区域对范围图像分割的贡献

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Abstract: Most of the segmentation algorithms of range images are based upon either a region approach or an edge approach. While the region growing methods are poor in delimiting the region boundaries, the edges do not give information about the different surfaces and are not connected. But these two approaches can collaborate because they give complementary information about the scene. In the proposed method we first extract edges from the image. Two kinds of edges are considered: occlusion edges or signal discontinuity and roof edges or orientation discontinuities. The edges are completed by a concurrent step and roof edges closing method in order to form initial closed regions by connected components labeling. Then begins an iterative region correcting process. At each iteration we fit a least squares bivariate polynomial to every region. Then each boundary point is examined to see if it is better approximated by its region or by a neighboring region. But the regions are not allowed to overpass initial edge points considered as confident surface boundaries. This process converges after few iterations and produces a better correspondence between the shapes of the regions and the surfaces of the objects. Results are shown for real range images. !9
机译:摘要:大多数距离图像的分割算法都是基于区域方法或边缘方法。尽管区域生长方法在限定区域边界方面很差,但是边缘不提供有关不同表面的信息,并且没有连接。但是这两种方法可以协作,因为它们可以提供有关场景的补充信息。在提出的方法中,我们首先从图像中提取边缘。考虑了两种边缘:遮挡边缘或信号不连续性以及屋顶边缘或方向不连续性。通过并发步骤和屋顶边缘封闭方法完成边缘,以便通过连接的组件标记形成初始封闭区域。然后开始迭代区域校正过程。在每次迭代中,我们将最小二乘二元多项式拟合到每个区域。然后检查每个边界点,以查看其边界区域或邻近区域是否更好地近似了该边界点。但是不允许这些区域超过被认为是可靠表面边界的初始边缘点。此过程在几次迭代后收敛,并在区域的形状和对象的表面之间产生更好的对应关系。显示了真实范围图像的结果。 !9

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