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Relaxation method for segmenting quadratic surfaces from depth and intensity images

机译:从深度和强度图像中分割二次曲面的松弛方法

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Abstract: Recovery of shape and structure of objects present in a scene from its image is a significant problem in vision. The purpose of this paper is to develop and demonstrate an iterative relaxation algorithm that combines (by a fusion process) surface interpolation using a nonuniformly sampled range image and the recovery of shape from shading of a uniformly sampled intensity image. The recovery of shape from shading requires uniform lighting, as well as uniform viewing directions across the scene which is difficult to achieve. The objective is to extract several piecewise planar surfaces whose orientation parameters are extracted iteratively. With a few exceptions, most range sensors provide nonuniformly sampled depth images. It is desirable to extract the intrinsic surfaces and resample the image over a uniformly spaced grid. Then, it is expected that the structure of each image is isomorphic to that of the other image. Once the structure based region/volume correspondence is established, it becomes possible to adapt the consistency constraint for each surface and the smoothness criterion at the boundaries between two surfaces, and to activate resegmentation (incremental) if necessary. !17
机译:摘要:从场景图像中恢复场景中对象的形状和结构是视觉中的重要问题。本文的目的是开发和演示一种迭代松弛算法,该算法结合(通过融合过程)使用非均匀采样的距离图像进行表面插值,以及从均匀采样的强度图像的阴影中恢复形状。从阴影中恢复形状需要均匀的照明,以及难以实现的整个场景的统一观察方向。目的是提取迭代地提取其定向参数的几个分段平面。除少数例外,大多数距离传感器都提供不均匀采样的深度图像。希望提取本征表面并在均匀间隔的网格上对图像进行重新采样。然后,期望每个图像的结构与另一个图像的同构。一旦建立了基于结构的区域/体积对应关系,就有可能在每个曲面之间调整每个曲面的一致性约束和两个曲面之间边界处的平滑度标准,并在必要时激活重新分段(增量)。 !17

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