拓扑深层结构分割

S. Kalitzin, B. H. Romeny, M. Viergever
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引用次数: 14

摘要

对灰度图像进行线性尺度演化,得到层次分割模型。在每个尺度上,区段都以Voronoi图的形式生成,并在图像景观上定义距离度量。Voronoi细胞的中心集是灰度图像的局部极值集。利用梯度矢量场的圈数分布对该集合进行了局部化。尺度演化导致嵌入段的层次结构。在较粗的尺度上定义的对象在较细的尺度上“分解”成子对象。这一过程自然被描述为平滑尺度演化中的奇点突变。另外,我们提出了一个纯粹的拓扑分割程序,基于奇异的同种异构体。最后由图像中的鞍点集合生成,这些鞍点也用拓扑圈数法检测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On topological deep-structure segmentation
A hierarchical segmentation model is obtained by using linear scale evolution of gray-scale images. At each scale segments are generated as Voronoi diagrams with a distance measure defined on the image landscape. The set of centers of the Voronoi cells is the set of local extrema of the gray-scale image. This set is localized by using the winding number distribution of the gradient vector field. Scale evolution induces hierarchical structure of embedded segments. Objects defined at coarser scales "decompose" into sub-objects at finer scales. The process is naturally described in terms of singularity catastrophes within the smooth scale evolution. Alternatively, we present a purely topological segmentation procedure, based on singular isophotes. The last are generated by the set of saddle points in the image which are detected also with the topological winding-number method.
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