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On topological deep-structure segmentation

机译:关于拓扑深层结构分割

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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.
机译:通过使用灰度图像的线性尺度演化获得分层分割模型。在每个比例尺上,将生成分段,作为Voronoi图,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。通过使用梯度矢量场的绕组数分布来定位该集合。规模演变诱发了嵌入式片段的层次结构。在较粗的比例下定义的对象会在较细的比例下“分解”为子对象。自然地,该过程是根据平滑尺度演变中的奇异灾难描述的。或者,我们提出一种基于奇异等位线的纯拓扑分割程序。最后一个是由图像中的一组鞍点生成的,这些鞍点也可以通过拓扑缠绕数方法进行检测。

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