论文标题
大坝爆发：一种基于区域合并的图像分割方法
Dam Burst: A region-merging-based image segmentation method
论文作者
论文摘要
到目前为止，除基于CNN的所有单级分割算法都导致过度分割。基于CNN的细分算法有自己的问题。为了避免过度分割，在区域合并过程中采用了多个标准阈值以产生层次分割结果。但是，在层次结构的低级别中，仍然存在极端的细分，并且在高级层次结构的高层次中将出色的微小对象合并到它们的大邻接中。本文提出了一种基于区域合并的图像分割方法，我们称其为大坝爆发。作为单级分割算法，此方法避免了分割并同时保留细节。它的命名是因为它模拟了地下的洪水摧毁了水池之间的大坝。如果将边缘检测结果视为大坝上的大坝结构。为了模拟地下的洪水，通过区域内的平均gra-dient的上升顺序合并了区域。
Until now, all single level segmentation algorithms except CNN-based ones lead to over segmentation. And CNN-based segmentation algorithms have their own problems. To avoid over segmentation, multiple thresholds of criteria are adopted in region merging process to produce hierarchical segmentation results. However, there still has extreme over segmentation in the low level of the hierarchy, and outstanding tiny objects are merged to their large adjacencies in the high level of the hierarchy. This paper proposes a region-merging-based image segmentation method that we call it Dam Burst. As a single level segmentation algorithm, this method avoids over segmentation and retains details by the same time. It is named because of that it simulates a flooding from underground destroys dams between water-pools. We treat edge detection results as strengthening structure of a dam if it is on the dam. To simulate a flooding from underground, regions are merged by ascending order of the average gra-dient inside the region.
