  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.
