分水岭分割对图像特征使用梯度下降法和沿区域边界分析弱点(weak points) 来将像素分

类为区域。想像在一个有水流动的拓扑地形结构中，水在重力的引导下聚集到一个地势较低

的盆地。随着水量的增加，水将流满整个盆地直到水流溢出到另一个盆地，这样就会将一些

小盆地吞没形成大的盆地。使用局部的几何结构来形成区域(集水的盆地)，在图像领域中正

如使用一些诸如曲率或梯度强度等特征中的局部极值来将像素连接成区域。这种技术不像其

他区域分割，它几乎不需要用户定义门限，尤其适合对以不同的特征类型从不同的数据集融

合而来的图像进行分割。分水岭技术在那些不仅仅产生单一图像分割的应用中也更加灵活，

但是使用一个门限，或者交互式地在一个用户绘图界面的帮助下，从一个可以提取一个单一

区域或区域集的a-priori 中的一个分割层级更合适。-Watershed segmentation classifies pixels into regions using gradient descent on image features

and analysis of weak points along region boundaries. Imagine water raining onto a landscape

topology and flowing with gravity to collect in low basins. The size of those basins will grow

with increasing amounts of precipitation until they spill into one another, causing small basins

to merge together into larger basins. Regions (catchment basins) are formed by using local

geometric structure to associate points in the image domainwith local extrema in some fea-ture measurement such as curvature or gradient magnitude. This technique is less sensitive

to user-defined thresholds than classic region-growing methods, and may be better suited for

fusing different types of features from different data sets. The watersheds technique is also

more flexible in that it does not produce a single image segmentation, but rather a hierarchy of

segmentations from which a single region or set of regio