Watershed, Hierarchical Segmentation and Waterfall Algorithm
Watershed, Hierarchical Segmentation and Waterfall Algorithm
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DOI：
10.1007/978-94-011-1040-2_10
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发表时间：
1994
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影响因子：
--
通讯作者：
S. Beucher
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文献类型：
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作者：
S. Beucher
使用分水岭变换作为分割工具的一个主要缺点是图像的过度分割。过度分割是由嵌入图像或其梯度中的大量最小值产生的。设计了一种强大的技术，通过主要选择标记来指出图像中要分割的区域或对象，从而抑制过度分割。然而，只有在应用分水岭变换之前，我们能够计算出标记集，这种方法才能使用。但是，在许多情况下，特别是对于复杂的场景，这是不可能的，必须使用替代技术来减少过度分割。该技术是基于拼接图像，并计算从拼接图像中得到的值图的分水岭变换。这种方法导致了图像的分层分割，并大大减少了过度分割。然后，使用瀑布算法对分层分割进行重新定义。该算法允许选择最小值和与其邻域相比具有更高意义的集水区。本文还提出了一种利用测地线重构函数实现该算法的强大方法。最后，将此方法与M. Grimaud介绍的用于选择图像中重要极值的另一个强大工具进行比较：动力学。
A major drawback when using the watershed transformation as a segmentation tool comes from the over-segmentation of the image. Over-segmentation is produced by the great number of minima embedded in the image or in its gradient. A powerful technique has been designed to suppress over-segmentation by a primary selection of markers pointing out the regions or objects to be segmented in the image. However, this approach can be used only if we are able to compute the marker set before applying the watershed transformation. But, in many cases and especially for complex scenes, this is not possible and an alternative technique must be used to reduce the over-segmentation. This technique is based on mosaic images and on the computation of a watershed transform on a valued graph derived from the mosaic images. This approach leads to a hierarchical segmentation of the image and considerably reduces over-segmentation.Then, this hierarchical segmentation is redefined by means of a new algorithm called the waterfall algorithm. This algorithm allows the selection of minima and of catchment basins of higher significance compared to their neighborhood. A very powerful implementation of this algorithm using geodesic reconstruction of functions is also presented.Finally, this approach is compared to another powerful tool introduced by M. Grimaud for selecting significant extrema in an image: the dynamics.