Levner Ilya, Zhang Hong
Department of Computing Science, University of AlberM, Edmonton, AB T6G 2E8, Canada.
IEEE Trans Image Process. 2007 May;16(5):1437-45. doi: 10.1109/tip.2007.894239.
This paper presents a novel approach for creation of topographical function and object markers used within watershed segmentation. Typically, marker-driven watershed segmentation extracts seeds indicating the presence of objects or background at specific image locations. The marker locations are then set to be regional minima within the topological surface (typically, the gradient of the original input image), and the watershed algorithm is applied. In contrast, our approach uses two classifiers, one trained to produce markers, the other trained to produce object boundaries. As a result of using machine-learned pixel classification, the proposed algorithm is directly applicable to both single channel and multichannel image data. Additionally, rather than flooding the gradient image, we use the inverted probability map produced by the second aforementioned classifier as input to the watershed algorithm. Experimental results demonstrate the superior performance of the classification-driven watershed segmentation algorithm for the tasks of 1) image-based granulometry and 2) remote sensing.
本文提出了一种用于创建分水岭分割中使用的地形函数和目标标记的新方法。通常，基于标记的分水岭分割提取表示特定图像位置处目标或背景存在的种子。然后将标记位置设置为拓扑表面（通常是原始输入图像的梯度）内的区域最小值，并应用分水岭算法。相比之下，我们的方法使用两个分类器，一个训练用于生成标记，另一个训练用于生成目标边界。由于使用了机器学习的像素分类，所提出的算法可直接应用于单通道和多通道图像数据。此外，我们不是对梯度图像进行泛洪，而是使用上述第二个分类器生成的反向概率图作为分水岭算法的输入。实验结果证明了分类驱动的分水岭分割算法在1）基于图像的粒度分析和2）遥感任务中的卓越性能。
