GPU加速的分水岭算法实现图像层级分割，速度快且适合做语义分割预处理。
Parallel Watershed Partitioning: GPU-Based Hierarchical Image Segmentation
- 基于GPU并行计算，重复应用分水岭算法生成图像层级分区
- 800兆像素图像分割耗时不足1.4秒，2D/3D均表现优异
- 可替代超像素算法，提升语义分割训练速度且精度相当
许多图像处理应用依赖将图像划分为互不重叠的区域，其中像素具有‘相似性’。分水岭与瀑布变换是成熟的数学形态学像素聚类技术，适用于需一次性确定像素组或依赖邻接信息的现代应用。本文提出三种面向GPU的新并行分割算法，通过反复应用分水岭算法，生成形成输入图像层次结构的瀑布结果。所提分水岭算法在2D和3D场景下均达到有竞争力的执行效率，可将800兆像素图像在1.4秒内完成分割。此外，我们展示了这种完全确定性的图像分割可作为机器学习语义分割的预处理步骤，替代超像素算法，取得相当的精度并显著加快训练速度。
原文摘要 · Abstract (English)
Many image processing applications rely on partitioning an image into disjoint regions whose pixels are 'similar.' The watershed and waterfall transforms are established mathematical morphology pixel clustering techniques. They are both relevant to modern applications where groups of pixels are to be decided upon in one go, or where adjacency information is relevant. We introduce three new parallel partitioning algorithms for GPUs. By repeatedly applying watershed algorithms, we produce waterfall results which form a hierarchy of partition regions over an input image. Our watershed algorithms attain competitive execution times in both 2D and 3D, processing an 800 megavoxel image in less than 1.4 sec. We also show how to use this fully deterministic image partitioning as a pre-processing step to machine learning based semantic segmentation. This replaces the role of superpixel algorithms, and results in comparable accuracy and faster training times.
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