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An Efficient Parallel Algorithm for Graph-Based Image Segmentation

机译:基于图的图像分割的高效并行算法

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Automatically partitioning images into regions ('segmentation') is challenging in terms of quality and performance. We propose a Minimum Spanning Tree-based algorithm with a novel graph-cutting heuristic, the usefulness of which is demonstrated by promising results obtained on standard images. In contrast to data-parallel schemes that divide images into independently processed tiles, the algorithm is designed to allow parallelisation without truncating objects at tile boundaries. A fast parallel implementation for shared-memory machines is shown to significantly outperform existing algorithms. It utilises a new microarchitecture-aware single-pass sort algorithm that is likely to be of independent interest.
机译:在质量和性能方面,将图像自动划分为多个区域(“细分”)具有挑战性。我们提出了一种基于最小生成树的算法,该算法具有新颖的图割启发式算法,其有效性通过在标准图像上获得有希望的结果得到证明。与将图像分为独立处理的图块的数据并行方案相反,该算法设计为允许并行化而不会在图块边界处截断对象。共享内存计算机的快速并行实现显示出明显优于现有算法。它利用了一种可能具有独立利益的新的微体系结构感知单遍排序算法。

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