SIMD hypercube algorithm for complete Euclidean distance transform

Henry Y. H. Chuang, Ling Chen
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引用次数: 4

Abstract

The Euclidean distance transform (EDT) converts a binary image into one where each pixel has a value equal to its Euclidean distance to the nearest foreground pixel. A parallel EDT algorithm on SIMD hypercube computer is presented here. For an n/spl times/n image, the algorithm has a time complexity of O(n) on an n/sup 2/ nodes machine. With modifications to minimize dependency among partitions, the algorithm can be adapted to compute large EDT problems on smaller hypercubes. On a hypercube of t/sup 2/ nodes, the time complexity of the modified algorithm is O(n/sup 2//t log n/t).<>
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SIMD超立方算法的完全欧氏距离变换
欧几里得距离变换(EDT)将二值图像转换为其中每个像素的值等于其到最近前景像素的欧几里得距离。提出了一种基于SIMD超立方体计算机的并行EDT算法。对于n/spl /n次图像,该算法在n/sup 2/节点机器上的时间复杂度为O(n)。通过修改以最小化分区之间的依赖性,该算法可以适用于在较小的超立方体上计算大型EDT问题。在t/sup 2/个节点的超立方体上,改进算法的时间复杂度为O(n/sup 2//t log n/t)
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