SIMD hypercube algorithm for complete Euclidean distance transform

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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