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×n image, the algorithm has a time complexity of O(n) on an n² 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 t² nodes, the time complexity of the modified algorithm is O(n²/t log n/t)