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SIMD hypercube algorithm for complete Euclidean distance transform

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2 Author(s)
Chuang, H.Y.H. ; Dept. of Comput. Sci., Pittsburgh Univ., PA, USA ; Chen, L.

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)

Published in:

Algorithms and Architectures for Parallel Processing, 1995. ICAPP 95. IEEE First ICA/sup 3/PP., IEEE First International Conference on  (Volume:2 )

Date of Conference:

19-21 Apr 1995