A Parallel Algorithm for Weighted Distance Transforms
IPPS '97 Proceedings of the 11th International Symposium on Parallel Processing
Parallel Computation of the Euclidean Distance Transform on a Three-Dimensional Image Array
IEEE Transactions on Parallel and Distributed Systems
Computing the Euclidean Distance Transform on a Linear Array of Processors
The Journal of Supercomputing
A parallel algorithm for medial axis transformation
ISPA'03 Proceedings of the 2003 international conference on Parallel and distributed processing and applications
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In this paper, based on the diagonal propagation approach, we first provide an O(N2) time sequential algorithm to compute the chessboard distance transform (CDT) of an N X N image, which is a DT using the chessboard distance metrics. Based on the proposed sequential algorithm, the CDT of a 2-D binary image array of size N X N can be computed in O (log N) time on the EREW PRAM model using O(N2/log N) processors, O(log N/log log N) time on the CRCW PRAM model using O(N2log log N/log N) processors and O(log N) time on the hypercube computer using O(N2) processors. Following the mapping as proposed by Y.H. Lee and S.J. Horng (1995), the algorithm for the MAT is also efficiently derived. The medial axis transform of a 2-D binary image array of size N X N can be computed in O(log N) time on the EREW PRAM model using O(N2log N) processors, O(log N/log log N) time on the CRCW PRAM model using O(N2log log N/log N) processors, and O(log N) time on the hypercube computer using O(N2) processors. Our algorithms are faster than the best previous results as proposed by J.F. Jenq and S. Sahni (1992).