Parallel Algorithms for Higher-Dimensional Euclidean Distance Transforms with Applications(Special Issue on Parallel and Distributed Computing, Applications and technologies)

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概要

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• Based on the dimensionality reduction technique and the solution for proximate points problem, we achieve the optimality of the three-dimensional Euclidean distance transform (3D_EDT) computation. For an N × N × N binary image, our algorithms for both 3D_EDT and its applications can be performed in O (log log N) time using (N^3)/(log log N) CRCW processors or in O (log N) time using (N^3)/(log N) EREW processors. To the best of our knowledge, all results described above are the best known. As for the n-dimensional Euclidean distance transform (nD_EDT) and its applications of a binary image of size N^n, all of them can be computed in O (n log log N) time using (N^n)/(log log N) CRCW processors or in O (n log N) time using (N^n)/(log N) EREW processors.

• 社団法人電子情報通信学会の論文
• 2003-09-01

著者

• Lee Yu-hua

  Department Of Electrical Engineering National Taiwan University Of Science And Technology

• Wang Yuh-rau

  Department Of Electrical Engineering National Taiwan University Of Science And Technology.:departmen

• HORNG Shi-Jinn

  Department of Electrical Engineering, National Taiwan University of Science and Technology

• LEE Pei-Zong

  Institute of Information Science, Academia Sinica

• Lee Pei-zong

  Institute Of Information Science Academia Sinica

• Horng Shi-jinn

  Department Of Electrical Engineering National Taiwan University Of Science And Technology

関連論文

• Parallel Algorithms for Higher-Dimensional Euclidean Distance Transforms with Applications(Special Issue on Parallel and Distributed Computing, Applications and technologies)
• Generalized Mesh-Connected Computers with Hyperbus Broadcasting for a Computer Network (Special Issue on Architectures, Algorithms and Networks for Massively parallel Computing)

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